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... types for developing in vitro models for predicting organ- specific toxicity Immortalized cell lines such as the standard murine fibroblast cell line NIH/3T3 are commonly used in toxicity testing... human 18 induced pluripotent stem cell (hiPSC)-derived HPTC-like cells for the development of in vitro models. 19 1.4 Role of inflammation in drug-induced nephrotoxicity in humans In the development. .. developers 11 1.2 In vitro models for the prediction of drug-induced nephrotoxicity The interest in in vitro models has been growing strongly in recent years due to legislation changes in the EU (Registration,
DEVELOPMENT OF HUMAN IN VITRO MODELS FOR PREDICTING ORGAN-SPECIFIC TOXICITY LI YAO (B. Sci. (Hons.), NUS) A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY DEPARTMENT OF BIOLOGICAL SCIENCES NATIONAL UNIVERSITY OF SINGAPORE 2014 Table of Contents Acknowledgements ....................................................................................................................... 4 Summary ........................................................................................................................................ 5 List of Tables ................................................................................................................................. 7 List of Figures................................................................................................................................ 8 1. Introduction ............................................................................................................................... 9 1.1 Nephrotoxicity and drug-induced acute kidney injury (AKI)................................................... 9 1.2 In vitro models for the prediction of drug-induced nephrotoxicity ........................................ 12 1.3 Application of stem cell-derived HPTC-like cells .................................................................. 18 1.4 Role of inflammation in drug-induced nephrotoxicity in humans .......................................... 20 2. Hypotheses and Goals ............................................................................................................. 22 3. Materials and Methods ........................................................................................................... 24 3.1 Static culture of commercial primary cells and cell lines ....................................................... 24 3.2 Isolation of HPTCs from human kidney tissue samples ......................................................... 24 3.3 Differentiation of hESC and hiPSC into HPTC-like cells ...................................................... 25 3.4 Cell culture materials for evaluating substrate-specific cell performance .............................. 26 3.5 Test materials for assessing cell type-specific toxicity ........................................................... 27 3.6 Adhesion of test materials to the cell surface ......................................................................... 27 3.7 Cell viability assays ................................................................................................................ 28 3.8 Test compounds for validation of endpoints for in vitro nephrotoxicity ................................ 28 3.9 Drug treatment ........................................................................................................................ 30 3.10 Quantitative real-time polymerase chain reaction (qPCR) ................................................... 31 3.11 qPCR-based prediction of drug-induced nephrotoxicity ...................................................... 32 3.12 Enzyme-linked immunosorbent assay (ELISA) ................................................................... 34 3.13 Gene knockdown by RNA interference (RNAi)................................................................... 34 3.14 Immunostaining .................................................................................................................... 35 3.15 High content screening (HCS) .............................................................................................. 35 3.16 Immunoblotting..................................................................................................................... 36 3.17 Standard toxicity assays ........................................................................................................ 37 3.18 Statistics ................................................................................................................................ 37 4. Results ...................................................................................................................................... 38 4.1 Evaluation of culturing substrates suitable for in vitro toxicology with primary human endothelial and renal cells............................................................................................................. 38 4.2 Cell type-specific cytotoxicity of chemical compounds ......................................................... 45 4.2.1 Cell type-specific cytotoxicity of layered clays and MCF-26 ........................................................................45 4.2.2 Cell type-specific cytotoxicity of silver nanoparticles (Ag NPs) ...................................................................54 4.3 Identification and validation of endpoints suitable for in vitro prediction of drug-induced nephrotoxicity in humans .............................................................................................................. 57 4.3.1 Model design and identification of suitable endpoints for in vitro nephrotoxicology ...................................57 4.3.2 Validation of the predictive performance with 41 test compounds ................................................................64 4.3.3 Comparison of endpoints ...............................................................................................................................81 2 4.4 Application of stem cell-derived HPTC-like cells for the prediction of drug-induced nephrotoxicity in humans .............................................................................................................. 85 4.4.1 Predictive performance of hESC-derived HPTC-like cells ............................................................................85 4.4.2 Comparison to standard toxicity assays .........................................................................................................92 4.4.3 Prediction of the PT toxicity of blinded compounds......................................................................................97 4.4.4 Predictive performance of hiPSC-derived HPTC-like cells ......................................................................... 100 4.5 Molecular and cellular mechanism of drug-induced IL-6/IL-8 expression in renal PTCs ... 106 4.5.1 Puromycin-induced nuclear translocation of NF-B and IL-6/IL-8 expression .......................................... 106 4.5.2 Effects of p65 silencing on nuclear translocation of NF-B and IL-6/IL-8 expression ............................... 108 4.5.3 Effects of inhibition of nuclear translocation of NF-B .............................................................................. 114 5. Discussion............................................................................................................................... 119 5.1 Effects of substrate stiffness on primary human endothelial and renal cells ........................ 119 5.2 Cell type-specific responses to toxicants in cultured primary cells ...................................... 122 5.3 Validation of an in vitro method for the prediction of drug-induced nephrotoxicity ........... 126 5.4 Application of stem cell-derived HPTC-like cells ................................................................ 131 5.5 The role of the NF-B pathway in nephrotoxicant-induced up-regulation of IL-6/IL-8 ...... 135 6. Conclusions ............................................................................................................................ 138 7. Recommendations for future research................................................................................ 140 8. References .............................................................................................................................. 142 9. Appendices ............................................................................................................................. 162 Appendix i: List of abbreviations ............................................................................................... 162 Appendix ii: Supplementary data................................................................................................ 165 Appendix iii: List of publications ............................................................................................... 205 3 Acknowledgements I would like to thank the National University of Singapore (NUS) and Institute of Bioengineering and Nanotechnology (IBN, an institute under the Agency for Science, Technology and Research (A*STAR)) for giving me the opportunity to pursue my Ph.D. studies. In particular, I would like to express my utmost gratitude to my supervisors Associate Prof. He Yuehui (NUS) and Dr. Daniele Zink (IBN) for their support and guidance throughout the course of the study. I am deeply grateful for the precious learning experience that they have given me. I would like to thank all ex- and current members of Dr. Zink’s group for their support and helpful discussions. I thank all collaborators: Prof. Anantharaman Vathsala, Dr. Tiong Ho Yee, Dr. Thomas Thamboo and all staff of National University Health System Tissue Repository (NUHS-TR) for their support. I also greatly appreciate the contributions to the experimental work by all the Youth Research Program (YRP; IBN) attachment students under my supervision. Finally, I would like to thank the IBN directors Prof. Jackie Ying and Ms Noreena AbuBakar, for their constant encouragement and leadership. My Ph.D. course was fully sponsored by the Scientific Staff Development Award (SSDA), A*STAR. The work is funded by Biomedical Research Council (BMRC, A*STAR) and a grant from the Joint Council Office (JCO, A*STAR) Development Program. 4 Summary The human kidney is a major target organ for drug-induced toxicity. Various environmental toxins and marketed drugs can cause nephrotoxicity and increase the incidence of acute kidney injury (AKI), which can in turn amplify the long-term risk of chronic kidney diseases [1, 2]. There is currently a lack of reliable pre-clinical models for predicting nephrotoxicity [3]. Animal models are affected by interspecies variability. A major problem with respect to in vitro models is the identification of appropriate cell types and endpoints. Therefore, nephrotoxicity of drug candidates is typically only detected during late stages of drug development [4]. This is a major obstacle in the development of new drugs with reduced nephrotoxic effects and leads to high costs for the pharmaceutical industry. Goal of my thesis was to develop an in vitro model that predicts nephrotoxicity in humans with high accuracy. My work focused on the use of renal proximal tubular cells (PTCs), which are most susceptible to toxic effects of drugs and chemicals in the human kidney due to their roles in drug transport and metabolism [3]. Human primary PTCs (HPTCs) were used to overcome interspecies variability associated with animal cells and functional changes of standard immortalized cell lines. I first investigated different culturing substrates for human primary cells, and the results revealed unexpectedly that uncoated tissue culture polystyrene (TCPS) was most suitable for HPTCs [5]. It has also been demonstrated in the thesis that variable cellular responses towards the same toxicants were mainly affected by cell type-specific effects [6], highlighting the importance of using the most relevant cell type. The HPTC-based in vitro model for nephrotoxicology developed here employed drug-induced increases in mRNA expression levels of the pro-inflammatory interleukins (IL)-6 and IL-8 as 5 endpoint. Pro-inflammatory responses play an important role in the pathophysiology of AKI, including drug-induced AKI [7]. The HPTC-based in vitro model was validated with 41 wellcharacterized drugs and chemicals and the major performance metrics ranged between 0.76 and 0.85, indicating that 76% - 85% of predictions made with this model would be correct [8]. This work established the first in vitro model that predicts nephrotoxicity in humans with high accuracy. Stem cell-derived HPTC-like cells [9] were employed in a next step. Also the HPTClike cell-based in vitro model was validated with the same set of 41 compounds. The predictivity of this model was also high [10]. This work demonstrated the first successful application of stem cell-derived human renal cells. All results were compared to results obtained with renal standard cell lines and widely used endpoints, which were associated with poor predictivity. Further, I addressed the underlying mechanisms of drug-induced IL-6 and IL-8 up-regulation in PTCs. The results showed that this was dependent on the nuclear translocation of NF-B p65. These results give further insights into the important role of pro-inflammatory pathways in druginduced nephrotoxicity. 6 List of Tables Table 1: List of test compounds used for validation of predictive endpoints for in vitro nephrotoxicity ............................................................................................................................... 30 Table 2: List of primer pairs used for qPCR analysis of IL-6/IL-8 expression ............................ 32 Table 3: IC50 values and cell viability (%) at the maximal concentrations of kaolin, bentonite, montmorillonite and MCF-26 ....................................................................................................... 49 Table 4: IC50 values and cell viability (%) at the maximal concentrations of Ag nanoparticles and DMSO .................................................................................................................................... 55 Table 5: Highest expression levels of IL-6 and IL-8 in HK-2 and LLC-PK1 cells ...................... 67 Table 6: Highest expression levels of IL-6 and IL-8 in HPTC ..................................................... 69 Table 7: Example for the thresholding procedure at threshold = 2.0............................................ 72 Table 8: Example for the thresholding procedure at threshold = 3.5............................................ 73 Table 9: Determination of true positives (TP), true negatives (TN), sensitivity and specificity in HPTC, HK-2 and LLC-PK1 cells ................................................................................................. 75 Table 10: Area under the curve (AUC) values of receiver operating characteristic (ROC) curves for HPTC, HK-2 and HPTC.......................................................................................................... 79 Table 11: Performance metrics of the IL-6/IL-8 endpoints in HPTC, HK-2 and HPTC.............. 80 Table 12: Comparison of drug effects on IL-6/IL-8 expression and cell numbers ....................... 82 Table 13: Highest expression levels of IL-6 and IL-8 in hESC-derived HPTC-like cells ........... 85 Table 14: Determination of TP, TN, sensitivity and specificity in hESC-derived HPTC-like cells ....................................................................................................................................................... 88 Table 15: Summary of results obtained with different endpoints and cell types .......................... 91 Table 16: Comparison of different assays performed with HPTC-like cells and HPTCs ............ 94 Table 17: Results obtained with three blinded compounds and prediction of PT toxicity ........... 98 Table 18: Highest expression levels of IL-6 and IL-8 in hiPSC-derived HPTC-like cells......... 101 Table 19: Determination of TP, TN, sensitivity and specificity in hiPSC-derived HPTC-like cells ..................................................................................................................................................... 102 7 List of Figures Figure 1: Standard terms and definitions used in statistical analysis ...................................... 33 Figure 2: Detection of F-actin, CD31 and CD146 in HUVECs ............................................... 40 Figure 3: Immunostaining of ZO-1 and vWF in HUVECs cultivated on TCPS .................... 41 Figure 4: Cell performance on PE and PLA films and membranes ......................................... 42 Figure 5: Relationship between absorbance (MTS assay) cell numbers ................................. 47 Figure 6: Dose-dependent effects of kaolin on cell viability ................................................... 48 Figure 7: Dose-dependent curves for layered clays and MCF-26 in different cell types ...... 50 Figure 8: Adhesion of layered clays and MCF-26 to the cell surface ..................................... 53 Figure 9: Relative marker gene expression in response to nephrotoxicants ........................... 60 Figure 10: Marker gene expression in response to nephrotoxicants (percentage of GAPDH expression) ................................................................................................................................... 62 Figure 11: Protein concentrations of IL-6 and IL-8 in cell culture supernatants ................... 63 Figure 12: Dose-response curves for expression of IL-6 and IL-8 .......................................... 65 Figure 13: Sensitivity, specificity and overall concordance with clinical data in three batches of HPTCs ........................................................................................................................................... 76 Figure 14: ROC curves for HPTCs, HK-2 and LLC-PK1 cells ............................................... 78 Figure 15: Sensitivity, specificity, overall concordance and ROC curves for hESC-derived HPTC-like cells ........................................................................................................................... 90 Figure 16: Dose-dependent curves for ATP depletion assay and GSH depletion assay in HPTC and HPTC-like cells ......................................................................................................... 93 Figure 17: Sensitivity, specificity, overall concordance and ROC curves for hiPSC-derived HPTC-like cells ......................................................................................................................... 103 Figure 18: Immunostaining of NF-B p65 in HK-2 cells and HPTCs ....................................... 107 Figure 19: Detection of NF-B p65 in by immunoblotting in protein lysates of HK-2 cells and HPTCs ......................................................................................................................................... 109 Figure 20: Immunostaining of NF-B p65 in HK-2 cells after siRNA transfection .................. 111 Figure 21: Marker gene expression levels determined by qPCR in HK-2 cells and HPTCs after RNAi ........................................................................................................................................... 113 Figure 22: Percentage of cells classified as positive for nuclear translocation of NF-B p65 ... 115 Figure 23: IL-6 and IL-8 gene expression levels after NF-B inhibition ................................... 117 8 1. Introduction 1.1 Nephrotoxicity and drug-induced acute kidney injury (AKI) The kidney is a major target organ for drug-induced toxicity apart from the liver. Due to its essential role in the excretion process, it receives 25% of resting cardiac output and is therefore frequently exposed to large amounts of drugs and chemicals in blood circulation [11]. Various agents, such as heavy metals, fungal toxins or other environmental toxins, and a large number of drugs have been shown to be nephrotoxicants [1, 2]. The use of such nephrotoxic chemicals and drugs can lead to acute kidney injury (AKI). AKI is a complex disorder and usually characterized by functional and/or structural injury of the kidneys, leading to an acute decline in their functions [12]. In the United States, an estimated 1% of all hospital admissions are affected by AKI, which also develops in 5% to 7% of hospitalized patients [13, 14]. This incidence rate increases drastically to 30% to 60% in the intensive care unit (ICU) patients [15]. Among all hospital- and community-acquired AKI cases, about 20% are attributed to drug- or toxicant-induced nephrotoxicity [2, 16]. Several mechanisms are involved in the toxicity of nephrotoxicants, including vasoconstriction, altered intraglomerular hemodynamics, tubular cell toxicity, interstitial nephritis, crystal deposition, thrombotic microangiopathy, osmotic nephrosis and rhabdomyolysis [1, 2, 17]. Among these mechanisms, direct tubular damage is the most common cause of AKI. The renal proximal tubular epithelial cells (PTCs) are a major target for the toxic effects of nephrotoxicants, due to their primary functions of glomerular filtrate concentration as well as drug transport and metabolism [18, 19]. Several drugs with known evidence of PTC toxicity are widely used to treat 9 various conditions such as cancer, sepsis, or used as immunosuppressants after transplantation, as briefly discussed below. One such example is cisplatin (also known as cis-dichlorodiammine platinum (II)), one of the most widely used chemotherapeutic agents in the treatment of cancers. Cisplatin is taken up by PTC through the organic cation transporter 2 (OCT 2) and the copper transporter Ctr1 [20]. It damages the nuclear and mitochondrial DNA inside the cells and results in apoptosis or necrosis [20, 21]. Diuresis and dose reduction are partially successful in lowering the nephrotoxicity of cisplatin [22, 23], but limits the anticancer efficacy of the drug. Despite these renoprotective techniques, the incidence of cisplatin-induced AKI remains high in cancer patients [24]. Therefore there is an urgent need to identify new substitutes with similar antitumor potency and less nephrotoxicity. Aminoglycoside antibiotics, such as gentamicin, tobramycin and amikacin, are effective drugs in clinical use to treat sepsis, a systemic response to infections. Aminoglycosides are taken up by the PTC via the megalin (MEG)/cubilin endocytotic receptor complex [25, 26]. Cellular accumulation of aminoglycosides can lead to disruption of protein turnover as well as mitochondrial dysfunction, which often results in PTC death and AKI [27]. It is also well established that AKI can amplify long-term risk of chronic kidney disease (CKD) and end stage renal disease (ESRD) [28-30], further increasing morbidity and mortality. For ESRD patients, the most effective treatment is kidney transplantation. Calcineurin inhibitors, such as cyclosporine and tacrolimus, are often used in immunosuppressive regimens to prevent 10 allograft rejection due to their clinical effectiveness. However, these drugs are also commonly associated with nephrotoxicity, which may lead to acute changes in renal heamodynamics [31] and apoptosis-inducing direct tubular epithelial cell toxicity [32, 33]. Calcineurin inhibitors are also known to cause chronic nephrotoxic effects such as striped interstitial fibrosis and arteriolar hyalinosis, leading to chronic allograft dysfunction [34-37]. In fact, nephrotoxicity is the major pitfall of the current calcineurin inhibitor-based immunosuppressive regimens in transplant recipients, and this problem greatly limits the effectiveness of such treatments. Due to such adverse side-effects of existing drugs, it is essential to develop new drugs with similar efficacy but less nephrotoxicity. However, nephrotoxicity is typically detected only during late stages of drug development. 2% of drug attrition during pre-clinical studies is due to nephrotoxicity, and in phase 3 clinical trials this percentage increases drastically to 19% [4]. The major reason for this is the low predictivity of animal tests, usually due to interspecies variability. Therefore, in vitro models based on human cells are recently gaining more interests among drug developers. 11 1.2 In vitro models for the prediction of drug-induced nephrotoxicity The interest in in vitro models has been growing strongly in recent years due to legislation changes in the EU (Registration, Evaluation, Authorization and Restriction of Chemicals (REACH) and Cosmetics Directive) and new initiatives in the USA (ToxCast and Tox 21). However, there are currently no regulatory approved in vitro models for the prediction of nephrotoxicity. The European Centre for the Validation of Alternative Test Methods (ECVAM) funded a prevalidation project that was based on the use of two animal cell lines and 15 compounds, and the endpoints used were transepithelial electrical resistance (TEER) and fluorescein isothiocyanate (FITC) influx [38]. This study was published more than a decade ago and there was no follow-up on this approach. No further validation study specific to in vitro nephrotoxicology was performed since then. There have been other studies which attempted to develop or validate models for in vitro nephrotoxicology [3, 39-44]. However, one major problem in these studies was that only very limited numbers of test compounds were used, and it was not possible to determine the predictivity of the models. In a recent study on organ-specific toxicity, 621 compounds (including 273 hepatotoxicants, 191 cardiotoxicants, 85 nephrotoxicants, and 72 non-toxic compounds) were used on multiple organ-specific cell lines (human hepatoma cell line, rat myocardial cell line and rat kidney epithelial cell line) [45]. Adenosine triphosphate (ATP) content was used as the endpoint, and major performance metrics such as sensitivity and specificity were calculated. However, the results of this study showed that the model could not achieve accurate prediction of organ-specific toxicity. This implies that the choice of appropriate 12 cell types and endpoints could have an essential impact on the performance of an in vitro model for the prediction of organ-specific toxicity. ADME (absorption, distribution, metabolism and excretion) properties can also affect how different cell types respond to drugs in vivo, but drug attrition due to ADME properties has substantially declined during recent years [46, 47], due to pharmacokinetics modeling and better absorption models (for example, the Caco-2 cell line as a model for intestinal epithelial permeability [48]). On the contrary, toxicity prediction could not be improved and still remains as the major reason for drug attrition [46, 47]. Nevertheless, bioavailability and biodistribution principles have limited relevance to in vitro toxicity models, where the choice of cell types and endpoints would have a greater impact on predictive performance of such models. For an in vitro nephrotoxicity model, PTCs are the most appropriate cell type as they are the major target for toxic effects of drugs and chemicals in the kidney. Their vulnerability to toxicants is due to their roles in glomerular filtrate concentration and the transport of drugs and organic compounds [1, 49]. PTCs actively transport a large variety of drugs, organic compounds and xenobiotics from blood circulation to the glomerular filtrate and also metabolise such compounds. A wide spectrum of drug transporters as well as drug metabolizing enzymes is expressed in PTCs [3, 18, 19]. These expression patterns are essential in regulating the cellular responses towards the toxic effects of drugs and chemical compounds. This is well demonstrated by the differences in drug response between cells from different organ systems [50, 51], as well as between animal and human PTCs. For example, human PTCs express only one multidrug resistance (MDR 1) gene which encodes the P-glycoprotein transporter (which participates, for 13 instance, in the removal of paraquat from PTCs [52]), but mice have MDR 1a and MDR 1b [53, 54]. Another example would be OCT1, which plays an important role in cationic drug secretion in the rodent kidney, but is expressed at extremely low levels in the human kidney [55]. Flavincontaining monooxygenases also show marked interspecies differences in their expression patterns in the kidney [18]. Such interspecies variability explains why high predictivity with respect to human responses is difficult to achieve with preclinical animal models. Therefore it remains challenging to accurately detect drug-induced nephrotoxicity in early stages of drug development [4]. With respect to human cells, the human kidney-2 (HK-2) cell line is commonly used for nephrotoxicity testing (for example, see [56, 57]). However, one major problem with the use of cell lines is the functional changes that had occurred in the cells during immortalization. HK-2 cells were derived from human PTC and immortalized with human papilloma virus-16 (HPV-16) E6/E7 genes [58]. Although these cells demonstrate functional features of PTC and express PTC markers, it has been shown that they lack the expression of certain drug transporters, such as organic anion transporter 1 (OAT 1), OAT 3, OCT 2, as well as breast cancer resistance protein (BCRP) [59]. The expression levels of MEG were also low in HK-2 cells [59], leading to reduced uptake of gentamicin [60]. As aminoglycoside antibiotics such as gentamicin are major nephrotoxicants in humans [61], the insensitivity towards such drugs [57] greatly undermines the usefulness of HK-2 cells in nephrotoxicology. Due to functional changes and changes in drug transporter expression associated with immortalization [59, 62, 63], HK-2 are in general less sensitive towards nephrotoxicants than human primary renal proximal tubular cells (HPTCs) [64], which would conceptually serve as a more appropriate cell type in nephrotoxicology studies. 14 Our group has been working extensively on characterization of HPTCs and in vitro cultivation conditions, and this work addressed culturing substrates [5, 65-67], coatings [67, 68], culture media and growth factors [69, 70]. The work also included development of bioreactors [66, 67, 71], co-culture/three-dimensional (3D) models [65, 70, 72] and genetic engineering [69]. Here my objective was to develop an in vitro model for the prediction of drug-induced nephrotoxicity based on HPTCs, and validate this model in a retrospective study with a large number of environmental toxicants and drugs with well-characterized effects on human kidneys. For primary cell-based in vitro models, it is crucial to select an appropriate culture substrate to support optimal performance of the cells, as different substrates interact differently with cells and in turn affect cell performance. As HPTCs usually grow on a basal lamina in vivo, it is generally believed that extracellular matrix (ECM) coatings could possibly improve the performance of HPTCs on synthetic substrates. Indeed, a previous study from our group showed that laminin and collagen IV ECMs could sustain differentiated monolayers of HPTCs in static cultures on multiwell plates [68]. Our other studies have also shown that a double coating with 3,4-dehydroxy-Lphenylalanine (DOPA) and collagen IV could improve HPTC performance in bioreactor units of bioartificial kidneys, where cells were cultured under dynamic conditions [66, 67]. To our surprise, our more recent data indicated that under static conditions, the stiffness of the underlying substrate seemed to play a dominant role in supporting primary cell performance [5]. However, the impact of material stiffness on the performance of cultured primary human soft tissue cells has not been systematically characterized before. Therefore it is important to 15 investigate how this factor can affect the performance of HPTCs and to identify a suitable material for the in vitro nephrotoxicology model. In addition to cell types and cell culture substrates, another important aspect of developing an in vitro nephrotoxicology model is to identify appropriate endpoints. For toxicity testing, endpoints that measure general cytotoxicity are frequently used. These include cell death, metabolic activity or ATP depletion. However, use of such endpoints for determining organ-specific toxicity does not give convincing results. For instance, in a recent study using liver-, kidney PTand heart-derived cell lines [45], ATP depletion was measured after treatment with organspecific toxicants. The majority of test compounds gave similar results with all three cell lines [45]. Many potential novel biomarkers for AKI, such as kidney injury molecule-1 (KIM-1) and neutrophil gelatinase-associated lipocalin (NGAL), showed up-regulation in PTCs in vivo after kidney injury or as a result of drug-induced nephrotoxicity [73-78]. However, the EU-funded Predict IV project reported that up-regulation of these biomarkers was greatly compromised in vitro, and no consistent results could be obtained with HPTC-based models or with a model based on a newly established PTC line1. Up-regulation of potential novel AKI biomarkers was also not observed in a recently developed 3D model [79]. In addition to potential novel AKI biomarkers, pro-inflammatory cytokines such as IL-6, IL-8 and IL-18 are also often up-regulated in injured or diseased kidneys [80-82]. In fact, pro-inflammatory cytokines play an important role in the pathophysiology of AKI [7], and they had also been suggested as potential biomarkers 1 Predict IV, third and fourth Annual Report. http://www.predict-iv.toxi.uni-wuerzburg.de/periodic_reports/ 16 for the detection of nephrotoxicant-induced AKI [83]. It is thus interesting to further evaluate these biomarkers in cultured PTCs treated with PT-specific nephrotoxicants to address their usefulness in the prediction of drug-induced nephrotoxicity. For screening of large numbers of new drug candidates, it is also important that the in vitro model is economically self-sustainable and compatible with industry-scale procedures. This would be, however, difficult to achieve with the use of solely human primary cells. Stem cellderived HPTC-like cells [9], which can be propagated in relatively large numbers inexpensively, offer a timely solution to this problem as well as to other limitations associated with primary cells. 17 1.3 Application of stem cell-derived HPTC-like cells Primary cells, such as HPTCs, are often associated with problems such as the limited cell source and proliferative capacity [63, 84], functional changes during passaging [85], inter-donor variability [8, 86] as well as de- and trans-differentiation in vitro [65, 87]. Stem cell-based methods would be highly interesting in view of these limitations. Very recently, various protocols have been developed for the differentiation of human or murine embryonic (ESCs) or induced pluripotent stem cells (iPSCs) into cells of the renal lineage [8893]. These protocols generally involve multiple steps of differentiation to recapitulate different stages in embryonic kidney development. Embryonic kidney precursor structures or [90, 93] or a spectrum of different renal cell types [91, 92] were typically obtained. Although useful for regenerative medicine, such heterogeneous cell populations are only of limited applicability in in vitro drug safety screening. In contrast, our group was previously involved in developing a one-step feeder-free protocol for the differentiation of human embryonic stem cells (hESCs) into HPTC-like cells [9]. The results revealed that hESC-derived HPTC-like cells displayed gene and protein expression patterns similar to HPTCs. They were also able to form polarized epithelial and tubular structures in vitro, and displayed functional characteristics of HPTC [9]. Such morphological and functional similarity to HPTCs suggests that the hESC-derived HPTC-like cells could be applied in in vitro models for drug testing. Nevertheless, it is important to address the ethical and legal controversies associated with the use of hESCs. A potential solution would be to use human 18 induced pluripotent stem cell (hiPSC)-derived HPTC-like cells for the development of in vitro models. 19 1.4 Role of inflammation in drug-induced nephrotoxicity in humans In the development of in vitro models for the prediction of drug-induced nephrotoxicity, it is important to understand the cellular pathways and molecular processes underlying the potential endpoints. In another study from our group, it was found that the nuclear factor kappa B (NF-B) often translocated into the cell nuclei of HPTCs when the cells were exposed to known nephrotoxicants (Xiong et al., unpublished results). NF-B induces or regulates the transcription of genes by binding to B elements in promoter and enhancer sequences of the target loci, which includes genes associated with inflammation [94-97]. On the other hand, as mentioned earlier, it is also established that inflammation plays an important role in the pathogenesis of AKI [7, 98]. It is therefore interesting to further investigate the relationship between drug-induced nuclear translocation of NF-B and the inflammatory responses of the renal proximal tubular cells. In mammals, 15 possible different homo- or heterodimers of NF-B can be formed from the different combinations of the five Rel family proteins: p50 (also known as NF-B1, a cleavage product of p105), p52 (also called NF-B2, a cleavage product of p100), p65 (also called RelA), RelB and c-Rel [99]. The Rel family proteins dimerize with each other via the shared Rel-homology domain, which also contains a nuclear translocation signal [99]. Among these subunits, p65 is the most interesting and relevant for my study. Firstly, the transcription activation domain can only be found in the p65, RelB and c-Rel subunits [94]. The p65/p50 dimer is the most abundant form of NF-B in cells and also the best 20 characterized [99]. Furthermore, p65 is DNA-binding subunit in the canonical pathway of NF-B activation as well as in the hybrid pathway (which also links to the non-canonical pathway) [99]. The canonical pathway is also a rapid response to a wide range of external stimuli [100], making it more relevant to the effects of acute nephrotoxicity. In this thesis, I developed and validated an in vitro model for the prediction of drug-induced nephrotoxicity using HPTCs cultured on polystyrene-based multiwall plates. The endpoints used were increased expression levels of the pro-inflammatory cytokines IL-6 and IL-8. The relationships between the up-regulation of these cytokines and the nuclear translocation of NF-B p65 in human proximal tubular cells were investigated. The results showed that high accuracy in the predictions of nephrotoxicity in humans could be achieved with HPTCs. Furthermore, limitations associated with the use of primary cells were addressed by using stem cell-derived HPTC-like cells. These were shown to be a viable alternative and high predictivity was also obtained by using this cell type. 21 2. Hypotheses and Goals Goals of my thesis were to: 1. Determine the most suitable culturing substrate for developing in vitro models for the prediction of toxicity. My work addressed the hypothesis that the stiffness of materials has a major impact on proliferation and differentiation of relevant human primary cell types. Other substrate features such as surface roughness and water contact angle have also been addressed in a larger study [5] in which my work focused on investigating the effects of substrate stiffness. In this study, substrate stiffness was also the only parameter which turned out to be correlated with cell performance [5]. I evaluated the performance of primary human umbilical vein endothelial cells (HUVECs) and HPTCs on synthetic substrates of different stiffness. HUVECs are subsequently used for testing toxicity of hemostatic agents (Section 4.2), whereas HPTCs are the major model cell type for prediction of drug-induced nephrotoxicity (Section 4.3). 2. Compare toxicity of mesocellular foam (MCF)-26 with commonly used layered claybased hemostatic agents and address cell type-specific responses towards toxicants in vitro. It is important to assess the toxicity of these materials in skin-related cell types such as epidermal keratinocytes, dermal fibroblasts and endothelial cells due to direct exposure to hemostatic materials at wound sites. In addition to such cell types HPTC were included for comparison. Cell type-specific responses were investigated by comparing the toxic effects of different hemostatic agents in various cell types. This work was done in collaboration with Professor Galen Stucky’s team (University of California, Santa Barbara (UCSB)). 22 3. Identify suitable endpoints (with the use of HPTCs) for an in vitro model that should predict human PT toxicity. The hypothesis was that pro-inflammatory markers, including cytokines IL-6 and IL-8, would be up-regulated in HPTCs in response to PT-specific nephrotoxicants. The endpoints were validated with 41 well-characterized drugs and environmental toxicants. Also, all results were compared with the data obtained with other PT-derived cell types. 4. Evaluate the usefulness of stem cell-derived HPTC-like cells as alternative to HPTCs for the prediction of human PT toxicity in vitro. A similar validation process with 41 wellcharacterized compounds was performed with hESC- and hiPSC-derived HPTC-like cells in order to examine the hypothesis that comparable predictivity could be achieved with these cell types due to their functional similarities to HPTCs. 5. Investigate the underlying mechanisms of nephrotoxicant-induced up-regulation of the pro-inflammatory cytokines IL-6 and IL-8. It was hypothesized that the observed changes in their expression levels were mediated by the NF-B pathway, based on findings obtained from another study by our group (Xiong et al., unpublished results). The connection between up-regulation of IL-6 and IL-8 and activation of the NF-B pathway was examined in my PhD thesis by RNA interference (RNAi) and inhibitor studies. 23 3. Materials and Methods 3.1 Static culture of commercial primary cells and cell lines Two batches of HPTCs (Lot.-Nr. 58488852, HPTC 1 and Lot.-Nr. 61247356, HPTC 5) were obtained from the American Type Culture Collection (ATCC, Manassas, VA, USA). They were cultivated as described [5, 66, 67, 72] and used at passage (P) 4 and P 5. Three batches of HUVECs (HUVEC 1-3; Lot.-Nr. 3516, 5025 and 5117, respectively) and three batches of primary adult human epidermal keratinocytes (HEK 1-3; Lot.-Nr. 6539, 6937 and 6940, respectively) were purchased from ScienCell Research Laboratories (Carlsbad, CA, USA). Multiple batches of primary adult human dermal fibroblasts (HDF) were purchased from ScienCell Research Laboratories, ATCC, and Promocell GmbH (Heidelberg, Germany). HK-2 [58], LLC-PK1 [101, 102] and NIH/3T3 cell lines were obtained from ATCC. All cell types were cryopreserved before use and were cultivated in their respective culture media recommended by the vendors (as described [72]). 3.2 Isolation of HPTCs from human kidney tissue samples Three batches of HPTCs (HPTC 2-4) were isolated from anonymized fresh normal human kidney tissues obtained from the Tissue Repository of the National University Health System (NUHS, Singapore). Nephrectomy samples were derived from patients with renal cancer. Associated normal tissue was identified by a pathologist before being used for HPTC isolation. The use of human kidney tissue samples was reviewed and approved by the Institutional Review Board (NUS-IRB reference code: 11-143). 24 All HPTC isolation procedures were performed under sterile conditions. Upon surgical removal, tissue samples were preserved and transported in ice-cold cell culture medium (Dulbecco’s Modified Eagle’s Medium (DMEM)/Ham’s F12 (Invitrogen, Carlsbad, CA, USA)) supplemented with transferrin (5 g/ml), insulin (5 g/ml), hydrocortisone (0.02 g/ml), epidermal growth factor (10 ng/ml), prostaglandin E1 (0.05 g/ml), selenium (3.95 g/ml), triiodothyronine (3.36 pg/ml) and penicillin/streptomycin (1%). All supplments were obtained from Sigma-Aldrich, Singapore. HPTC isolation procedures were performed as described in [103]. For overnight cell attachment (16 h; after isolation or passaging), fetal bovine serum (2%) has been added to the culture medium. Cells were subsequently cultivated in serum-free complete DMEM/Ham’s F12 medium. HPTC 2-4 were cryopreserved at P 2 or P 3 and subsequently used in experiments at P 3 and P 4. 3.3 Differentiation of hESC and hiPSC into HPTC-like cells HUES 7 cells (P 11) were obtained from the Harvard Stem Cell Institute (Harvard University, Cambridge, MA, USA). iPS(Foreskin)-4 cells were obtained from the WiCell Research Institute (Madison, WI, USA). Undifferentiated stem cells were cultivated in mTeSR1 medium (Stemcell Technologies, Singapore) in multi-well plates coated with growth factor-reduced Matrigel (BD, Franklin Lakes, NJ, USA). Differentiation into HPTC-like cells was performed as described in [9]. Briefly, stem cells were seeded into Matrigel-coated dishes and cultivated for 20 days in complete renal epithelial growth medium (REGM) supplemented with growth factors (REGM BulletKit, Lonza BioScience, Singapore). 0.5% fetal bovine serum, 10 ng/ml of bone morphogenetic protein (BMP)2 and 2.5 ng/ml of BMP7 (R&D Systems, Minneapolis, MN, USA) were also added to the medium for differentiation. Differentiated HUES 7-derived and 25 iPS(Foreskin)-4-derived HPTC-like cells were harvested after 20 days and cryopreserved before use. Stem cell-derived HPTC-like cells were subsequently cultivated in complete REGM (as described above). The differentiation procedures were performed by Dr. Wei Seong Toh and Dr. Karthikeyan Kandasamy (IBN, A*STAR, Singapore). The Institutional Review Board of the National University of Singapore approved work with hESC and hiPSC cells (NUS-IRB reference code: 13-437). 3.4 Cell culture materials for evaluating substrate-specific cell performance The following commercial cell culture materials were tested for cell performance: tissue culture polystyrene (TCPS, BD, Franklin Lakes, NJ, USA), Thermanox coverslips (TX) consisting of a polyester film with modified surface for optimal cell adherence (Nunc, Roskilde, Denmark), cover glass (CG, VWR Singapore, Singapore) and Cyclopore polycarbonate (PC-1) membranes (Whatman, Germany). Low density polyethylene (PE) films (cling ware) were purchased from The Glad Products Company (Oakland, CA, USA) and high density PE films (sandwich bags) were obtained from the National Trades Union Congress (NTUC) Fairprice Co-operative Ltd. (Singapore). Both PE products do not contain additives or plasticizers, and they were both treated with corona discharge using a high frequency generator (BD-10AV, Electro-Technic Products, Inc., Chicago, IL, USA). The surface treatment of these PE materials were performed by Dr. Ming Ni (IBN, A*STAR, Singapore). Hexafluoroisopropanol was used to dissolve poly(lactic acid) (PLA) at a concentration of 150 g/ml and this PLA solution was subsequently used to produce PLA films and electrospun 26 membranes. PLA films were produced by casting the PLA solution into aluminum cups, which were then left in a solvent-saturated environment for 72 h to allow slow evaporation of the solvent. Electrospun PLA membranes were generated by loading the PLA solution in a syringe fitted with a 26-gauge metal needle. The flow rate applied was 0.24 ml/h and a voltage between 10 and 15 kV was used across a pair of oppositely charged electrodes separated at a distance of 10 cm. The PLA materials were graciously prepared and provided by Dr. Meng Fatt Leong and Dr. Andrew Wan (IBN, A*STAR, Singapore). 3.5 Test materials for assessing cell type-specific toxicity Kaolin, bentonite, montmorillonite were purchased from Sigma-Aldrich (St. Louis, MO, USA) and graciously provided by Prof. Galen Stucky (Department of Chemistry and Biochemistry and Materials Department, University of California, Santa Barbara, USA). A mesocellular foam with a cell window size of 26 nm (MCF-26) was synthesized as described in [6] and provided by Prof. Galen Stucky’s team. Silver nanoparticles (Ag NPs; 10 nm) were obtained from Meliorum Technologies (Rochester, NY, USA). Dimethyl sulfoxide (DMSO) was purchased from SigmaAldrich. 3.6 Adhesion of test materials to the cell surface HUVEC were cultivated for 24 h on glass coverslips (Menzel-Gläser, Braunschweig, Germany) and subsequently exposed to test materials at 1 mg/ml for 10 minutes. Cells were then repeatedly washed with 10 ml of 1X PBS and fixed with 3.7% formaldehyde in PBS. Dark field imaging of 27 the cells was performed with CytoViva® high resolution imaging system (CytoViva, Auburn, AL, USA), which is compatible with visualizing small particles adhered onto cell surfaces. 3.7 Cell viability assays The neutral red uptake (NRU) assay was performed as described [104] and as recommended by the International Standard for the Biological Evaluation of Medical Devices (Part 5: Tests for In vitro Cytotoxicity, ISO 10993-5:2009 (E)), with the following modifications: different cell types were used and cells were seeded at a density of 50,000 cells/cm2 into 96-well microplates. Cells were cultivated for 24 h and were then treated overnight with test materials. Before the NRU assay was performed the cells were washed with phosphate-buffered saline (PBS). Data acquisition and analysis was performed as described [72]. The NRU assay was used for the generation of all data on cell viability shown in section 4.2. 3.8 Test compounds for validation of endpoints for in vitro nephrotoxicity 41 compounds were selected and tested with various HPTC batches. The detailed list of these compounds is shown in Table 1, where they were classified into 3 categories. All test compounds were purchased from Sigma-Aldrich (St. Louis, MO, USA) except the following: compounds 3-5, 8, 14, 18-20, 23, 30, 34 and 37 were purchased from Merck (Darmstadt, Germany), compound 1 from PAA Laboratories GmbH (Pasching, Austria), compound 10 from ChemService (West Chester, PA, USA) and compound 22 from Tocris Bioscience (Bristol, UK). Stock solutions (10 mg/ml) of compounds 1,2, 4-6, 9-18, 23, 25, 28, 30-36, and 40 were prepared with biotechnology grade water (1st Base, Singapore). Stock solutions of other compounds (6.8 mg/ml -100 mg/ml depending on the solubility of the individual compound) were prepared with 28 dimethyl sulfoxide (DMSO; Sigma-Aldrich; compounds 3, 7, 8, 19, 22, 27 and 41) or ethanol (compounds 20, 21, 24, 26, 29, 37 and 39). Vehicle controls were included in experiments using the respective solvents. All stock solutions were stored at 4˚C and protected from light. Stock solutions of metal oxides and inorganic salts (compounds 11-16 and 18) were stored for up to 12 months. Stock solutions of organic compounds were stored for no longer than 3 months. 29 Table 1. Test compounds used for the validation of endpoints for in vitro nephrotoxicity. The 41 test compounds were divided into three groups. Group 1 (compounds 1-22) represents nephrotoxicants that directly damage PT. Group 2 (compounds 23-33) comprises nephrotoxicants that do not directly damage PT and injure the kidney by different mechanisms. Group 3 (compounds 34-41) represents nonnephrotoxic compounds. The nephrotoxic effects in humans are compiled and described in Appendix ii, Table S1. Group 1 PT-specific nephrotoxicants No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Compound Gentamicin Tobramycin Rifampicin Tetracycline Puromycin Cephalosporin C 5-Fluorouracil Cisplatin Ifosfamide Paraquat Arsenic(III) oxide Bismuth(III) oxide Cadmium(II) chloride Copper(II) chloride Germanium(IV) oxide Gold(I) chloride Lead acetate Potassium dichromate Tacrolimus Cyclosporin A Citrinin Tenofovir No. 23 24 25 26 27 28 29 30 31 32 33 Group 2 Non-PT-specific nephrotoxicants Compound Vancomycin Phenacetin Acetaminophen Ibuprofen Furosemide Lithium Chloride Lindane Ethylene glycol Valacyclovir Lincomycin Ciprofloxacin No. 34 35 36 37 38 39 40 41 Group 3 Non-nephrotoxic compounds Compound Ribavirin Glycine Dexamethasone Melatonin Levodopa (DOPA) Triiodothyronine Acarbose Atorvastatin 3.9 Drug treatment Cryopreserved cells were thawed at 37˚C and seeded into 24-well microplates at a density of 5 x 104 cells/cm2 (HPTC, HK-2 and LLC-PK1) or 1 x 105 cells/cm2 (HPTC-like). Stem cell-derived HPTC-like cells had lower proliferation rates compared to HPTC, HK-2 and LLC-PK1. 30 Therefore, a higher seeding density was required for HPTC-like cells to form confluent epithelia. Cells were cultivated for 72 h in commercial renal epithelial cell medium purchased from ATCC (HPTC) or Lonza BioScience (Singapore; HPTC-like cells). Both media contained 0.5% fetal bovine serum. Cells were then treated with various compounds at concentrations 1, 10, 100 and 1000 g/ml for 16 h. Respective solvents were used as vehicle controls for the test compounds and all data were subsequently normalized to these vehicle controls. 3.10 Quantitative real-time polymerase chain reaction (qPCR) Total RNA was isolated from cells treated with various compounds using NucleoSpin® RNA II (Macherey-Nagel, Düren, Germany) or RNeasy® Mini Kit (Qiagen, Hilden, Germany). SuperScript® III First Strand Synthesis System (Invitrogen, Carlsbad, CA, USA) and MyCycler® thermal cycler (Bio-Rad, Hercules, CA, USA) were used for cDNA synthesis. qPCR (up to 40 cycles) was then performed with the 7500 Fast Real-Time PCR System (Applied Biosystems, Carlsbad, CA, USA). Procedures were carried out according to the manufacturers’ instructions with the software included in the device. The Sequence Detection Software 7500 Fast version 2.0.5 was used for data analysis. Relative gene expression levels were determined with the 2-CT method [105]. In cases where percentages of GAPDH expression were shown, expression of different target genes were normalized to GAPDH expression of the same samples by using the 2-CT method [106-108]. Primers were designed with the Primer Express Software version 3.0 (Applied Biosystems). Details of all primers used (purchased from Sigma-Aldrich) are provided in Table 2. 31 Table 2. Details of primer pairs and amplicons. The sequences of the primer pairs (forward: F, reverse: R) for the different markers are shown. The sizes of the amplicons are provided in base pairs (bp). Marker Primer Pairs Amplicon (bp) Vimentin (VIM) F 5’-ACCTGAGGGAAACTAATCTG-3’ R 5’-CGTTGATAACCTGTCCATCT-3’ 105 Kidney injury molecule-1 (KIM-1) F 5’-CAGGCTGATCCCATAATGCA-3’ R 5’-CTGCCTCTCCACCAACCTTTAC-3’ 100 Neutrophil gelatinaseassociated lipocalin (NGAL) F 5’-CAAGGAGCTGACTTCGGAACTAA-3’ R 5’-TGCACTCAGCCGTCGATACA-3’ 120 Interleukin-6 (IL-6) Interleukin-8 (IL-8) Interleukin-18 (IL-18) Glyceraldehyde 3phosphate dehydrogenase (GAPDH) F 5’-TGGCTGCAGGACATGACAAC-3’ R 5’-TGAGGTGCCCATGCTACATTT-3’ F 5’-TTGGCAGCCTTCCTGATTTCT-3’ R 5’-GGGTGGAAAGGTTTGGAGTATG-3’ F 5’-GAACCAGTAGAAGACAATTGCATCA-3’ R 5’-CCAGGTTTTCATCATCTTCAGCTA-3’ F 5’-CCCCTTCATTGACCTCAACTACA-3’ R 5’-GACGGTGCCATGGAATTTG-3’ 100 110 91 76 3.11 qPCR-based prediction of drug-induced nephrotoxicity All calculations were performed using Microsoft Office Excel 2003 and 2010. Compounds were predicted as PT-specific nephrotoxicants if the increase of expression of at least one of the marker genes (IL-6 or IL-8) was equal to or higher than a threshold value at any of the compound concentrations tested. Such compounds were thus defined as positive in the in vitro model. Threshold values examined for HPTC, HK-2 and LLC-PK1 cells ranged from 0.3 to 4.0. Threshold values between 0.1 and 5.0 were examined for stem cell-derived HPTC-like cells. 32 Figure 1. Standard terms and definitions used for the statistical analysis. This matrix illustrates the definitions of true positives (TP), false positives (FP), false negatives (FN) and true negatives (TN) based on positive (+) and negative (-) clinical and in vitro data. Definitions of predictive performance metrics are provided. In cases where percentages were shown, the resultant values from the above equations were multiplied by 100%. Adapted from [8]. Reproduced by permission of The Royal Society of Chemistry. Standard terms and definitions of various performance metrics are illustrated and provided in Figure 1. True positives (TP) were defined as PT-specific nephrotoxicants in humans (Table 1, compounds 1-22, group 1) which gave positive results in the in vitro model. True negatives (TN) were defined as non-nephrotoxic compounds (Table 1, group 3, compounds 34-41) or nephrotoxic compounds that do not damage the PT in humans (Table 1, group 2, compounds 2333) that gave negative results in the in vitro model. The sensitivity was calculated by dividing the number of TP by the total number of PT-specific nephrotoxicants (group 1, compounds 1-22). The specificity was calculated by dividing the number of TN by the total number of non-PTdamaging compounds (groups 2 and 3, compounds 23-41). Balanced accuracy was defined as the average of sensitivity and specificity. The positive predictive value (PPV) was calculated by 33 dividing the number of TP by the total number of positives identified by the in vitro model. The negative predictive value (NPV) was calculated by dividing the number of TN by the total number of negatives identified by the in vitro model. The receiver operating characteristic (ROC) curves were generated by plotting sensitivity against (1-specificity) at all threshold values ranging from 0.3 - 4.0 for HPTC, HK-2 and LLC-PK1 cells, and 0.1-5.0 for HUES 7-derived HPTC-like cells. 3.12 Enzyme-linked immunosorbent assay (ELISA) The levels of IL-6 and IL-8 proteins in the cell culture supernatant of HPTC 1 and HPTC 4 were quantified by ELISA after drug treatment. Cells were cultivated for 72 h and subsequently treated with drugs for 16 h. ELISA kits specific for human IL-6 and IL-8 were purchased from Invitrogen (Carlsbad, CA, USA). The experimental procedures were performed as described in the manufacturer’s instructions. 3.13 Gene knockdown by RNA interference (RNAi) Cells were seeded into 6-well, 24-well or 96-well tissue culture microplates at 25,000 cells/cm2. After 24h, cells were transfected with Ambion® p65 (RELA) small interfering RNA (Life Technologies, Carlsbad, CA, USA) and X-tremeGENE siRNA transfection reagent (Roche, Mannheim, Germany). Ambion® non-target siRNA and GAPDH siRNA were used as controls (Life Technologies). Amounts of siRNA used were 12 pmol/well, 40 pmol/well and 160 pmol/well for 96-, 24-, and 6-well microplates respectively. Cells were transiently transfected under normal cell culture conditions (5% CO2, 37˚C) for 48h. 34 3.14 Immunostaining Immunostaining was performed as described [68]. Briefly, cells were seeded in 96-well microplates at densities of 16,000 cells/cm2 (LLC-PK1 cells) and 50,000 cells/cm2 (HPTC and HK-2 cells), due to different doubling rates of various cell types. Cells were cultivated for 72 h and were then treated with test compounds for 16 h. After fixation for 10 min with 3.7% formaldehyde in phosphate-buffered saline, cell nuclei were stained with 4',6-diamidino-2phenylindole (DAPI; Merck), NF-B p65 was detected with a human NF-B p65-specific primary antibody (Abcam, Cambridge, England, UK) and a fluorochrome-conjugated secondary antibody, which was obtained from Life Technologies. 3.15 High content screening (HCS) Images of fixed cells stained with DAPI and by immunofluorescence were captured with the ImageXpress Micro High Content Screening System (Molecular Devices, Sunnyvale, CA, USA). 3 replicates were imaged for each cell type and each treatment condition. From each replica 9 fields were imaged. Cell nuclei were counted after cells were treated with test compounds for 16 h and fluorescence intensity was measured on each individual image from which average values were derived. Data acquisition and analysis was performed by MetaXpress® 2.0 (Molecular Devices). For immunofluorescence of NF-B p65, both nuclear and cytoplasmic fluorescence levels were measured. Calculations of NF-B p65 / cytoplasmic NF-B p65 ratios were based on average cytoplasmic and nuclear levels of fluorescence intensity normalized to cell number. Cells which had a nuclear NF-B p65 / cytoplasmic NF-B p65 ratio ≥ 1 were defined as positive (+). Compartmentalization of cell nuclear and cytoplasmic regions was performed using the MetaXpress® 2.0 as a standard procedure optimized by a colleague (Dr. Sijing Xiong). 35 3.16 Immunoblotting Protein expression levels of NF-B p65 in HPTC and HK-2 cells were examined by immunoblotting after transient transfection with p65 siRNA. Cell lysates were prepared with Pierce® radioimmunoprecipitation assay (RIPA) buffer (Thermo Scientific, Waltham, MA, USA) supplemented with 1 mM phenylmethylsulfonyl fluoride (Sigma-Aldrich) and 1× protease inhibitor cocktail (Nacalai Tesque, Kyoto, Japan). Protein concentrations in lysates were determined using Pierce® bicinchoninic acid (BCA) assay kit and absorbance was measured with a microplate reader (Tecan Safire2 TM , Männedorf, Switzerland). Proteins samples were treated with NuPAGE® sample reducing agent (Life Technologies) according to the manufacturer’s instructions. 10-20 mg of protein samples were loaded into NuPAGE® Novex® 4-12% Bis-Tris precast gels (Life Technologies). Gels were run in NuPAGE® 2-(Nmorpholino)ethanesulfonic acid (MES) sodium dodecyl sulfate (SDS) running buffer (Life Technologies) at a voltage of 80V for 30 minutes followed by 100V for 60 minutes. Samples were transferred onto polyvinylidene fluoride (PVDF) membranes using the iBlot® Gel Transfer Device (Life Technologies). Rabbit anti-p65 primary antibody (Abcam) was added to the membranes in Tris-buffered saline (TBS) containing 1% Tween-20 and 10% bovine serum albumin (BSA). Donkey anti-rabbit horseradish peroxidase-conjugated secondary antibody (GE Healthcare, Buckinghamshire, UK) was used to detect primary antibodies bound to the PVDF membranes. Samples were visualized by chemiluminescence using Pierce® ECL western blotting substrate (Thermo Scientific) and ChemiDocTM XRS gel documentation system (BioRad). 36 3.17 Standard toxicity assays Cells were treated in the same way as for the IL-6/IL-8-based assay. Cellular ATP depletion was measured with the Molecular Probes® ATP determination kit (Life Technologies). Compound 11 was also tested with the CellTiter-Glo® Assay (Promega, Madison, WI, USA). Assay kits for determining GSH depletion (GSH-Glo™ glutathione assay) and LDH leakage (CytoTox-ONE™ homogeneous membrane integrity assay) were purchased from Promega. All assays were performed according to the manufacturers’ instructions. Assay readouts were obtained using a Safire2 TM microplate reader (Tecan). 3.18 Statistics Microsoft Excel 2010 was used for all calculations. The unpaired t-test was used for statistics and all data were compared with corresponding vehicle controls. Normal distribution of the data was confirmed using SigmaStat (3.5) (Systat Software Inc., Chicago, IL, USA). Z’ values were calculated as described [109]. 37 4. Results 4.1 Evaluation of culturing substrates suitable for in vitro toxicology with primary human endothelial and renal cells Endothelial and renal cells are widely used for in vitro toxicology studies [3, 110]. Proper cell performance under in vitro conditions is essential. Prior to my PhD thesis, extensive work has been carried out in the lab on the systematic characterization of culturing conditions affecting the performance of human primary cells, in particular HPTC. As mentioned in the introduction, major areas that have been investigated included coatings [67, 68], cell culture medium composition and growth factors [69, 70], as well as the development of reliable cell sources, cocultures, microfluidic and 3D models [65-67, 70-72]. I performed during my thesis part of the experiments for the evaluation of culturing substrates suitable for the in vitro culture of primary endothelial and renal cells. The work on endothelial cells was performed with HUVECs. Previous data from our group suggested that cell numbers and differentiation of HPTCs and HUVECs were mainly affected by substrate stiffness (all of the results described in [5]). To further investigate the effects of substrate stiffness on cell morphology, cell numbers, cytoskeleton arrangement and the differentiation of such adherent cell types, I investigated HUVECs seeded on 3 stiff materials: TCPS, CG, and TX. A more compliant substrate, polycarbonate membranes (PC-1), was tested for comparison (Fig. 2). Young’s modulus is a measure of the stiffness of materials. The Young’s modulus values for the three stiff materials were 3,500 megapascals (MPa; TCPS), 90,000 MPa (CG) and 2,700 MPa (TX), whereas that of PC-1 was only 63.5 ± 16.0 MPa [5]. Only synthetic materials were included here and no extracellular matrix coatings were used. To develop an in vitro model for predicting organ38 specific toxicity of presumably large number of test compounds, it is important that the culture platform is inexpensive and relatively easy to prepare in a large scale. In addition, my previous data showed that coatings are not required [72]. Fig. 2 shows immunostaining of various cellular components and markers of HUVECs cultured on the different substrates. The results showed low cell numbers and poor morphology on TX (Fig. 2 c, g, k, o) and especially on PC-1 (Fig. 2 d, h, l, p). The organization of the actin cytoskeleton of cells cultured on the stiff materials (TX, TCPS and CG) was different in comparison to PC-1: actin stress fiber formation was observed only on the three stiff materials, but not on PC-1 (Fig. 2 a-h). Actin stress fibers are a typical feature of endothelial cells, also in vivo [111, 112]. Apart from the features outlined above, proper cell differentiation is also an essential indicator of cell performance on biocompatible materials. Here, I examined cell differentiation of HUVECs on the same materials as described above by performing immunostaining of two endothelial cell markers CD31 (also called platelet endothelial cell adhesion molecule-1 (PECAM-1)) and CD146 (also called melanoma cell adhesion molecule (MCAM) or cell surface glycoprotein MUC18). The results showed that both markers were expressed and showed characteristic enrichment at cell junctions on TCPS and CG, forming typical chicken wire-like patterns (Fig. 2i, j, m, n). Largely confluent cell layers were also generated on TX, but CD31 and CD146 expression was suboptimal and their subcellular localization was disturbed, suggesting partial dedifferentiation of the cells (Fig. 2 k, o). Cell attachment and growth was most compromised on PC-1, where HUVECs did not form a confluent monolayer, and endothelial cell marker expression and subcellular localization were severely disturbed (Fig. 2 l, p). These results 39 suggested a strong correlation between substrate stiffness and cell performance, as HUVECs were only able to form properly differentiated endothelia on the 2 stiffest materials (TCPS and CG). Notably, the chemistry and surface chemical structures and other features of these two substrates were very different [5]. Figure 2. Detection of F-actin fibres, CD31 and CD146 in HUVECs. HUVECs were grown on various materials as indicated (TCPS: tissue culture polystyrene; CG: cover glass; TX: Thermonax coverslips; PC1: polycarbonate). Cell nuclei were stained with DAPI (blue). (a-h) display F-actin (red). (e-h) shows enlarged boxed areas displayed in (a-d). Arrowheads indicate some areas of stress fiber formation. (i-p) shows immunostaining of CD31 (red; i-l) and CD146 (green; m-p). Representative images from different batches of HUVECs are shown. Scale bars: 25 m (e-h) and 50 m (a-d; i-p). Adapted with permission from [5] Copyright 2012 Elsevier Ltd. 40 In addition, immunostaining of the tight junctional protein zonula occludens-1 (ZO-1) and the endothelial glycoprotein von Willebrand factor (vWF) was performed in the case of TCPS. The results showed robust vWF expression and tight junction formation (Fig. 3). Figure 3. Immunostaining of ZO-1 (green) and vWF (red) in HUVECs cultivated on TCPS. Cell nuclei were stained with DAPI (blue). Scale bar: 50 m. Adapted with permission from [5] (Supplementary Information) Copyright 2012 Elsevier Ltd. My work on HUVECs described here was performed in the context of a larger study and essentially similar results had been obtained with respect to HPTC [5]. However, the above results were derived from testing cell performance on different materials with varying physical and chemical properties. To further confirm that substrate stiffness was the major determinant of cell performance, materials with similar chemical composition but different stiffness were tested. For these experiments, HPTCs and HUVECs were seeded on electrospun PLA membranes (Young’s modulus = 28 ± 7 MPa, n = 3) or PLA films (Young’s modulus = 482 ± 75 MPa, n = 3). Both materials were derived from the same PLA solution (see Materials and Methods). Immunostaining was performed to assess cell differentiation on these materials. 41 Figure 4. Cell performance on PE and PLA films and membranes. (a-f) HPTCs or HUVECs were seeded ( 5 x 104 cells/ cm2) on electrospun PLA membranes (a, c, e) or PLA films (b, d, f). Specimens were fixed after 3 days and ZO-1 (a and b, red), CD31 (c and d, green) and CD146 (e and f, red) were detected by immunostaining (DAPI: blue). Arrowheads in (b) and (f) indicate ZO-1 or CD146 enrichments at cell boundaries. Three replicas were analyzed for each cell type and material and from each sample at least 7 images were captured. The figure shows representative results. Scale bars: 100 m. Adapted with permission from [5] Copyright 2012 Elsevier Ltd. Fig. 4 showed that HPTCs were able to form confluent monolayers on both electrospun PLA membranes and PLA films after 3 days of culturing. However, enrichment of ZO-1 at cell boundaries was only observed on the stiffer PLA films, whereas in HPTCs cultured on the more compliant electrospun PLA membranes, ZO-1 was diffusely distributed in the cytoplasm (Fig. 4 a, b). Absence of ZO-1 enrichment at cell boundaries indicates de-differentiation of HPTCs [68]. 42 On the other hand, HUVECs could only form a monolayer on PLA films. Typical chicken wirelike patterns were obtained for CD31 staining only on this substrate, and CD146 enrichment was observed in at least some of the cells (Fig. 4 d, f). HUVEC numbers were substantially reduced on the electrospun PLA membranes, where cells failed to proliferate and form confluent endothelia (Fig. 4 c, e). Overall, these results suggested that substrate stiffness was indeed a major determining factor of the performance of soft tissue-derived human primary cells under in vitro culturing conditions. TCPS appeared to be among the most suitable materials for developing in vitro models with respective cell types due to its high substrate stiffness, which was associated with excellent cell performance. This was observed here, as well as in my previous work [72]. It is important to note that this thesis shows only part of the characterization with a large variety of different substrates and their effects on various aspects of HPTC performance. Here I focused on the work performed by myself, and the full picture with all of the results is provided in [5]. With all of the 16 different substrates tested in [5] a thorough material characterization was performed and in addition to stiffness also surface roughness, hydrophilicty, chemical composition, pore size, porosity, surface charge and protein adsorption were determined. With respect to all of these features a correlation analysis was performed and only substrate stiffness correlated with cell performance for both HUVECs and HPTCs [5]. Further, it was also shown that consistent HPTC performance was only obtained on TCPS in terms of both marker gene expression and functionality (as assessed by responsiveness to parathyroid hormone and gamma-glutamyl transferase (GGT) activity [5]. In addition, our lab has performed extensive characterization of PTC performance on ECM coatings [68] and other coatings [66, 67]. Performance of every type of PTC used was tested on coated and uncoated substrates, and no differences were found 43 between uncoated TCPS and TCPS coated with the most appropriate ECM coatings (collagen IV and laminin [68]) when HPTC derived from ATCC were used and our own isolated batches of HPTC. Further work on substrate optimization and more tests were also regularly performed by other staff in the lab (unpublished work). It regularly turned out that uncoated TCPS was the best, also for HPTC. In fact, coating was not recommended for the maintenance of the commercially obtained HPTC batch used in this thesis, according to the instructions from the vendor (ATCC). Lastly, TCPS is readily available in suitable formats as it is commonly used in tissue culture multi-well plates. Therefore, in the subsequent studies, non-ECM-coated TCPS-based multi-well plates were employed as the culturing platform for primary cells such as HUVECs and HPTCs. 44 4.2 Cell type-specific cytotoxicity of chemical compounds 4.2.1 Cell type-specific cytotoxicity of layered clays and MCF-26 After confirming that TCPS was the most suitable culturing substrate, it was then important to select appropriate cell types for developing in vitro models for predicting organ-specific toxicity. Immortalized cell lines such as the standard murine fibroblast cell line NIH/3T3 are commonly used in toxicity testing by pharmaceutical companies. In case of in vitro nephrotoxicology, renal cell lines were widely used [3]. However in my previous work [72], the results suggested that in the case of nephrotoxicity, HPTCs were far more sensitive than immortalized cell lines to the cytotoxic effects of cadmium-based quantum dots. Also, the porcine cell line LLC-PK1 demonstrated different uptake dynamics as compared to human cell line HK-2 or HPTCs. These and other results [3] highlighted the importance of using the most appropriate cell type for assessing the toxicity of a chemical compound in vitro. In this section of the thesis, I developed a model for testing toxicity of hemostatic agents on relevant skin cell types such as fibroblasts, keratinocytes and endothelial cells. Renal proximal tubular cells were also included for comparison, though they were not of primary importance in this model. Test compounds used here included aluminum silicate-based layered clays (kaolin, bentonite and montmorillonite) and MCF-26. Layered clays, such as kaolin, are frequently used as the hemostatic agent in battle gauzes on the battlefields to promote blood clotting [113, 114]. In practice, different cell types at the wound sites such as HEKs, HDFs and endothelial cells are directly exposed to these hemostatic agents. However, layered clays have been shown to have profound cytotoxic effects on HUVECs, primary murine neurons and RAW267.4 murine macrophage-like cells, but not on HeLa cells or murine neuroblastoma cells [115-117]. Such 45 cell-type specific toxic effects on relevant human cell types needed to be further addressed. On the other hand, MCF-26 demonstrated similar hemostatic potency as the most effective commercial hemostatic agents [118], and can be potentially used as an alternative hemostatic agents, but its cytotoxicity to relevant human cell types was not clearly understood. In this part of the thesis, cytotoxicity of MCF-26 was also thoroughly investigated and compared with layered clays using the different relevant cell types mentioned above. First, I investigated the differences between various cell types in terms of how they responded to the cytotoxic effects of kaolin. Different concentrations of kaolin were applied. In order to address inter-donor variability of the primary cells, three different batches (each from a different donor) of HDFs, HEKs and HUVECs were included in the tests. Human primary (HPTC) and immortalized (HK-2) renal proximal tubular cells were included for comparison with the skin cell types commonly found at wound sites. Lastly, immortalized NIH/3T3 mouse fibroblasts, a widely used standard cell line in toxicology studies, was also included as a control. In order to assess cell viability, the neutral red uptake (NRU) assay was performed on various cell types after overnight treatment with the different compounds. The NRU assay was found to be most reliable as the results were linearly related to cell numbers [72], whereas in the commonly used MTS assay, absorbance readings become saturated at high cell seeding density (Fig. 5). Therefore, all cell viability data shown in this section (4.2) were obtained with the NRU assay. This assay is also recommended by the International Standard ISO 10993-5:2009(E). 46 Figure 5. Relationship between absorbance measure with the MTS assay and cell numbers. HPTC and HK-2 cells were seeded at different densities and the MTS assay was performed after 24 hours. The absorbance readings obtained with the MTS assay (mean +/- s.d., n = 3) were plotted against the seeding densities. Adapted from [6] (Supplementary Information). Reproduced by permission of The Royal Society of Chemistry. Kaolin was applied to the cells at concentrations up to 250 g/ml in order to determine its effects on cell viability. Dose-response curves were displayed in Fig. 6. The left-hand columns in Table 3 summarizes the IC50 values as well as the percentages of cell viability at the highest concentration used (250 g/ml) for each tested cell type and batch. The results indicated that the cytotoxic effects of kaolin were strongly cell type-specific. The viability of NIH/3T3 fibroblasts was not significantly reduced by kaolin (P > 0.05) and the proliferation of two batches of human primary skin fibroblasts (HDF1 and HDF2) even appeared to be promoted by the presence of kaolin (P < 0.05; Fig. 6 and Table 3). This could be due to hormesis, a common observation in a broad spectrum of biological models, including in vitro skin biology, where sub-lethal doses of toxicants can transiently lead to increased cell growth [119]. In contrast, in all other tested cell types and batches, there was a dose-dependent decrease of cell viability. Among the different cell types, HUVECs were in general the most sensitive: the IC50 values of the three different batches 47 of HUVEC ranged between 50~125 g/ml. There was also substantial cell death observed in the case of HEK, where the IC50 values ranged between 180~250 g/ml (Table 3, the cell viability of HEK1 and HEK2 was slightly above 50% at 250 g/ml). The human renal cells tested (HPTCs and HK-2 cells) were only moderately affected by the cytotoxic effects of kaolin and their IC50 values were clearly above 250 g/ml. Figure 6. Dose-dependent effects of kaolin on cell viability. Kaolin was applied at concentrations of up to 250 g/ml to the cell types indicated on the right-hand side. Three different batches each of HDF, HEK and HUVEC were tested. Cell viability was determined with the NRU assay and all values were normalized to the values obtained with untreated control cells (set to 1). Error bars show the s.d. ( n = 3). Adapted from [6]. Reproduced by permission of The Royal Society of Chemistry. Overall, these results demonstrated strong cell type-dependent cytotoxic effects of kaolin. The sensitivity of tested cell types could be ranked in the order HUVEC > HEK > HK-2/HPTC > NIH/3T3 > HDF. While the cell viability of HUVECs fell drastically even at low concentrations of kaolin, the fibroblastic cell types (either immortalized murine cell line or primary human 48 fibroblasts) did not display any significant reduction in cell viability, even at the highest concentration of kaolin applied. It is also important to note that, although some inter-donor variability was observed among different batches of a same cell type, cell-type specificity had a more prominent influence in determining how cells responded to kaolin treatment. Table 3. Cell viability (%) at the maximal concentrations of kaolin, bentonite, montmorillonite and MCF26 and IC50 values. Adapted from [6] with modifications. Reproduced by permission of The Royal Society of Chemistry. Kaolin Bentonite Montmorillonite MCF-26 Cell Type IC50 (g/ml) % at 250 g/ml IC50 (g/ml) % at 1000 g/ml IC50 (g/ml) % at 1000 g/ml IC50 (mg/ml) % at 7 mg/ml HUVEC 1 125 ± 47 39 ± 5 11 ± 1 18 ± 1 65 ± 3 18 ± 0 6.3 ± 0.2 50 ± 3 HUVEC 2 48 ± 4 35 ± 2 17 ± 2 37 ± 3 33 ± 2 25 ± 3 2.1 ± 0.8 27 ± 7 HUVEC 3 51 ± 2 21 ± 2 17 ± 4 34 ± 2 95 ± 13 33 ± 4 0.7 ± 0.0 21 ± 2 HDF 1 > 250 138 ± 14 300 ± 85 45 ± 3 272 ± 99 29 ± 1 2.0 ± 0.2 13 ± 3 HDF 2 > 250 119 ± 8 > 1000 60 ± 3 335 ± 32 38 ± 5 5.6 ± 1.4 40 ± 8 HDF 3 > 250 107 ± 3 468 ± 18 52 ± 4 454 ± 120 57 ± 1 5.0 ± 0.7 41 ± 3 HEK 1 > 250 58 ± 2 > 1000 60 ± 6 > 1000 78 ± 6 > 7.0 92 ± 9 HEK 2 >250 51 ± 12 58 ± 14 41 ± 6 > 1000 62 ± 2 > 7.0 60 ± 4 HEK 3 181 ± 44 46 ± 2 34 ± 10 41 ± 2 > 1000 69 ± 2 > 7.0 78 ± 3 NIH/3T3 > 250 96 ± 5 > 1000 56 ± 1 461 ± 22 38 ± 2 > 1000 ND HK-2 > 250 62 ± 6 > 1000 82 ± 4 > 1000 66 ± 2 > 1000 ND HPTC > 250 77 ± 9 > 1000 55 ± 3 280 ± 27 34 ± 2 > 1000 ND Further tests where only kaolin was incubated in cell culture medium in cell-free multi-well plates revealed that kaolin interfered with the absorbance measurements of the multi-well platebased NRU assay (data not shown). Therefore, kaolin was only tested at concentrations ≤ 250 g/ml, as higher concentrations would exaggerate the readings for the NRU assay, which would then lead to overestimation of cell viability. 49 Next, I tested the cytotoxicity of other layered clays (bentonite and montmorillonite) and MCF26. These compounds could be used at higher concentrations of up to 1000 g/ml as they interfered less strongly with the absorbance readings of NRU assay as compared to kaolin (data Figure 7. Dose-response curves. HUVEC, HDF and HEK were treated with up to 1000 g/ml of bentonite (black), montomorillonite (grey) or MCF-26 (red). Cell viability was determined by NRU assay and all values were normalized to the values obtained with untreated control cells. Error bars show the s.d. (n = 3). Adapted from [6]. Reproduced by permission of The Royal Society of Chemistry. not shown). The same cell types were used as in the case of kaolin and the results of the NRU assays are summarized in Fig. 7 and Table 3. The NRU results showed that both layered clays tested had substantial cytotoxic effects. Similar to the case of kaolin, HUVEC were also most sensitive to the effects of bentonite (black graphs in Fig. 7) and montmorillonite (grey graphs) and the IC50 values ranged between 11 ~ 17 g/ml 50 (bentonite) and 33 ~ 95 g/ml (montmorillonite) (Table 3). Although the cytotoxic effects of bentonite and montmorillonite on the other cell types were more variable, in general, the IC50 values of at least one of these two compounds were clearly below 1000 g/ml in all cell types and batches tested, with exception of HK-2 cells and HEK batch 1 (Table 3). On the other hand, when cells were treated with MCF-26, cell viability was generally higher as compared to layered clays (red graphs in Fig. 7). As more than 50% of the cells were still viable at the highest tested concentration, IC50 values for MCF-26 could not be calculated within the tested range of 1 ~ 1000 g/ml. This was also observed in the case of HUVECs, which were the most sensitive to the cytotoxic effects of the other tested compounds. These results showed that MCF-26 had strongly reduced cytotoxicity in comparison to layered clays. Therefore much higher concentrations in the mg/ml range (up to 7 mg/ml) were tested in addition. Such concentrations were also more clinically relevant as MCF-26 demonstrated its efficacy in promoting blood clotting when used in the mg/ml range on human plasma [6]. The IC50 values were summarized in table 3 and the results again revealed prominent cell type-specific effects. In this case the HEKs were the least sensitive with IC50 values of > 7.0 mg/ml in all three batches. Again, inter-donor variability was observed and the IC50 values of the different batches of HDF and HUVEC ranged between 2.0 ~ 6.3 mg/ml with the exception of HUVEC batch 3, where an IC50 value of ~ 0.7 mg/ml was determined (Table 3). This discrepancy from the previous result, where cell viability remained above 50% when treated with up to 1000 g/ml of MCF-26 for this batch (red graph, HUVEC 3, Figure 7), can probably be explained by the fact that this cell batch was used at a higher passage number (P6) when MCF-26 was tested at higher concentrations (P4 was used in the other experiments). Nevertheless, the lowest IC50 value obtained with this cell 51 batch and MCF-26 (0.70 mg/ml, Table 3) was still ~ 7-fold higher than the highest value obtained with this cell batch and a layered clay (95.0 +/- 12.5 mg/ml, Table 3, montmorillonite). Such drastic differences between the toxic effects of MCF-26 and the tested layered clays, especially in HUVECs, could be due to differences in the uptake and/or adherence of the clay materials onto cell surfaces. This question was further addressed experimentally. Figure 8 shows that when cells were incubated with kaolin and other layered clays, these compounds adhered strongly to cell and substrate surface and could not be completely removed by repeated washing (Fig. 8 e - j). In contrast, adherence of MCF-26 was weaker and this compound was largely removed by washing (Fig.8 c, d). A comparison of washed and unwashed cells is also shown in supplementary data, Figure S1. As such, these results confirmed that the hemostatic agent MCF-26 was less cytotoxic to relevant human cell types than currently used layered clays (bentonite, montmorillonite and kaolin). The IC50 values were in the mg/ml range when relevant primary human cell types were tested. More importantly, these results showed strong cell type specificity in terms of how cells responded to the same test compound. In case of MCF-26 HUVEC and HDF were more sensitive to the cytotoxic effects at high concentrations of > 1000 g/ml, whereas HEK cells displayed cell viability levels of > 60% even at the highest concentration of MCF-26 tested (7 mg/ml). 52 Figure 8. Adhesion of layered clays and MCF-26 to the cell surface. HUVEC were exposed for 10 minutes to 1 mg/ml of kaolin, bentonite, montmorillonite or MCF-26 (control: untreated). Cells were washed subsequently and images were captured with the CytoViva® system after fixation. Particles of test compounds appear white on the images. The left-hand images show fields of cells (scale bars: 100 m), whereas the right-hand images show individual cells (scale bars: 10 m). Adapted from [6]. Reproduced by permission of The Royal Society of Chemistry. 53 4.2.2 Cell type-specific cytotoxicity of silver nanoparticles (Ag NPs) In order to compare the cell type-specific cytotoxic effects of layered clays and MCF-26 to the effects of other cytotoxic compounds, we also performed NRU assays with Ag NPs and DMSO. Ag NPs are widely applied as antibacterial agents in consumer products, cosmetics and wound dressings [120, 121]. However, the cytotoxicity of Ag NPs, which is due to the leaching of silver ions, is well documented in the literature and is often controversially discussed with respect to the use of such products [121, 122]. Here we show that the IC50 values of Ag NPs were in the same range as those of layered clays (Table 3 and Table 4). Again, sensitivity towards the cytotoxic effects of Ag NPs was mainly dependent on the cell types tested: HUVEC and HK-2 cells displayed the lowest IC50 values, whereas cell viability of HEK and NIH/3T3 cells remained well above 50% even at the highest concentration of Ag NPs applied (417 g/ml). DMSO is a mildly cytotoxic organic solvent that is frequently used for the cryopreservation of cells and for dissolving pharmaceuticals in clinical applications [123, 124]. The IC50 values of DMSO ranged from ~ 25 mg/ml to > 100 mg/ml (Table 4) and were about 1 - 2 orders of magnitude higher than the IC50 values obtained with MCF-26 (Table 3). However, HUVEC again displayed the highest sensitivity and lower IC50 values as compared to most of the other cell types. The lowest IC50 value was obtained here with HK-2 cells, which were relatively insensitive to layered clays. 54 Table 4. IC50 values and cell viability (%) at the maximal concentrations of Ag NPs and DMSO. The maximal concentration of Ag NPs was 417 g/ml and the maximal concentration of DMSO was 100 mg/ml. The table provides the mean values ± s.d. (n = 3). Adapted from [6] (Supplementary Information). Reproduced by permission of The Royal Society of Chemistry. Ag NPs (10 nm) DMSO Cell Type IC50 (g /ml) % at 417 g/ml IC50 (mg/ml) % at 100 mg/ml HUVEC 1 140 ± 49 32 ± 3 85 ± 3 41 ± 2 HUVEC 2 66 ± 19 29 ± 3 39 ± 4 32 ± 6 HUVEC 3 77 ± 5 38 ± 6 46 ± 2 31 ± 1 HDF 1 > 417 81 ± 8 > 100 63 ± 4 HDF 2 137 ± 12 22 ± 2 > 100 79 ± 6 HDF 3 347 ± 6 22 ± 3 > 100 88 ± 4 HEK 1 > 417 72 ± 13 > 100 108 ± 3 HEK 2 > 417 79 ± 19 > 100 103 ± 1 HEK 3 > 417 128 ± 14 > 100 117 ± 7 NIH/3T3 > 417 62 ± 10 > 100 52 ± 7 HK-2 84 ± 3 29 ± 6 25 ± 4 27 ± 4 HPTC 343 ± 19 39 ± 1 > 100 74 ± 5 Together, the results obtained here showed strong cell type-specific effects with respect to the cytotoxicity of the compounds tested. Endothelial cells were generally most sensitive. NIH/3T3 cells and also human primary fibroblasts were most insensitive. It should be noted that such fibroblastic cell types are most frequently used for cytotoxicity assays [44]. This emphasizes the 55 necessity to work with relevant and preferably primary human cell types, which are more sensitive than respective cell lines [72]. Although when using human primary cells, inter-donor variability also plays a role in affecting the cellular responses towards toxic compounds, my results showed that cell type specificity had a dominant effect. Further, the results showed that MCF-26, which has strongly hemostatic effects [118], has much lower cytotoxicity than widely used hemostatic agents [115-117] and Ag NPs, which are also widely applied [121, 122]. 56 4.3 Identification and validation of endpoints suitable for in vitro prediction of druginduced nephrotoxicity in humans 4.3.1 Model design and identification of suitable endpoints for in vitro nephrotoxicology As demonstrated in the previous section, different cell types can respond very differently to the same compound. Therefore in organ-specific toxicology studies, it is essential to employ the most relevant cell type for in vitro analysis. In this part of the thesis, my focus was to develop in vitro models for prediction of drug-induced nephrotoxicity. For this application HPTCs are the most appropriate cell type due to their physiological role in filtrate concentration and xenobiotic clearance in humans. It is also important to note that use of human primary cells can avoid issues associated with interspecies variability and problems associated with cellular changes due to immortalization, as demonstrated in a previous study [72]. Routine characterization of all batches of HPTC used in this section was performed by Dr. Karthikeyan Kandasamy (IBN), based on quantification of the expression levels of 31 different marker genes by qPCR [8] (Supplementary data, Figure S2). Some of these markers were further examined at the protein level by immunostaining and immunoblotting. These characterization procedures were regularly performed as a quality control step to ensure consistent cell phenotypes across different batches of HPTC. Cells were seeded and cultivated in uncoated TCPS-based multi-well plates. As shown in section 4.1 and previous studies, performance of primary human renal and endothelial cells was best sustained on uncoated TCPS as compared to other materials with or without extracellular matrix coating [5, 67]. High seeding density was used and cells were cultivated for three days before drug exposure to generate a differentiated epithelium with tight junction formation. To obtain appropriate cell type-specific responses in 57 vitro, it is essential to ensure that cells are well differentiated with their proper phenotypes adequately expressed. Also, control experiments showed that HPTC responded more sensitively to nephrotoxicants when seeded at the high densities used here (data not shown). To identify a suitable endpoint, I first assessed the relative gene expression levels of different marker genes related to PT injury with respect to untreated negative controls. Among these markers assessed, kidney injury molecule-1 (KIM-1) and neutrophil gelatinase-associated lipocalin (NGAL) often show up-regulation in the tubular epithelium in vivo after injury and are potential novel biomarkers for the early detection of AKI [73-76, 125]. Interleukin (IL)-18 is upregulated in the PT epithelium in diseased and injured kidneys and might be a useful biomarker for detecting kidney toxicity [73, 126, 127]. Here I also included the mesenchymal marker vimentin (VIM), which is also up-regulated in injured kidneys [128, 129]. IL-6 and IL-8 are key pro-inflammatory cytokines and can be expressed in PT and PT-derived cells in vivo and in vitro [72, 130-133]. Different studies previously showed that up-regulation of IL-6 and IL-8 occurred in injured or diseased kidneys [80-82]. It is also well established that these pro-inflammatory cytokines play a pivotal role in the pathophysiology of AKI, also in case of drug-induced AKI [7, 134]. Significant nephrotoxicant-induced up-regulation of IL-6 has also been demonstrated in a kidney culture model using purified murine PTs [135]. To examine the effects of nephrotoxicants on the expression patterns of these six marker genes in vitro, two batches of HPTC (HPTC 1 and 4) were exposed to high doses of gentamicin and CdCl2 overnight and marker gene expression levels were subsequently analyzed by qPCR. All expression levels obtained were normalized to those of glyceraldehyde 3-phosphate dehydrogenase (GAPDH), which was used as the endogenous control. For the expression level 58 of a specific marker gene to be a suitable endpoint for detecting nephrotoxicity of drugs in vitro, it is most desired that the gene is only expressed at relatively low levels in negative control cells and that its expression is highly inducible by nephrotoxicants. In this case, the candidate marker gene should also be consistently up-regulated in response to different nephrotoxicants and such responses should not be drastically affected by inter-donor variability. The results obtained from qPCR revealed that expression levels of IL-6 and IL-8 were significantly and consistently elevated by both nephrotoxicants in both batches of HPTCs (Fig. 9). IL-8 showed the highest level of induction by nephrotoxicants among all marker genes tested. 59 Figure 9. Marker gene expression in response to nephrotoxicants. Two batches of HPTC (1 and 4, derived from different donors) were treated with 2.5 mg/ml gentamicin (light gray bars) and 10 g/ml CdCl2 (dark gray bars). Vehicles controls (set to 1) were shown as white bars. High doses of test compounds were used to ensure the induction of cellular responses to these nephrotoxicants. The relative expression levels (yaxis) of the marker genes shown on the x-axis were determined by qPCR. Mean fold changes (normalized to vehicle controls) in expression were shown with standard deviation (s.d.; n = 3). Asterisks indicate significant differences (P < 0.05) in comparison to vehicle controls. Adapted from [8]. Reproduced by permission of The Royal Society of Chemistry. Fig. 9 also showed that, among the other candidate marker genes, NGAL displayed consistent up-regulation when treated with nephrotoxicants. However, the magnitudes of up-regulation were limited. The fold increases ranged between 1.8 and 3.5, which were considerably lower than those of IL-6 and IL-8 in both HPTC batches. VIM was up-regulated only in HPTC 1. Upregulation of KIM-1 and IL-18 was inconsistent, and only induced by one of the compounds and only in one cell batch. The low levels of induction of NGAL expression and the partial 60 irresponsiveness of KIM-1 observed here were consistent with the results obtained with other in vitro models based on human primary renal proximal tubular cells and a PT-derived cell line2. It is important to note that primary PTCs were isolated from disruption of kidney tissues, thus it is not unexpected that such cells always exhibit some degree of cellular injury response. Here my results showed that VIM, NGAL and KIM-1, even in untreated negative control cells, were already expressed at relatively high levels in terms of percentages of the endogenous control, GAPDH (Fig. 10). This is also consistent with a previous study in case of VIM [87]. Such high background expression levels of injury markers VIM, NGAL and KIM-1 in cultured HPTC might provide insights to the reason why there was a lack of further up-regulation when cells were exposed to nephrotoxicants. Among all candidate marker genes, only expression levels of IL-6 and IL-8 were consistently < 0.1% of GAPDH expression in control cells. This again strengthened my hypothesis that among all marker genes tested the expression levels of IL-6 and IL-8 appeared to be the most reliable endpoints for predicting drug-induced nephrotoxicity in vitro. 2 PREDICT-IV, third and fourth project periodic reports http://www.predict-iv.toxi.uni-wuerzburg.de/periodic_reports/4th_annual_report/ 61 * * Figure 10. Marker gene expression in response to nephrotoxicants. Data were derived from the same experiment as Fig. 9. The expression levels of the marker genes shown on the x-axis were determined by qPCR and expressed as percentage (y-axis) of GAPDH expression of the respective samples. Mean percentage values (normalized to vehicle controls) were shown with standard deviation (s.d.; n = 3). Asterisks indicate significant differences (P < 0.05) in comparison to vehicle controls. In order to further investigate the expression levels of these two marker genes as predictive endpoints, I next performed ELISA on both batches of HPTC to assess the concentration of secreted IL-6 and IL-8 protein in cell culture supernatants after cells were treated overnight with either gentamicin or CdCl2. Results showed that only CdCl2 was able to induce a significant increase in secreted levels of both interleukins. This occurred in one of the cell batches (HPTC 4, 62 Fig. 11). CdCl2 treatment also caused an elevation of IL-8 levels in the supernatants of HPTC 1 after CdCl2 exposure, but no increase in IL-6 protein was observed in this cell batch. Gentamicin appeared to be ineffective, as no elevated secretion of either interleukin was observed in either cell batch. Together, these results suggested that the level of secreted IL-6 or IL-8 proteins would not be a good endpoint for predicting drug-induced nephrotoxicity in vitro, as there was both significant inter-donor variability in the responses, and inconsistency in results when different nephrotoxicants were used. This could be due to the fact that some nephrotoxicants, including the aminoglycoside gentamicin, are inhibitors of protein synthesis [136, 137]. Therefore a more upstream endpoint, such as mRNA levels as determined by qPCR, would be more reliable for predicting drug-induced nephrotoxicity in vitro. * * * Figure 11. Protein concentrations of IL-6 and IL-8 in cell culture supernatants. Two batches of HPTC (1 and 4, derived from different donors) were treated with 2.5 mg/ml gentamicin (light grey bars) and 10 g/ml CdCl2 (dark grey bars). Vehicles controls were shown as white bars. High doses of test compounds were used to ensure the induction of cellular responses to these nephrotoxins. The protein concentrations of IL-6 and IL-8 in cell culture supernatants were measured by ELISA. Mean values were shown with s.d. (n=3). Asterisks indicate significant increases (P < 0.05) in comparison to vehicle controls. 63 4.3.2 Validation of the predictive performance with 41 test compounds Next, I proceeded to validate the predictive performance of the in vitro model by using 41 wellcharacterized test compounds (see Table 1). Most of these compounds were drugs that are routinely applied in clinical practice. Some compounds, such as CdCl2 and lindane, are wellcharacterized environmental toxicants. There is an abundance of human and animal in vivo and in vitro data available in literature for all of the 41 compounds selected here. I first selected compounds based on their classification in published compound lists [1, 38, 45, 49], which categorized them according to their nephrotoxicity in humans and as well as their toxic effects on different parts of the human kidney or the nephron. Extensive literature search based on PubMed, Google and the ChemIDplus Advanced database 3 was then performed to obtain further information on the test compounds and to confirm their classification. Details in humans on the nephrotoxic effects of all 41 compounds selected here as well as respective references are provided in appendix (see supplementary data, Table S1). 22 compounds were classified as nephrotoxicants which directly damage the PT in humans (group 1, Table 1, compounds 1-22). Some of the Group 1 compounds also have other adverse effects on the kidney in addition to such direct PT-specific damage. Group 2 consisted of 11 compounds (Table 1, compounds 23-33) that are nephrotoxic in humans, but do not directly injure PTCs. In addition, 8 non-nephrotoxic compounds were included (group 3, Table 1, compounds 34-41). The validation was performed by treating cultured HPTC with the 41 compounds and subsequently examining expression levels of IL-6 and IL-8 in these cells by 3 ChemIDplus Advanced database focuses on human clinical data. http://chem.sis.nlm.nih.gov/chemidplus/ProxyServlet?objectHandle=DBMaint&actionHandle=default&nextPage=js p/chemidheavy/ResultScreen.jsp&ROW_NUM=0&TXTSUPERLISTID=0015663271 64 qPCR. Three batches of HPTC derived from different donors were used to address inter-donor variability. For comparison, all experiments were also repeated with immortalized human (HK-2) and porcine (LLC-PK1) renal PT cell lines, which are standard cell lines commonly used for nephrotoxicology studies [3]. PT-Specific Nephrotoxicants Non-PT-Specific Nephrotoxicants Figure 12. Dose-response curves for expression of IL-6 and IL-8. HPTC 1 were treated with PT-specific nephrotoxicants (copper (II) chloride, cisplatin and paraquat; left-hand panels), non-PT-specific nephrotoxicants (ethylene glycol, valacyclovir and lindane; middle) or non-nephrotoxic compounds (acarbose, glycine and dexamethasone; right-hand panels) at concentrations indicated on the x-axis (logarithmic scale). The figure shows expression levels of IL-6 (grey curves) and IL-8 (black curves) relative to those of the vehicle controls (mean ± s.d., n = 3). In the case of cisplatin, marker gene expression levels could not be determined at the highest tested concentration due to massive cell death. Adapted from [8]. Reproduced by permission of The Royal Society of Chemistry. In all experiments, cells were exposed to the compounds for 16 hours after 3 days of cultivation at confluent densities. In preliminary tests, a wide range of concentrations covering 5 orders of 65 magnitude (0.01 1000 g/ml) of each compound were used. Due to the absence of any significant drug-induced responses at the two lowest concentrations (0.01 and 0.1 g/ml, data not shown), only concentrations of 1, 10, 100 and 1000 g/ml were tested for all compounds in all cell types. This choice of concentrations ensured that uninformative low concentrations below the current lower limit (1 g/ml) were eliminated from the tests. The concentration range was also capped at an upper limit (1000 g/ml), beyond which solubility issues with many compounds were observed. This would in turn undermine the accuracy of the test results. All qPCR results obtained were normalized to respective vehicle controls and presented as relative expression (fold changes) of IL-6 and IL-8. Fig. 12 shows the dose-response curves obtained with HPTC and three test drugs selected from each group of compounds (categorized as in Table 1). These results revealed that different compounds had different effects on the expression levels of IL-6 and IL-8: some compounds induced up-regulation of both marker genes, while other compounds induced only one or none of the two marker genes (Fig. 12). Detailed results on IL-6 and IL-8 expression levels for each cell batch/type and all 41 compounds at every concentration tested are listed in the Supplementary Tables S2-S11. Highlighted in these tables are the highest levels of IL-6 and IL-8 expression determined for each drug and cell batch/type within the range of drug concentrations tested. These highest expression levels are summarized in Tables 5 and 6. It is worth noting that expression of at least one of the two marker genes was often substantially increased when cells were treated with a PT-specific nephrotoxicant (group 1, Tables 5 and 6, Fig. 12), whereas cells generally remained irresponsive at all test concentrations when cells were treated with compounds from groups 2 and 3 (Tables 5 and 6, Fig. 12). 66 Table 5. Highest expression levels of IL-6 and IL-8 in HK-2 and LLC-PK1 cells. Cells were exposed to the 41 compounds (as listed and numbered in Table 1) at concentrations ranging from 1 g/ml and 1000 g/ml. This table lists the highest expression levels of both marker genes that were recorded at any concentration of a drug within this range. The values indicate the mean fold expression level with s.d. (n = 3) relative to vehicle controls. The highest expression levels shown here are highlighted in the Supplementary Tables S2-S5 (Appendix ii), where detailed expression levels measured at all drug concentrations were listed. Adapted from [8]. Reproduced by permission of The Royal Society of Chemistry. No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 HK-2 LLC-PK1 IL-6 IL-8 IL-6 IL-8 1.2 ± 0.1 1.3 ± 0.5 5.2 ± 1.0 2.7 ± 0.1 1.4 ± 0.2 1.1 ± 0.1 1.2 ± 0.0 2.3 ± 0.2 12.8 ± 1.7 5.1 ± 0.2 1.4 ± 0.2 8.4 ± 0.1 8.6 ± 1.3 18.8 ± 5.3 4.8 ± 0.3 8.9 ± 0.3 120.5 ± 26.1 30.5 ± 2.7 313.7 ± 31.4 839.4 ± 305.9 2.6 ± 0.3 1.9 ± 0.3 1.1 ± 0.0 1.6 ± 0.2 3.6 ± 0.8 14.3 ± 1.2 1.8 ± 0.1 10.1 ± 0.7 1.7 ± 0.1 2.5 ± 0.1 1.7 ± 0.1 1.2 ± 0.4 1.2 ± 0.1 1.7 ± 0.1 1.0 ± 0.1 1.7 ± 0.0 1.2 ± 0.1 1.4 ± 0.5 6.3 ± 0.3 20.3 ± 2.0 5.9 ± 0.5 2.0 ± 1.0 0.9 ± 0.1 6.9 ± 0.3 1.3 ± 0.0 3.0 ± 0.0 1.8 ± 0.1 4.3 ± 0.9 2.8 ± 0.8 3.7 ± 0.7 12.9 ± 7.2 12.2 ± 4.3 16.1 ± 1.1 1.2 ± 0.1 3.7 ± 0.2 37.0 ± 4.4 0.7 ± 0.1 1.5 ± 0.2 2.4 ± 0.0 2.5 ± 0.1 3.5 ± 0.5 1.9 ± 0.4 2.8 ± 0.0 1.6 ± 0.3 2.0 ± 0.4 2.1 ± 0.1 2.5 ± 0.2 16.3 ± 4.3 0.6 ± 0.0 0.6 ± 0.1 0.9 ± 0.0 1.4 ± 0.2 7.8 ± 0.4 2.8 ± 0.1 7.4 ± 1.0 301.3 ± 27.5 1.2 ± 0.1 1.6 ± 0.3 2.4 ± 0.6 4.9 ± 1.2 14.7 ± 1.5 1.7 ± 0.0 1.7 ± 0.5 2.6 ± 0.3 2.0 ± 0.3 3.9 ± 0.5 1.5 ± 0.1 4.0 ± 0.3 0.8 ± 0.0 0.7 ± 0.1 1.2 ± 0.2 0.9 ± 0.5 1.0 ± 0.1 1.6 ± 0.0 1.5 ± 0.6 1.3 ± 0.1 1.4 ± 0.0 0.9 ± 0.2 1.2 ± 0.2 4.7 ± 0.6 1.2 ± 0.1 0.5 ± 0.2 8.3 ± 0.7 1.4 ± 0.1 0.5 ± 0.0 1.6 ± 0.1 1.7 ± 0.3 1.1 ± 0.2 0.9 ± 0.1 1.3 ± 0.1 2.3 ± 0.4 2.1 ± 0.5 2.6 ± 0.1 1.0 ± 0.0 0.9 ± 0.2 1.1 ± 0.1 1.6 ± 0.3 1.6 ± 0.1 1.2 ± 0.1 16.1 ± 0.4 1.8 ± 0.3 2.9 ± 1.0 13.7 ± 1.2 1.0 ± 0.1 1.2 ± 0.1 2.6 ± 0.1 1.3 ± 0.0 2.1 ± 0.1 67 33 34 35 36 37 38 39 40 41 1.1 ± 0.1 1.2 ± 0.1 8.2 ± 2.6 9.1 ± 0.6 1.3 ± 0.1 1.3 ± 0.1 1.3 ± 0.1 1.5 ± 0.0 1.0 ± 0.1 0.8 ± 0.1 0.8 ± 0.0 0.3 ± 0.0 1.5 ± 0.3 1.2 ± 0.1 2.4 ± 0.6 10.2 ± 3.5 1.2 ± 0.1 1.0 ± 0.0 35.0 ± 4.3 82.4 ± 8.2 1.8 ± 0.0 0.9 ± 0.1 0.8 ± 0.0 2.4 ± 0.3 0.8 ± 0.0 7.7 ± 0.6 1.1 ± 0.0 0.9 ± 0.0 0.8 ± 0.0 1.0 ± 0.0 0.7 ± 0.1 2.6 ± 0.2 1.1 ± 0.0 164.4 ± 4.0 1.1 ± 0.0 11.0 ± 1.0 68 Table 6. Highest expression levels of IL-6 and IL-8 in HPTC. Three batches of HPTC (HPTC 1-3, derived from different donors) were exposed to the 41 test compounds at concentrations ranging from 1 g/ml to 1000 g/ml. The table shows the highest expression levels of IL-6 and IL-8 obtained at any given concentration of a test compound within this concentration range. The values indicate the mean fold expression level with s.d. (n = 3) relative to vehicle controls. The highest expression levels shown here are highlighted in the Supplementary Tables S6-S11 (Appendix ii), where detailed expression levels measured at all drug concentrations were listed. Adapted from [8]. Reproduced by permission of The Royal Society of Chemistry. No. HPTC 1 HPTC 2 HPTC 3 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 IL-6 16.9 ± 0.2 8.0 ± 0.9 38.9 ± 3.0 6.3 ± 0.7 8.5 ± 1.0 3.9 ± 0.1 3.6 ± 0.8 3.8 ± 0.6 1.7 ± 0.2 3.1 ± 0.7 3.0 ± 0.1 1.1 ± 0.3 6.6 ± 0.5 10.4 ± 2.9 1.7 ± 0.5 10.0 ± 1.3 3.5 ± 0.4 1.7 ± 0.2 24.6 ± 5.7 1.1 ± 0.2 3.9 ± 0.7 1.6 ± 0.1 IL-8 8.6 ± 1.3 8.9 ± 1.2 110.8 ± 39.0 1.5 ± 0.1 9.5 ± 0.6 2.3 ± 0.2 2.4 ± 0.2 20.1 ± 2.2 1.5 ± 0.2 16.3 ± 4.6 6.0 ± 0.1 11.9 ± 6.2 9.7 ± 2.5 7.5 ± 4.4 2.9 ± 1.7 2.2 ± 0.7 3.3 ± 0.1 3.8 ± 1.0 3.9 ± 1.3 5.6 ± 1.3 2.3 ± 0.7 4.3 ± 0.4 IL-6 1.7 ± 0.1 1.4 ± 0.0 3.6 ± 0.7 2.3 ± 0.3 79.5 ± 1.7 3.6 ± 0.3 4.8 ± 0.6 27.9 ± 0.8 1.3 ± 0.1 7.5 ± 0.0 1.6 ± 0.0 1.9 ± 0.3 12.9 ± 0.6 12.4 ± 1.4 3.6 ± 0.2 13.6 ± 1.0 8.6 ± 0.4 0.8 ± 0.0 37.6 ± 0.7 3.4 ± 0.7 1.3 ± 0.1 0.7 ± 0.0 IL-8 2.0 ± 0.1 1.5 ± 0.1 13.1 ± 3.0 3.6 ± 0.8 146.1 ± 3.1 9.2 ± 0.2 3.8 ± 0.9 32.7 ± 0.9 1.8 ± 0.4 13.2 ± 0.3 25.7 ± 3.8 22.5 ± 2.0 165.2 ± 14.7 119.0 ± 5.4 5.7 ± 0.2 8.3 ± 0.5 23.8 ± 2.9 2.7 ± 0.3 29.3 ± 4.3 67.4 ± 3.1 4.9 ± 0.1 1.0 ± 0.0 IL-6 1.6 ± 0.1 1.4 ± 0.1 5.3 ± 0.4 23.6 ± 2.8 80.4 ± 2.7 1.4 ± 0.1 12.0 ± 1.8 12.1 ± 0.4 2.9 ± 0.3 11.8 ± 0.5 31.4 ± 12.8 4.0 ± 0.1 10.8 ± 1.2 0.9 ± 0.2 2.2 ± 0.2 5.8 ± 2.5 2.3 ± 0.2 0.6 ± 0.2 38.1 ± 1.2 1.3 ± 0.1 2.3 ± 0.3 1.1 ± 0.1 IL-8 1.9 ± 0.3 2.0 ± 0.1 7.3 ± 2.7 35.5 ± 4.4 46.4 ± 1.2 2.4 ± 0.6 5.4 ± 0.4 21.9 ± 0.8 2.6 ± 0.2 9.7 ± 0.2 22.8 ± 4.0 9.6 ± 1.8 13.3 ± 1.4 9.9 ± 1.1 3.4 ± 0.5 3.5 ± 1.0 2.8 ± 0.4 10.1 ± 0.9 25.0 ± 0.8 17.8 ± 1.6 1.5 ± 0.1 2.0 ± 0.2 23 24 25 26 27 28 29 30 1.4 ± 0.2 1.2 ± 0.1 1.3 ± 0.0 2.6 ± 0.8 1.7 ± 0.4 0.7 ± 0.1 1.0 ± 0.3 1.0 ± 0.1 1.6 ± 0.3 1.3 ± 0.1 1.5 ± 0.1 3.1 ± 1.4 1.9 ± 0.5 0.5 ± 0.1 1.1 ± 0.4 1.1 ± 0.1 1.4 ± 0.2 0.8 ± 0.1 2.9 ± 0.2 16.1 ± 1.2 2.6 ± 0.2 17.4 ± 0.8 0.8 ± 0.0 1.8 ± 0.1 1.8 ± 0.4 0.9 ± 0.2 2.6 ± 0.1 4.3 ± 0.3 2.6 ± 0.2 20.5 ± 0.8 1.5 ± 0.1 2.0 ± 0.1 1.0 ± 0.4 6.2 ± 1.0 0.8 ± 0.1 2.9 ± 0.1 9.0 ± 0.6 1.1 ± 0.2 0.9 ± 0.2 1.0 ± 0.2 0.8 ± 0.2 3.6 ± 0.3 0.9 ± 0.0 1.5 ± 0.3 8.2 ± 0.7 1.3 ± 0.3 1.2 ± 0.0 1.2 ± 0.3 69 31 32 33 1.7 ± 0.4 1.3 ± 0.1 4.7 ± 0.6 1.3 ± 0.1 1.2 ± 0.0 5.3 ± 1.1 1.6 ± 0.3 1.3 ± 0.1 1.3 ± 0.1 1.2 ± 0.2 1.4 ± 0.1 1.6 ± 0.2 1.4 ± 0.2 1.0 ± 0.2 2.9 ± 0.1 1.3 ± 0.2 1.1 ± 0.2 3.2 ± 0.3 34 35 36 37 38 39 40 41 1.5 ± 0.1 1.7 ± 0.2 0.9 ± 0.1 1.2 ± 0.1 1.8 ± 0.0 3.4 ± 1.0 1.6 ± 0.2 1.0 ± 0.3 1.4 ± 0.1 2.0 ± 0.1 1.1 ± 0.1 1.5 ± 0.3 1.3 ± 0.1 1.8 ± 0.1 1.5 ± 0.1 3.6 ± 1.2 1.2 ± 0.1 2.7 ± 0.8 2.7 ± 0.1 1.1 ± 0.3 1.2 ± 0.2 3.1 ± 0.3 1.2 ± 0.0 1.0 ± 0.0 1.4 ± 0.1 1.3 ± 0.0 2.9 ± 0.0 1.4 ± 0.2 3.1 ± 0.2 24.0 ± 3.4 1.3 ± 0.0 1.6 ± 0.1 1.2 ± 0.3 1.0 ± 0.1 1.4 ± 0.2 0.9 ± 0.1 0.6 ± 0.1 3.4 ± 0.3 1.3 ± 0.1 128.5 ± 21.1 1.7 ± 0.4 1.2 ± 0.1 1.7 ± 0.3 1.3 ± 0.1 1.3 ± 0.2 5.1 ± 0.3 1.3 ± 0.1 38.2 ± 7.1 For further analysis of these gene expression results, the results were classified for each drug and cell type/batch as either positive or negative based on the highest expression levels of IL-6 and IL-8 obtained (Tables 5 and 6). A compound was defined as positive if the highest increase in gene expression (in terms of fold changes over its vehicle control, listed in Tables 5 and 6) of at least one of the two marker genes (IL-6 and IL-8) was equal to or higher than a given threshold value. Compounds that did not fulfill this condition were defined as negative compounds. However, the use of a stringent threshold (high fold change value) would very likely lead to markedly different interpretation of results as compared to a lenient threshold (low fold change). In order to address this issue and to select the most appropriate threshold, subsequent data analysis was performed based on a range of threshold levels from 0.3 to 4.0. The thresholding procedure is illustrated in Tables 7 and 8, using 2.0 and 3.5 as two threshold examples. Both tables display the highest expression levels for IL-6 and IL-8 obtained with HPTC 1 (derived from Table 6). These values were processed with a threshold value of 2.0 (Table 7) or 3.5 (Table 8). Wherever at least one of the two marker genes had a highest expression level equal to or higher than the threshold, these values were highlighted in the respective tables and the corresponding compound was designated as positive (indicated as “+”). 70 A compound with highest expression levels of both markers below the threshold level was not highlighted and was designated as negative (indicated as “-”). Based on the numbers of positive and negative compounds as classified at each threshold, the sensitivity and specificity values (%) were calculated as illustrated in Fig. 1 4 and indicated here in Tables 7 and 8. 4 Sensitivity = number of positive test results from group 1 compounds / total number of 22 group 1 compounds; specificity = number of negative test results from group 2 and 3 compounds / total number of 19 group 2 and 3 compounds. 71 Table 7. Example for the thresholding procedure at threshold = 2.0. Highest expression values (compare Table 6) equal to or above the threshold 2.0 were bolded. Compounds with at least one bolded value were designated as positive (+).Adapted from [8]. Reproduced by permission of The Royal Society of Chemistry. HPTC 1 IL-6 IL-8 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 16.9 ± 0.2 8.0 ± 0.9 38.9 ± 3.0 6.3 ± 0.7 8.5 ± 1.0 3.9 ± 0.1 3.6 ± 0.8 3.8 ± 0.6 1.7 ± 0.2 3.1 ± 0.7 3.0 ± 0.1 1.1 ± 0.3 6.6 ± 0.5 10.4 ± 2.9 1.7 ± 0.5 10.0 ± 1.3 3.5 ± 0.4 1.7 ± 0.2 24.6 ± 5.7 1.1 ± 0.2 3.9 ± 0.7 1.6 ± 0.1 1.4 ± 0.2 1.2 ± 0.1 1.3 ± 0.0 2.6 ± 0.8 1.7 ± 0.4 0.7 ± 0.1 1.0 ± 0.3 1.0 ± 0.1 1.7 ± 0.4 1.3 ± 0.1 4.7 ± 0.6 1.5 ± 0.1 1.7 ± 0.2 0.9 ± 0.1 1.2 ± 0.1 1.8 ± 0.0 3.4 ± 1.0 1.6 ± 0.2 1.0 ± 0.3 8.6 ± 1.3 8.9 ± 1.2 110.8 ± 39.0 1.5 ± 0.1 9.5 ± 0.6 2.3 ± 0.2 2.4 ± 0.2 20.1 ± 2.2 1.5 ± 0.2 16.3 ± 4.6 6.0 ± 0.1 11.9 ± 6.2 9.7 ± 2.5 7.5 ± 4.4 2.9 ± 1.7 2.2 ± 0.7 3.3 ± 0.1 3.8 ± 1.0 3.9 ± 1.3 5.6 ± 1.3 2.3 ± 0.7 4.3 ± 0.4 1.6 ± 0.3 1.3 ± 0.1 1.5 ± 0.1 3.1 ± 1.4 1.9 ± 0.5 0.5 ± 0.1 1.1 ± 0.3 1.1 ± 0.1 1.3 ± 0.1 1.2 ± 0.0 5.3 ± 1.1 1.4 ± 0.1 2.0 ± 0.1 1.1 ± 0.1 1.5 ± 0.3 1.3 ± 0.1 1.8 ± 0.1 1.5 ± 0.1 3.6 ± 1.2 Threshold = 2.0 +/Sensitivity / Test Specificity + + + + + + + + + Sensitivity = + 21 / 22 = 96% + + + + + + + + + + + + Specificity = 14 / 19 = 74% + + + + 72 Table 8. Example for the thresholding procedure at threshold = 3.5. Highest expression values (compare Table 6) equal to or above the threshold 3.5 were bolded. Compounds with at least one bolded value were designated as positive (+).Adapted from [8]. Reproduced by permission of The Royal Society of Chemistry. HPTC 1 IL-6 IL-8 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 16.9 ± 0.2 8.0 ± 0.9 38.9 ± 3.0 6.3 ± 0.7 8.5 ± 1.0 3.9 ± 0.1 3.6 ± 0.8 3.8 ± 0.6 1.7 ± 0.2 3.1 ± 0.7 3.0 ± 0.1 1.1 ± 0.3 6.6 ± 0.5 10.4 ± 2.9 1.7 ± 0.5 10.0 ± 1.3 3.5 ± 0.4 1.7 ± 0.2 24.6 ± 5.7 1.1 ± 0.2 3.9 ± 0.7 1.6 ± 0.1 1.4 ± 0.2 1.2 ± 0.1 1.3 ± 0.0 2.6 ± 0.8 1.7 ± 0.4 0.7 ± 0.1 1.0 ± 0.3 1.0 ± 0.1 1.7 ± 0.4 1.3 ± 0.1 4.7 ± 0.6 1.5 ± 0.1 1.7 ± 0.2 0.9 ± 0.1 1.2 ± 0.1 1.8 ± 0.0 3.4 ± 1.0 1.6 ± 0.2 1.0 ± 0.3 8.6 ± 1.3 8.9 ± 1.2 110.8 ± 39.0 1.5 ± 0.1 9.5 ± 0.6 2.3 ± 0.2 2.4 ± 0.2 20.1 ± 2.2 1.5 ± 0.2 16.3 ± 4.6 6.0 ± 0.1 11.9 ± 6.2 9.7 ± 2.5 7.5 ± 4.4 2.9 ± 1.7 2.2 ± 0.7 3.3 ± 0.1 3.8 ± 1.0 3.9 ± 1.3 5.6 ± 1.3 2.3 ± 0.7 4.3 ± 0.4 1.6 ± 0.3 1.3 ± 0.1 1.5 ± 0.1 3.1 ± 1.4 1.9 ± 0.5 0.5 ± 0.1 1.1 ± 0.3 1.1 ± 0.1 1.3 ± 0.1 1.2 ± 0.0 5.3 ± 1.1 1.4 ± 0.1 2.0 ± 0.1 1.1 ± 0.1 1.5 ± 0.3 1.3 ± 0.1 1.8 ± 0.1 1.5 ± 0.1 3.6 ± 1.2 Threshold = 3.5 +/Sensitivity / Test Specificity + + + + + + + + + Sensitivity = + 20/22 = 91% + + + + + + + + + + Specificity = 17 /19 = 90% + + 73 Similarly, the sensitivity and specificity values based on the highest mRNA expression levels summarized in Tables 5 and 6 were calculated for every cell type and batch using 7 different threshold levels ranging from 0.3 to 4.0. The results are summarized in Table 9 and are graphically displayed in Fig. 13. The overall concordance values with clinical data 5 were also calculated and plotted in Fig. 13. 5 Concordance with clinical data = sum of number of positive test results from group 1 compounds (true positives) and number of negative test results from group 2 and 3 compounds (true negatives) / total number of 41 test compounds 74 Table 9: Determination of true positives (TP), true negatives (TN), sensitivity and specificity values. TP were defined as PT-specific nephrotoxicants (group 1; 22 drugs) that were correctly identified as positives by our assay. TNs were defined as non-PT-damaging nephrotoxicants and non-nephrotoxic drugs (group 2 and 3; 19 drugs) that were correctly identified as negatives in our assay. TP and TN were counted at the threshold levels indicated ranging from 0.3 to 4.0. Sensitivity and specificity values were calculated from these TP and TN numbers as described in Fig. 1. These percentages obtained with respect to sensitivity and specificity are displayed in brackets after the respective numbers of TP and TN. Values shown were derived from all cell types and batches based on the results from all 41 compounds. The percentage values of sensitivity and specificity are graphically displayed in Fig. 13. Adapted from [8]. Reproduced by permission of The Royal Society of Chemistry. Thresholds 0.3 1.5 2.0 2.5 3.0 3.5 4.0 HK-2 LLC-PK1 HPTC 1 HPTC 2 HPTC 3 TP 22 (100%) 22 (100%) TN 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) TP 18 (82%) 21 (96%) 22 (100%) 21 (96%) 22 (100%) TN 9 (47%) 7 (37%) 6 (32%) 5 (26%) 10 (53%) TP 15 (68%) 18 (82%) 21 (96%) 19 (86%) 21 (96%) TN 15 (79%) 9 (47%) 14 (74%) 10 (53%) 13 (68%) TP 14 (64%) 17 (77%) 21 (96%) 18 (82%) 17 (77%) TN 15 (79%) 11 (58%) 16 (84%) 11 (58%) 13 (68%) TP 12 (55%) 14 (64%) 20 (91%) 17 (77%) 15 (68%) TN 15 (79%) 14 (74%) 16 (84%) 15 (79%) 14 (74%) TP 11 (50%) 14 (64%) 20 (91%) 17 (77%) 14 (64%) TN 15 (79%) 14 (74%) 17 (90%) 16 (84%) 15 (79%) TP 8 (36%) 14 (64%) 15 (68%) 16 (73%) 14 (64%) TN 15 (79%) 14 (74%) 18 (95%) 16 (84%) 15 (79%) 22 (100%) 22 (100%) 22 (100%) 75 The results from Fig. 13 revealed that a threshold value of 3.5 was most appropriate for two of the HPTC batches (HPTC 1 and 2). At this threshold level, high sensitivity, specificity and overall concordance with clinical data (80% ~ 90%) were obtained for both HPTC batches. However, when the same threshold value (3.5) was applied to HPTC 3, the sensitivity and overall concordance were 64% and 71% respectively, despite the consistently high sensitivity of 80%. Thus, the results displayed some inter-donor variability. Figure 13. Sensitivity, specificity and overall concordance with clinical data in three batches of HPTCs, HK-2 and LLC-PK1 cells. The figure graphically displays the sensitivity and specificity values shown in Table 9. The overall concordance values were also calculated at all thresholds (x-axis) from 0.3 to 4.0. Dotted lines indicate 80% for comparison. Adapted from [8]. Reproduced by permission of The Royal Society of Chemistry. 76 On the other hand, HK-2 and LLC-PK1 cells had different optimal threshold levels from HPTC, and in general, the cell lines had lower predictivity than the primary cells (Fig. 13). For example, in LLC-PK1 cells sensitivity, specificity and concordance values were all between 64% ~ 74% at the optimal threshold levels (> 3.0). These values were substantially lower than those obtained with HPTCs. Further analyses on the overall predictivity of each cell type/batch were subsequently performed by plotting the receiver operating characteristic (ROC) curves and subsequently comparing the area under curve (AUC) values. The ROC curves for each cell type/batch tested are displayed in Fig. 14 for either IL-6 or IL-8 or the combination of both markers. The corresponding AUC values are summarized in Table 10. By comparing the mean and median AUC values of the three HPTC batches (or the individual AUC values of each HPTC batch) with those of HK-2 and LLC-PK1 cells, it is again clear that the predictivity was generally higher when HPTC were used. These results also showed that the use of a combination of both IL-6 and IL-8 could only slightly improve the results as compared to when only IL-8 was used. The AUC values (Table 10) obtained with the marker combination ranged from 0.71 (HK-2) to 0.94 (HPTC 1), whereas the use of IL-8 only gave AUC values ranging from 0.68 (HK-2) to 0.90 (HPTC 1). 77 Figure 14. ROC curves for HPTC, HK-2 and LLC-PK1 cells. For each cell type/batch the ROC curves were plotted for each single marker (gray graphs) and the combination of both markers (black graphs). The AUC values are summarized in Table 10. For comparison, panel F simultaneously displays the ROC curves (for combination of both markers) obtained with all the cell types/batches tested. Adapted from [8]. Reproduced by permission of The Royal Society of Chemistry. 78 Table 10. AUC values. The table provides the AUC values of the ROC curves (Fig. 14) for every cell batch/type tested. For HPTC also the mean and median values are shown. AUC values were determined separately for either IL-6 or IL-8 or the combination of these two markers. AUC values > 0.5 represent a predictive model that is better than chance. Adapted from [8]. Reproduced by permission of The Royal Society of Chemistry. Cell Type HPTC 1 HPTC 2 HPTC 3 HPTC mean HPTC median HK-2 LLC-PK1 IL-6/IL-8 0.94 0.81 0.82 0.85 0.82 0.71 0.73 AUC IL-6 0.85 0.72 0.71 0.76 0.72 0.74 0.65 IL-8 0.90 0.80 0.84 0.85 0.84 0.68 0.72 79 Table 11 lists the most important performance metrics (balanced accuracy, sensitivity, specificity, PPV, NPV and AUC values; see Figure 1 for definitions). All values were calculated based on the combined expression data of both IL-6 and IL-8. The mean PPV for HPTC was 0.85, suggesting that 85% of the compounds predicted as positive were indeed PT-specific nephrotoxicants. The PPV values for HK-2 and LLC-PK1 cells were 0.73 and 0.74, respectively. The mean NPV of HPTC was 0.79, indicating that 79% of compounds predicted as negative were non-nephrotoxic or damage the kidney via mechanisms other than direct toxicity towards PTCs. In this case, the NPV values for the cell lines were 0.6 (HK-2) and 0.67 (LLC-PK1). Also, the other values showed that throughout a higher predictivity was obtained with HPTCs, in comparison to the two cell lines. Table 11. Performance metrics. The table summarizes the values for the following performance metrics: balanced accuracy (defined as average between sensitivity and specificity), sensitivity, specificity, PPV, NPV and AUC. In case of sensitivity, specificity, PPV and NPV the values obtained at a threshold value of 3.5 (see Fig. 13) are displayed. With respect to the AUC values the results obtained with a combination of both markers are provided. These values are identical with those in Table 10 and are displayed here again for comparison. Adapted from [8]. Reproduced by permission of The Royal Society of Chemistry. Cell Type HPTC 1 HPTC 2 HPTC 3 HPTC mean HPTC median HK-2 LLC-PK1 Balanced Accuracy 0.90 0.81 0.71 0.81 0.81 0.65 0.69 Sensitivity Specificity PPV NPV AUC 0.91 0.77 0.64 0.77 0.77 0.50 0.64 0.90 0.84 0.79 0.84 0.84 0.79 0.74 0.91 0.85 0.78 0.85 0.85 0.73 0.74 0.94 0.76 0.68 0.79 0.76 0.60 0.67 0.94 0.81 0.82 0.85 0.82 0.71 0.73 80 4.3.3 Comparison of endpoints Next, we compared the predictive performance when either IL-6/IL-8 expression or cell death was used as endpoints. High content screening (HCS) was used here to determine cell numbers after cells were exposed to the same set of 41 compounds in the same way in previous experiments. As a large number of test compounds were used to treat various cell types at different concentrations, the HCS-based method was more appropriate in this case for determining cell numbers due to its high throughput. On the contrary, the neutral red uptake (NRU) assay employed for determining cell viability in Section 4.2 was more suitable for tests of smaller scales and therefore not used here. Reduction in cell numbers indicates cell death. Here in cases where cell numbers were decreased by more than 50% (normalized to respective vehicle controls) within the tested range of concentrations, IC50 values were calculated and summarized in Table 12. For compounds that did not cause massive cell death and more than 50% of cells survived up to the highest tested concentration of 1000 g/ml, the IC50 values were denoted as “>1000”. Table 12 shows that when IL-6/IL-8 expression was used as the endpoint, positive results were observed in 91% (20/22) of the PT-specific nephrotoxicants in HPTC 1, indicating high sensitivity. In contrast, it was possible to calculate the IC50 values for only 42% (8/19) of the tested group 1 compounds, as in all other cases cell viability remained > 50%. Even if all of such cases (where > 50% cell death has occurred) were classified as positives, the sensitivity would still be < 0.5 when cell death was used as the endpoint. Similar results were obtained with HK-2 and LLC-PK1 cells, where substantial cell death was observed in only 43% (6/14) and 53% (8/15) of group 1 compounds tested. 81 These results indicate that endpoints measuring general cytotoxicity are not necessarily useful for organ-specific assays. Many widely used toxicity assays are based on cell death, and determine either cell numbers (such as the neutral red uptake assay) or measure metabolic activity as a surrogate endpoint (MTS assay). Table 12. Comparison of drug effects on IL-6/IL-8 expression and cell numbers. HPTC 1, HK-2 and LLC-PK1 cells were exposed to the 41 test compounds. Data on IL-6/IL-8 expression were based on previous results (Tables 5 and 6). A result was defined as positive (+) when expression of at least one marker showed at any concentration an increase of 3.5-fold or above (for HPTC 1, compare Table 8). If marker expression values remained below 3.5-fold the result was classified as negative (-). IC50 values were calculated based on cell numbers determined by HCS. A value of >1000 g/ml was assigned if cell viability was > 50% up to the highest concentration of a compound (1000 g/ml). Cell numbers were not determined (ND) in some cases. Adapted from [8]. Reproduced by permission of The Royal Society of Chemistry. No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 HPTC 1 IC50 IL-6/ IL-8 (g/ml) + >1000 + >1000 + 707 + >1000 + >1000 + 47 + ND + ND >1000 + >1000 + 21 + >1000 + 4 + 147.0 >1000 + 96 + >1000 + 23 + 45 HK-2 IC50 IL-6/ IL-8 (g/ml) >1000 >1000 + 632 + ND + ND 94 + >1000 ND >1000 >1000 + 7 >1000 + ND + 116 ND + ND ND 14 + 44 LLC-PK1 IC50 IL-6/ IL-8 (g/ml) + >1000 >1000 + 795 + >1000 + ND 38 + 469 ND >1000 + 69 + 19 + >1000 + 9 + 79 >1000 ND + >1000 ND + 45 82 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 + + + + + >1000 ND >1000 >1000 >1000 >1000 742 >1000 >1000 >1000 >1000 >1000 >1000 >1000 >1000 >1000 >1000 >1000 >1000 >1000 >1000 >1000 + + + + + + >1000 ND >1000 >1000 >1000 >1000 678 >1000 >1000 >1000 >1000 >1000 ND >1000 >1000 >1000 >1000 >1000 >1000 >1000 >1000 >1000 + + + + + + + ND ND ND >1000 >1000 >1000 945 >1000 >1000 >1000 >1000 >1000 >1000 >1000 >1000 >1000 >1000 >1000 71 >1000 >1000 >1000 83 In summary, an in vitro model for the prediction of drug-induced nephrotoxicity in humans has been developed and the results were published in [8]. The model is primarily based on HPTCs seeded on uncoated TCPS surfaces. Expression levels of IL-6 and IL-8 were used as endpoints and the model was validated with 41 test compounds. The predictivity was found to be > 80% when HPTCs were used, and this value was significantly higher than those obtained with immortalized cell lines (Table 9). This model would allow highly accurate predictions on the PT toxicity of drugs in an early pre-clinical stage, which is currently not possible [45]. However, one major limitation of this model is the difficulty in obtaining large numbers of human primary renal cells. Healthy human kidneys are usually used for transplantation, and large amounts of healthy human kidney tissues are rarely available for PT cell isolation. Despite such high predictivity, large-scale screening of new drugs using HPTCs inevitably involves high costs due to scarce cell sources. Other issues associated with the use of primary cells include limited proliferative capacity [63, 84], functional changes during passaging [85], inter-donor variability [6, 8, 72, 86] as well as trans- and de-differentiation in vitro [65, 87]. In view of these problems, the use of HPTC-like cells differentiated from human stem cells under controlled conditions could provide a potential solution. Our group has been developing protocols to differentiate human pluripotent stem cells into HPTC-like cells [9]. An application of such stem cell-derived HPTC-like cells in this in vitro model and subsequent validation results will be described in detail in the next section of this thesis. 84 4.4 Application of stem cell-derived HPTC-like cells for the prediction of drug-induced nephrotoxicity in humans 4.4.1 Predictive performance of hESC-derived HPTC-like cells As mentioned earlier, stem cell-based approaches can potentially address many issues associated with the use of primary cells and immortalized cell lines. In a previous study, our lab was involved in developing an approach to differentiate human embryonic stem cells (hESCs) into HPTC-like cells [9]. Based on the findings that their morphological and functional features, as well as protein and gene expression patterns were similar to those of HPTCs, I tested these HPTC-like cells in the in vitro model described in Section 4.3. Subsequently the predictivity was determined by using the same 41 test compounds (as listed in Table 1). Here, HUES 7 cells were differentiated into HPTC-like cells as described in [9]. The HPTC-like cells were also characterized by examining marker expression to ensure that proper cell phenotypes were obtained. Cell differentiation was performed by Dr. Wei Seong Toh (IBN) and characterization of HPTC-like cells by Dr. Karthikeyan Kandasamy (IBN). Expression of 18 epithelial and HPTC-specific markers was assessed in HPTC-like cells and HPTC (HPTC 1, which had been extensively characterized [8, 9]). Among the 18 markers tested, 11 markers were expressed at similar or significantly higher levels in HPTC-like cells as compared to HPTCs [10]. These included PT-specific markers such as aquaporin (AQP) 1, glucose transporter (GLUT) 5 and N-cadherin. HPTC-like cells were harvested after 20 days of differentiation and cryopreserved before further use (the characterizations described above had been performed on cryopreserved and reseeded cells). For toxicity testing, cryopreserved cells were thawed and seeded at confluent density into 85 multi-well plates. Essentially, the work was performed in the same way as before with HPTCs (see Section 4.3). After seeding, cells were cultivated for 3 days, and were then exposed for 16 h to the 41 test compounds. All compounds were tested at concentrations of 1 μg/ml, 10 μg/ml, 100 μg/ml and 1000 μg/ml. The reasoning for the choice of concentrations was discussed earlier in Section 4.3. All results (IL-6 and IL-8 expression) were normalized to, and expressed as fold changes of the respective vehicle controls. Detailed results on IL-6 and IL-8 expression for each drug at every concentration tested are listed in the Supplementary Data (Tables S12 and S13), where the highest levels of expression determined for each compound within the concentration range tested were highlighted. These highlighted values were summarized in Table 13. The definition of a positive result was the same as previously described: a results was classified as positive if the highest increase in gene expression of at least one of the two markers (IL-6/IL-8) was equal to or higher than a threshold value. In the case of hESC-derived HPTC-like cells, the analysis on predictive performance was carried out over a range of cut-off values from 0.1 to 5.0. 86 Table 13. Highest expression levels of IL-6 and IL-8 in hESC-derived HPTC-like cells. Cells were exposed to the 41 compounds (same as listed and numbered in Table 1) at concentrations ranging from 1 g/ml and 1000 g/ml. This table lists the highest expression levels of both marker genes that were recorded at any concentration of a drug within this range. The values indicate the mean fold expression level with s.d. (n = 3) relative to vehicle controls. The highest expression levels shown here are highlighted in Appendix ii, Supplementary Data, Tables S12-S13, where detailed expression levels measured at all drug concentrations were listed. Adapted with permision from [10]. Copyright 2014 American Chemical Society. No. Compound 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 IL-6 IL-8 Gentamicin 1.9 ± 0.1 1.0 ± 0.1 Tobramycin 1.5 ± 0.3 0.4 ± 0.0 Rifampicin 14.9 ± 5.3 87.5 ± 38.8 Tetracycline 16.5 ± 5.4 29.0 ± 10.4 Puromycin 1252.9 ± 126.5 2765.7 ± 23.6 Cephalosporin C 6.8 ± 0.4 3.2 ± 0.5 5-Fluorouracil 2.1 ± 0.3 4.3 ± 0.2 Cisplatin 2.1 ± 0.0 2.3 ± 0.2 Ifosfamide 1.2 ± 0.1 0.9 ± 0.1 Paraquat 8.4 ± 1.5 1.8 ± 0.5 Arsenic(III) oxide 8.7 ± 0.2 0.5 ± 0.0 Bismuth(III) oxide 42.7 ± 2.5 36.1 ± 1.2 Cadmium(II) chloride 7.6 ± 0.4 7.4 ± 0.5 Copper(II) chloride 10.2 ± 0.6 7.9 ± 0.4 Germanium(IV) oxide 1.0 ± 0.1 1.1 ± 0.1 Gold(I) chloride 46.4 ± 5.6 71.6 ± 26.2 Lead acetate 27.8 ± 3.0 3.9 ± 0.1 Potassium dichromate 1.5 ± 0.3 0.9 ± 0.1 Tacrolimus 45.2 ± 13.8 549.3 ± 46.2 Cyclosporin A 36.1 ± 10.4 242.2 ± 4.8 Citrinin 6.0 ± 1.4 3.5 ± 1.8 Tenofovir 1.5 ± 0.2 3.0 ± 0.2 Vancomycin 2.4 ± 0.2 3.5 ± 0.4 Phenacetin 0.7 ± 0.3 1.2 ± 0.6 Acetaminophen 0.9 ± 0.1 1.1 ± 0.2 Ibuprofen 0.7 ± 0.1 0.4 ± 0.0 Furosemide 1.6 ± 0.0 7.4 ± 1.3 Lithium Chloride 1.1 ± 0.1 1.0 ± 0.3 Lindane 0.9 ± 0.4 0.1 ± 0.0 87 30 31 32 33 34 35 36 37 38 39 40 41 Ethylene glycol Valacyclovir Lincomycin Ciprofloxacin Ribavirin Glycine Dexamethasone Melatonin Levodopa (DOPA) Triiodothyronine Acarbose Atorvastatin 1.2 ± 0.0 3.2 ± 0.1 1.3 ± 0.1 54.8 ± 2.9 1.1 ± 0.1 1.1 ± 0.2 1.0 ± 0.1 1.0 ± 0.1 3.8 ± 0.2 1.8 ± 0.1 1.2 ± 0.2 12.7 ± 0.8 1.1 ± 0.1 2.1 ± 0.1 1.0 ± 0.1 21.3 ± 0.9 1.0 ± 0.1 0.8 ± 0.0 1.1 ± 0.0 2.2 ± 0.5 0.9 ± 0.0 1.6 ± 0.7 1.1 ± 0.1 6.6 ± 1.4 The analysis of the data was performed in a similar procedure as described in 4.3. True positives (TP), true negatives (TN), sensitivity and specificity were defined as shown in Fig. 1. Results for all cut-off values in the range of 0.1 ~ 5.0 are listed in Table 14 and are graphically illustrated in Fig. 15 a, which also shows the overall concordance with human clinical data. The results show that a cut-off value of 4.0 was optimal for hESC-derived HPTC-like cells. At this value the highest sensitivity (68%) and specificity (84%) could be achieved (Table 14, Fig. 15 a). Table 14. Determination of TP, TN, sensitivity and specificity in hESC-derived HPTC-like cells. Adapted with permission from [10]. Copyright 2014 American Chemical Society. Cut-Off TP Sensitivity TN Specificity 0.1 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 22 22 20 17 16 16 15 15 14 14 100% 100% 91% 77% 73% 73% 68% 68% 64% 64% 0 2 11 12 13 13 14 16 16 16 0% 11% 58% 63% 68% 68% 74% 84% 84% 84% 88 Other major performance metrics were therefore also calculated at the cut-off value of 4.0. Balanced accuracy, PPV and NPV were calculated (see definitions in Fig. 1) and are displayed in Table 15. The performance metrics of cell types discussed in the previous section 4.3 (HPTCs, HK-2 and LLC-PK1 cells, as displayed in Table 11) are also included in Table 15 for comparison. hESC-derived HPTC-like cells displayed similar specificity (84%) and PPV (83%) as HPTCs (mean; 84% and 85%, respectively). With respect to all other performance metrics listed, the HPTC-like cells performed at slightly lower levels than HPTCs. However, it is worth noting that all values were still substantially higher than those obtained with the human and animal PT-derived cell lines (HK-2 and LLC-PK1; at their respective optimal cut-off values), suggesting that hESC-derived HPTC-like cells can potentially serve as a viable alternative to the commonly used cell lines in in vitro prediction of drug-induced PT toxicity. To further investigate the predictive performance of the hESC-derived HPTC-like cells, I also plotted the ROC curves (Fig. 15 b) and calculated the AUC values (Table 15). The AUC values suggested that when both marker genes were used as the endpoint, HPTC-like cells (0.80, or 80% in Fig. 15 b and c) had slightly lower predictivity than HPTC (mean = 0.85, Table 15) but performed better than the PT-derived cell lines (0.71 and 0.73). In addition, predictivity appeared to be better when only IL-6 expression was used as endpoint (AUC value of 82%; Fig. 15 b), as compared to when IL-6 and IL-8 expression was used simultaneously (80%). 89 Figure 15. Sensitivity, specificity, overall concordance and ROC curves for hESC-derived HPTC-like cells. (a) Graphical display of the sensitivity and specificity values shown in Table 15. The figure also shows the overall concordance of the results obtained in Table 13 with the PT toxicity of the compounds in humans as reported in literature (classification as listed in Table 1). Cut-off values (x-axis) ranged from 0.1 – 5.0. (b) The ROC curves were plotted using sensitivity and specificity values at all cut-off values indicated in Table 14, based on data listed in Table 13. ROC curves were plotted and AUC values calculated with respect to each single marker gene (gray curves) or the combination of both markers (black). The respective AUC values are indicated in the legend as percentages. An AUC value of 50% suggests random chance and an AUC value of 100% indicates perfect predictivity. (c) The ROC curves were plotted based on the results obtained with ATP depletion assay in both HPTC-like cells and HPTC (dashed and dotted gray curves) and GSH depletion assays (solid gray graph). The ROC curve for the combinatorial endpoint of IL-6 and IL-8 is also displayed here for comparison (black curve; identical to that in panel b). The respective AUC values (%) are indicated in the legend. Adapted with permission from [10]. Copyright 2014 American Chemical Society. 90 Table 15. Summary of results obtained with different endpoints and cell types. Asterisks indicate data that had been listed earlier in Table 11. Cell death data were derived from Table 12. Adapted with permission from [10] (Supplementary Information) with addition of data on hiPSC-derived HPTC-like cells. Copyright 2014 American Chemical Society. Endpoints Cell Type Sensitivity Specificity Balanced Accuracy PPV NPV AUC 0.91 0.90 0.90 0.91 0.94 0.94 HPTC 2* 0.77 0.84 0.81 0.85 0.76 0.81 HPTC 3* 0.64 0.79 0.71 0.78 0.68 0.82 HPTC mean* 0.77 0.84 0.81 0.85 0.79 0.85 HPTC median* 0.77 0.84 0.81 0.85 0.76 0.82 HK-2* 0.50 0.79 0.65 0.73 0.60 0.71 LLC-PK1* hESC-derived HPTC-like hiPSC-derived HPTC-like HPTC-like 0.64 0.74 0.69 0.74 0.67 0.73 0.68 0.84 0.76 0.83 0.70 0.80 0.77 0.74 0.75 0.77 0.74 0.76 0.48 0.79 0.63 0.71 0.58 0.65 HPTC 1 0.50 0.74 0.62 0.69 0.56 0.65 GSH Depletion HPTC 1 0.45 0.74 0.60 0.67 0.54 0.60 LDH Leakage HPTC 1 0.64 0.58 0.61 0.64 0.58 - Cell Death HPTC 1 0.42 0.95 0.69 0.89 0.62 - IL-6/IL-8 Expression HPTC 1* ATP Depletion 91 4.4.2 Comparison to standard toxicity assays In order to compare the predictive performance of the IL-6/IL-8-based assay with widely used standard toxicity assays, ATP depletion assays were performed with HPTC and HPTC-like cells using the same set of 41 test compounds at the same concentrations. ATP depletion is widely used for studying organ-specific toxicity, including nephrotoxicity [45, 138]. Examples of results obtained with some of the compounds are shown in Fig. 16 (upper row). HPTC 1, which was the best performing HPTC batch with the IL-6/IL-8-based assay, was used at the same passage numbers (P4 and P5) in the ATP depletion assay. These results showed that when HPTC 1 were treated with PT-specific nephrotoxicants such as copper (II) chloride, there was in some cases a drastic decrease in the percentage cellular ATP content with increasing compound concentration. When CuCl2 was applied at concentrations ≥ 500 g/ml, complete depletion of cellular ATP was observed. In contrast, when cells were treated with a non-PT-specific nephrotoxicants or a nonnephrotoxic compound, such as lithium chloride and glycine, cellular ATP content remained often largely unaffected in the tested concentration range. IC50 values of all compounds were subsequently calculated and are displayed in Table 16. In cases where the IC50 values were below the highest tested concentration (1000 μg/ml), the compounds were defined as positives. Based on the number of positive compounds, sensitivity and specificity values were calculated and displayed at the bottom of Table 16. Table 16 shows that in the ATP depletion assay, HPTC and HPTC-like cells gave rise to comparable results. Most compounds which gave positive results (IC50 values < 1000 μg/ml) induced substantial ATP depletion in both cell types, and their overall sensitivity and specificity values were similar despite the differences in the exact magnitudes of the IC50 values (see bottom of Table 16). In particular, the IC50 values for compounds 5 and 6 (puromycin and 92 cephalosporin C) in HPTC-like cells were ~2-3 orders of magnitude lower than in HPTC. This observation is consistent with the fact that puromycin also induced exceptionally high levels of IL-6 and IL-8 induction in HPTC-like cells (Table 13), suggesting that this compound is indeed highly toxic for the HPTC-like cells. Figure 16. Dose-response curves obtained with the ATP depletion assay (upper row) and GSH depletion assay (lower row). HPTC 1 were treated with PT-specific nephrotoxicants (e.g. copper (II) chloride, lefthand column), non-PT-specific nephrotoxicants (e.g. lithium chloride, middle column) or nonnephrotoxic compounds (e.g. glycine, right-hand column) at concentrations indicated on the x-axis. The figures show percentage changes (mean ± s. d., n = 3) in cellular ATP or GSH content after drug treatment, as compared to the vehicle controls, which were set to 100%. 93 Table 16. Comparison of different assays performed with HPTC-like cells and HPTC 1. All assays were performed with the 41 compounds listed in Table 1 (numbered in the left column). Positive (+) and negative (-) results obtained with HPTC-like cells in the IL-6/IL-8-based assay at a cut-off value of 4.0 are listed. The ATP depletion assay was performed with HPTC-like cells and HPTC 1 and IC50 values (g/ml) are listed. In cases where ATP levels remained above 50% of vehicle control up to the highest concentration tested (1000 g/ml), an IC50 value of > 1000 g/ml was indicated. The GSH depletion assay and LDH leakage assay were performed with HPTC 1. IC50 values are listed for the GSH assay. LDH leakage was presented as percentages of vehicle controls. Significant increases were marked with asterisks. All values represent the mean ± s. d. (n = 3). Sensitivity and specificity values are listed at the bottom for each assay. All results were classified as positive if the IC50 values were below 1000 g/ml (ATP depletion assay and GSH depletion assay) or when there was a significant increase in LDH leakage. Adapted with permission from [10]. Copyright 2014 American Chemical Society. Compound 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 HPTC-like ATP IL-6/IL-8 Depletion > 1000 > 1000 + 981 ± 78 + > 1000 + 1±0 + 1±0 + > 1000 679 ± 16 > 1000 + 551 ± 129 + > 1000 + > 1000 + 82 ± 5 + 555 ± 53 > 1000 + 229 ± 122 + > 1000 4±1 + 654 ± 44 + > 1000 + ND > 1000 > 1000 > 1000 > 1000 618 ± 6 + > 1000 > 1000 HPTC ATP Depletion > 1000 > 1000 856 ± 22 > 1000 13 ± 1 699 ± 243 > 1000 889 ± 21 > 1000 875 ± 111 > 1000 > 1000 14 ± 5 315 ± 36 > 1000 232 ± 4 > 1000 9±1 49 ± 5 371 ± 114 > 1000 > 1000 > 1000 > 1000 > 1000 858 ± 13 > 1000 > 1000 GSH Depletion LDH Leakage > 1000 > 1000 769 ± 133 > 1000 1±0 837 ± 48 > 1000 742 ± 17 > 1000 > 1000 > 1000 944 ± 133 > 1000 708 ± 4 > 1000 869 ± 35 > 1000 81 ± 2 54 ± 22 > 1000 634 ± 100 > 1000 > 1000 > 1000 755 ± 8 239 ± 125 > 1000 > 1000 105% ± 13% 110% ± 4%* 128% ± 4%* 107% ± 2%* 106% ± 1%* 117% ± 5%* 146% ± 7%* 104% ± 4% 111% ± 5%* 111% ± 3%* 115% ± 6%* 108% ± 4%* 146% ± 6%* 100% ± 3% 95% ± 2% 96% ± 1% 109% ± 3%* 106% ± 22% 136% ± 29% 105% ± 9% 109% ± 2%* 118% ± 7%* 102% ± 3% 97% ± 4% 90% ± 3% 102% ± 29% 98% ± 4% 95% ± 5% 94 29 30 31 32 33 34 35 36 37 38 39 40 41 Sensitivity Specificity + + 68% 84% > 1000 > 1000 > 1000 > 1000 > 1000 667 ± 80 > 1000 > 1000 496 ± 59 216 ± 111 > 1000 > 1000 > 1000 48% 79% > 1000 > 1000 > 1000 > 1000 > 1000 619 ± 330 > 1000 > 1000 > 1000 94 ± 64 940 ± 3 > 1000 907 ± 49 50% 74% > 1000 > 1000 > 1000 > 1000 733 ± 316 > 1000 > 1000 > 1000 > 1000 814 ± 12 541 ± 112 > 1000 > 1000 45% 74% 110% ± 5%* 107% ± 4%* 109% ± 3%* 108% ± 5% 114% ± 13% 110% ± 3%* 115% ± 2%* 106% ± 3%* 101% ± 10% 113% ± 17% 101% ± 25% 110% ± 2%* 122% ± 2%* 64% 58% Overall, the results obtained with the ATP depletion assay indicated that both the values for sensitivity and specificity obtained with either HPTC or HPTC-like cells were lower than the respective values obtained with the IL-6/IL-8-based assay. The sensitivity of the ATP depletion assay was particularly low (50% in HPTC and 48% in HPTC-like cells), and the IC50 values remained above 1000 μg/ml even with compounds that have demonstrated strong toxic effects in PTCs, such as arsenic (III) oxide (compound 11). To verify this unexpected result, I performed the ATP depletion assay with kits from two different vendors (see Materials and Methods) and the results remained negative. This is also consistent with the high false negative rate (~ 50%) obtained by measuring ATP depletion in a previous in vitro study addressing organ-specificity, including nephrotoxicity [45]. This conclusion was further confirmed by the analysis of the ROC curves (Fig. 15 c). Whereas AUC values of at least 80% (or 0.8, HPTC-like cells, Fig. 15 c; HPTC, Table 15) were obtained with the IL-6/IL-8-based assay, AUC values of only 65% (or 0.65) were obtained with the ATP depletion assay with both cell types (Fig. 15 c; Table 15). 95 Another commonly used toxicity assay, the glutathione (GSH) depletion assay, was also performed for comparison with the IL-6/IL-8-based assay. GSH plays an important role in drug metabolism and protection against oxidative damages from reactive oxygen species in proximal tubular cells [18]. Here the same batch (HPTC 1) and passage numbers of HPTC were tested as previously used for determining ATP depletion. The results obtained with the GSH depletion assay were similar to those obtained with the ATP depletion assay (Fig. 16, bottom row; Table 16). The sensitivity of the GSH depletion assay (45%) was even slightly lower than that of the ATP depletion assay (50%), whereas their specificity values were the same (74%, Table 16). The AUC of the ROC curve for the GSH depletion assay (60%) was also lower than that of the ATP depletion assay (65%), indicating an overall lower predictivity of the GSH depletion assay (Fig. 15 c; Table 15). Lastly, a commonly used assay measuring cellular membrane damage was performed. This assay determines the degree of membrane damage induced by toxic compounds by quantifying the leakage of lactate dehydrogenase (LDH) from the cytoplasm. Here, the result of a test compound was designated as positive whenever there was a significant increase in LDH leakage as compared to the vehicle control (marked with asterisks in Table 16). Results obtained with HPTC 1 revealed that though the sensitivity (64%) was higher than those of the ATP depletion assay and the GSH depletion assay, the specificity value of the LDH leakage assay was only 58%. This value was significantly lower than the respective values obtained with the other assays (> 70%, Table 16). The values for sensitivity and specificity were also lower than those of the IL-6/IL-8-based model (68% and 84%, respectively). 96 4.4.3 Prediction of the PT toxicity of blinded compounds The results showed, so far, that the overall predictivity was highest when expression levels of the marker genes IL-6 and IL-8 were used as endpoints in combination with HPTC. HPTC-like cells are an alternative cell type with comparable predictivity which could overcome the limitations of relative scarcity and high costs associated with human primary cells. To further investigate the predictive performance of the model based on HPTC-like cells, blinded compounds were used to treat these cells. Based on the IL-6/IL-8 expression patterns their PT toxicity was predicted. Meanwhile, batch-to-batch variability of the HPTC-like cells was also addressed, by using a second batch of HPTC-like cells which had been differentiated from hESCs by a different colleague (Dr. Karthikeyan Kandasamy, IBN) about 6 months after the first batch was differentiated. The second batch of HPTC-like cells was also characterized by immunostaining of PT cell markers to ensure the quality of these cells [10]. In order to compare with results obtained earlier, blinded compounds were selected from the list of 41 compounds (Table 1). These compounds were added to cells by a colleague (Dr. Karthikeyan Kandasamy, IBN) and the identity of these compounds was only revealed to me after I had performed sample preparation, qPCR and data analysis. The blinded compounds were revealed to be cephalosporin C, tacrolimus and acarbose (compounds No. 6, 19 and 40). The range of concentrations tested was the same as in previous experiments. In addition, dexamethasone (100 μg/ml) and puromycin (100 μg/ml) were selected as negative and positive controls, respectively, to allow calculation of the Z’ values to monitor overall assay performance. Results on the controls were also obtained in a blinded manner. 97 Table 17. Results obtained with three blinded compounds and prediction of PT toxicity. Adapted with permission from [10]. Copyright 2014 American Chemical Society. Prediction Compound 6 19 40 IL-6 3.9 ± 1.0 70.5 ± 10.5 2.4 ± 0.8 Z’ IL-8 Z’ 0.7 0.9 0.8 67.8 ± 23.1 206.1 ± 51.5 3.7 ± 1.2 1.0 0.9 0.8 (Cut-Off = 4.0) + + - Table 17 displays the highest IL-6 and IL-8 expression levels obtained for the test compounds (the detailed results obtained at all tested concentrations are displayed in the appendix, supplementary data, Table S14). The cut-off value of 4.0 (fold changes in gene expression levels) as previously used for 41 test compounds with the first batch of HPTC-like cells was applied here for the prediction of PT toxicity of the blinded compounds. Based on this cut-off value compound 40 (acarbose) was correctly predicted to be non-toxic to cells of the renal PT in humans, whereas compounds 6 and 19 (cephalosporin C and tacrolimus) were correctly predicted to be toxic to this cell type (Table 17). These results are consistent to those obtained with the first batch of HPTC-like cells (Tables 16 and 17). Furthermore, these predictions were in agreement with clinical results of these compounds on PT toxicity. Tacrolimus has various negative effects on the human kidney, including direct toxic effects on renal proximal tubular cells [36, 139]. Cephalosporin antibiotics are semi-synthetic derivatives of cephalosporin C, and these compounds are substrates of the organic anion transport system of the proximal tubule. Several cephalosporins were found to be associated with acute tubular necrosis and there is a consistent risk of PT toxicity associated with cephalosporinderived compounds [140, 141]. Acarbose is an α-glucosidase inhibitor used for the improvement of glycemic control in adults with type 2 diabetes mellitus. Adverse effects on various human 98 organ systems including liver, lung and skin have been reported (see, for instance, http://chem.sis.nlm.nih.gov/chemidplus/cas/56180-94-0), but to the best of my knowledge, no adverse effects on the proximal tubules have been previously reported. Together, the results shown in sections 4.4.1 – 4.4.3 demonstrated for the first time that stem cell-derived HPTC-like cells can be used as an alternative cell type for in vitro nephrotoxicology. Similar predictivity can be achieved with HPTC-like cells and HPTCs, and the performance was in both cases better when IL-6/IL-8 expression was used as endpoint in comparison to standard assays. However, the use of hESCs is associated with ethical and legal issues as the harvesting of these cells involves the destruction of human embryos. To avoid such controversies, it would be more desirable to use human induced pluripotent stem cells (hiPSCs), which are reprogrammed adult cells [142, 143]. Results on the use of hiPSC-derived HPTC-like cells are described in the following subsection (4.4.4). 99 4.4.4 Predictive performance of hiPSC-derived HPTC-like cells Human iPS(Foreskin-4) cells were differentiated into HPTC-like cells over a period of 20 days using the same protocol as described for the differentiation of hESCs in [9]. Cell differentiation was performed by Dr. Wei Seong Toh (IBN). The hiPSC-derived HPTC-like cells had been cryopreserved before use. The same list of 41 test compounds (see Table 1) was used to validate the use of these cells in the IL-6/IL-8-based in vitro model. Drug treatment and qPCR procedures were performed as described in the previous sections. All results (IL-6 and IL-8 expression) were normalized to the respective vehicle controls and expressed as fold changes relative to the vehicle control. Detailed results on IL-6 and IL-8 expression for all concentrations of each test compound are listed in appendix, Supplementary Data, Tables S15 and S16. In these tables the highest levels of expression within the concentration range tested were highlighted for each compound. These highest values were summarized in Table 18. The definition of a positive result was the same as previously described: a result was classified as positive if the highest increase in gene expression of at least one of the two markers (IL-6/IL-8) was equal to or higher than a threshold value. In the case of hESC-derived HPTC-like cells, the analysis on the predictive performance was carried out over a range of threshold values from 0.3 to 5.0. 100 Table 18. Highest expression levels of IL-6 and IL-8 in hiPSC-derived HPTC-like cells. Cells were exposed to the 41 compounds (same as listed and numbered in Table 1) at concentrations ranging from 1 g/ml and 1000 g/ml. This table lists the highest expression levels of both marker genes that were recorded at any concentration of a drug within this range. The values indicate the mean fold expression level with s.d. (n = 3) relative to vehicle controls. The highest expression levels shown here are highlighted in the appendix, Supplementary Data, Tables S15-S16, where detailed expression levels measured at all drug concentrations were listed. No. Compound 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 IL-6 IL-8 Gentamicin 86.0 ± 1.5 860.1 ± 30.9 Tobramycin 36.5 ± 7.0 86.3 ± 20.4 Rifampicin 27.3 ± 2.7 23.2 ± 1.5 Tetracycline 60.9 ± 15.6 4710.3 ± 1107.7 Puromycin 266.0 ± 22.2 964.2 ± 34.2 Cephalosporin C 4.0 ± 0.3 58.4 ± 16.5 5-Fluorouracil 12.6 ± 0.8 11.2 ± 1.3 Cisplatin 20.0 ± 1.6 0.3 ± 0.0 Ifosfamide 1.2 ± 0.2 1.9 ± 0.1 Paraquat 3.1 ± 0.9 3.3 ± 0.7 Arsenic(III) oxide 3.5 ± 0.5 3.8 ± 0.1 Bismuth(III) oxide 31.8 ± 2.0 4321.1 ± 254.3 Cadmium(II) chloride 11.0 ± 1.3 11.1 ± 0.9 Copper(II) chloride 96.5 ± 13.8 13.8 ± 1.2 Germanium(IV) oxide 2.3 ± 0.5 2.0 ± 0.6 Gold(I) chloride 23.0 ± 3.7 2.3 ± 0.2 Lead acetate 19.8 ± 0.7 58.7 ± 14.5 Potassium dichromate 0.7 ± 0.1 1.4 ± 0.3 Tacrolimus 61.7 ± 3.4 48.5 ± 3.3 Cyclosporin A 49.1 ± 6.4 1246.9 ± 70.5 Citrinin 5.2 ± 0.3 19.2 ± 7.7 Tenofovir 2.3 ± 0.5 0.3 ± 0.2 Vancomycin 2.2 ± 0.1 2.8 ± 0.0 Phenacetin 1.8 ± 0.3 3.0 ± 1.2 Acetaminophen 1.1 ± 0.1 1.0 ± 0.2 Ibuprofen 2.5 ± 0.4 0.4 ± 0.1 Furosemide 2.1 ± 0.4 2.8 ± 0.9 Lithium Chloride 1.3 ± 0.1 1.9 ± 0.3 Lindane 1.6 ± 0.2 1.1 ± 0.1 Ethylene glycol 0.9 ± 0.1 1.2 ± 0.1 Valacyclovir 3.5 ± 0.4 20.0 ± 1.8 Lincomycin 2.7 ± 0.4 9.2 ± 2.5 Ciprofloxacin 80.1 ± 7.6 1140.9 ± 74.3 101 34 35 36 37 38 39 40 41 Ribavirin Glycine Dexamethasone Melatonin Levodopa (DOPA) Triiodothyronine Acarbose Atorvastatin 1.1 ± 0.1 1.9 ± 0.1 1.1 ± 0.2 1.9 ± 0.1 1.6 ± 0.3 0.8 ± 0.0 1.0 ± 0.0 72.2 ± 14.6 1.1 ± 0.2 6.1 ± 1.0 1.2 ± 0.1 1.8 ± 0.1 1.8 ± 0.3 2.8 ± 0.2 1.3 ± 0.1 86.1 ± 35.9 The analysis of these data was performed with a similar procedure as described in 4.3 and 4.4.1. True positives (TP), true negatives (TN), sensitivity and specificity were defined as shown in Fig. 1. The results for all cut-off values in the range of 0.3 - 5.0 are displayed in Table 19 and are graphically illustrated in Fig. 17 a, which also shows the overall concordance with human clinical data. The results show that a cut-off value of 3.5 is optimal for these hiPSC-derived HPTC-like cells, where both sensitivity and specificity values were above 70% (Table 19, Fig. 17 a). Table 19. Determination of TP, TN, sensitivity and specificity in hiPSC-derived HPTC-like cells Cut-Off TP Sensitivity TN Specificity 0.3 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 22 22 21 20 18 18 17 16 16 16 100% 100% 95% 91% 82% 82% 77% 73% 73% 73% 0 0 5 9 9 13 14 14 14 14 0% 0% 26% 47% 47% 68% 74% 74% 74% 74% 102 Figure 17. Sensitivity, specificity, overall concordance and ROC curve for hiPSC-derived HPTC-like cells. (a) Graphical display of the sensitivity and specificity values shown in Table 19. The figure also shows the overall concordance of the results with the PT toxicity of the compounds in humans as reported in literature (classification as listed in Table 1). Cut-off values (x-axis) ranged from 0.3 – 0.5. (b) The ROC curve was plotted using sensitivity and specificity values at all cut-off values as indicated in Table 19. The AUC value of the ROC curve was calculated with respect to the combination of both IL6 and IL8 expression and is indicated as percentage. A diagonal line representing an AUC value of 50% was displayed for comparison. Other major performance metrics such as PPV (0.77), NPV (0.74) and balanced accuracy (0.75) were also calculated at the cut-off value of 3.5 and are summarized in Table 15. Furthermore, the receiver operating characteristic (ROC) curve was plotted and its AUC was 76% (Fig. 17 b, or 0.76 in Table 15). Together, the results showed that the predictive performance of hiPSC-derived HPTC-like cells was close to that of hESC-derived HPTC-like cells. The results (Table 15) also showed that the performance of hiPSC-derived HPTC-like cells was better than the performance of standard PT cell lines (IL-6/IL-8-based endpoint). Further, the predictive performance was better than the performance of HPTCs or hESC-derived HPTC-like cells in combination with standard endpoints (Table 15). 103 Together, these results showed for the first time that the use of iPSC-derived HPTC-like cells is a viable alternative that could circumvent the ethical concerns associated with hESCs, without severely compromising the predictive performance of the current in vitro model. However, it is worth noting that qPCR-based characterization of these hiPSC-derived HPTC-like cells (performed by fellow colleagues in the lab) revealed differences in the expression of certain renal cell markers and drug transporters as compared to HPTCs in vitro (also applies to hESC-derived HPTC-like cells, see [10]). For example, hiPSC-derived HPTC-like cells showed low expression levels of efflux drug transporters (data not shown). Although such features could be due to in vitro culturing conditions, as previously observed [8, 9, 65, 87], they suggest that more extensive optimization of the differentiation procedures can possibly further enhance cell performance, which could potentially translate into even higher predictivity. A more extensive study on improving hiPSC-derived HPTC-like cells by modifying differentiation protocols and incorporation of bioinformatics algorithms in the data analysis is currently in progress and a manuscript has been recently submitted for review (see appendix iii, list of publications). As my contributions in this work were limited to supervising intern students and data analysis, a detailed report is beyond the scope of this thesis and therefore not included here. In conclusion, stem cell-based approaches appear to be viable alternatives to the use of primary cells and cell lines in in vitro nephrotoxicology. It remains highly intriguing that the IL-6/IL-8based endpoint consistently yielded high predictivity, although underlying mechanistic information was still lacking. In another study of our group, we found that nuclear translocation of NF-B was often observed in cases where PTCs were treated with PT-specific nephrotoxicants (data not shown), and there was a strong correlation with the positive results 104 from the IL-6/IL-8-based model. In order to provide mechanistic insights into pro-inflammatory pathways triggered by nephrotoxicants, it was thus important to further investigate the relationships between nuclear translocation of NF-B and IL-6/IL-8 up-regulation in PTCs. This work is outlined in the following section (4.5). 105 4.5 Molecular and cellular mechanism of drug-induced IL-6/IL-8 expression in renal PTCs 4.5.1 Puromycin-induced nuclear translocation of NF-B and IL-6/IL-8 expression As shown by data from a separate on-going study from our group, PT-specific nephrotoxicants often lead to nuclear translocation of NF-B in HPTCs (data not shown). It has also been shown in other cell types that IL-6 and IL-8 are target genes of NF-B, and the p65 (also called RelA) subunit of NF-B is essential for the up-regulation of these interleukins [144-146]. Thus, I hypothesized that drug-induced up-regulation of IL-6/IL-8 expression in human renal proximal tubular cells is mediated by the NF-B pathway. To test this hypothesis, I first examined druginduced nuclear translocation of NF-B by performing immunostaining of the p65 subunit. HPTCs and HK-2 cells were examined after overnight exposure to puromycin, which is a PTspecific nephrotoxicant also used in the previous sections of this thesis (compound 5, Table 1). It was selected here due to its high efficacy in inducing high levels of IL-6/IL-8 expression in various PT-derived cell types and batches as shown in previous sections 4.3 and 4.4. Immunostaining results showed that in untreated HK-2 cells and HPTCs, NF-B p65 (as indicated by the green fluorescence) was localized primarily in the cytoplasm of cells (Fig. 18 a, c). In contrast, after cells were treated with puromycin (100 g/ml) for 16h, there was a decrease in the fluorescence level in cytoplasmic area of both HK-2 cells and HPTCs (Fig. 18 b, d), and cell nuclei of both cell types became enriched with green fluorescence, indicating nuclear translocation of NF-B p65. Translocation was observed in only part of the HK-2 cells (some indicated by arrowheads in Fig. 18 b), whereas in HPTC almost all cell nuclei were brightly stained with green fluorescence (Fig, 18 d). This difference indicated a more efficient induction of the nuclear translocation of NF-B p65 by puromycin in HPTCs as compared to in HK-2 cells. 106 Figure 18. Immunostaining of NF-B p65 in HK-2 cells (a and b) and HPTC (c and d). Cells were either treated with the vehicle control (a and c) or puromycin (100 g/ml; b and d). Arrowheads in (b) p65.increased Scale bars: 100 13% m. to 68% in HKindicate examples of cell nucleithat withpercentage strong staining of NF-B The analysis results showed of positive cells from These results confirmed that treatment with puromycin could indeed lead to nuclear translocation of NF-B p65 in renal PTCs, in which puromycin-induced up-regulation of IL-6/IL-8 expression as previously observed (Tables 5, 6, 13, 18). The co-occurrence of these two effects suggested that puromycin was a suitable PT-specific nephrotoxicant to be used in the subsequent gene knockdown and inhibitor studies, for further investigations on the relationships between these two events. 107 4.5.2 Effects of p65 silencing on nuclear translocation of NF-B and IL-6/IL-8 expression To investigate whether the previously observed up-regulation of IL-6/IL-8 expression after overnight exposure to puromycin (in primary and immortalized human renal PTCs and stem cellderived HPTC-like cells , Tables 5, 6, 13 and 18) was mediated by the NF-B pathway, I next performed siRNA transfection with HPTCs and HK-2 cells to silence NF-B p65 expression. The siRNA used here (see Materials and Methods) had complementary sequence to the mRNA of NF-B p65, and therefore when transfected into cells, would bind to the single-stranded mRNA. The resultant double-stranded RNA molecule would then be targeted for degradation [147], leading to a reduced level of p65 mRNA being translated in the transfected cells. These experiments were performed to find out whether a reduction in the cellular levels of functional NF-B p65 heterodimers could lead to inhibition of puromycin-induced up-regulation of IL6/IL-8 expression. The protein levels of p65 in HPTCs and HK-2 cells were examined by western blotting after siRNA-mediated gene silencing. Results showed that there was substantial decrease in levels of p65 protein in both HPTC and HK-2 cells after siRNA transfection as compared to the negative controls (Fig. 19 A, B). On the other hand, p65 levels in HPTCs and HK-2 cells transfected with non-target siRNA or siRNA targeting the GAPDH mRNA remained comparable to nontransfected controls. Densitometric analysis of these results revealed that there was a ~ 50% and ~ 81% reduction in HPTCs and HK-2 cells respectively, after transfection with p65-specific siRNA (Fig. 19 C, D). The negative controls for HPTC remained ~ 100% of non-transfected cells, but HK-2 cells transfected with non-target siRNA also exhibited ~ 31% reduction in p65 expression, indicating the possibility of unspecific silencing in HK-2 cells. 108 Figure 19. Detection of p65 by immunoblotting in protein lysates of (A) HK-2 cells and (B) HPTCs. NT: non-target control; GAPDH: glyceraldehyde 3-phosphate dehydrogenase. The upper row of protein bands represents p65 and the lower row of bands represents -tubulin, the loading control. Panels C (HK-2) and D (HPTC) show relative protein levels of p65 quantified by densitometric analysis of the western blotting results, after normalization to loading controls. Asterisks indicate statistical significance (P < 0.05). Furthermore, I performed immunostaining of p65 in HK-2 cells after siRNA transfection, with or without puromycin treatment (100 g/ml). Immunostaining results revealed that in the untreated HK-2 cells, p65 protein (detected by green fluorescence) was primarily localized in the cytoplasm (Fig. 20 C). Cells transfected with p65-specific siRNA showed overall substantially lower levels of green fluorescence as compared to the controls (Fig. 20 A), indicating effective silencing of p65 expression. However, after cells had been treated with puromycin overnight, cytoplasmic p65 translocated into the nuclear region in all cases, including HK-2 cells transfected with p65-specific siRNA (Fig. 20 D-F). This shows that although siRNA transfection could reduce the overall level of p65 in the cells, it could not prevent residual p65 from 109 translocating into the nuclear region when cells were stimulated with puromycin (note also that the western blots showed the presence of residual p65 protein (Fig. 19 A, B)). Also high content analysis of these images revealed that there was a substantial decrease in p65 protein levels in both nuclear and cytoplasmic regions as a result of transfection with p65specific siRNA (Fig. 20 G, blue bars), confirming successful knockdown of the p65 protein. However, when cells were treated with puromycin overnight, there was a substantial increase in nuclear p65 as compared to the untreated cells. This observation applied to all transfection conditions, including cells transfected with p65-specific siRNA. Fig. 20 H shows that the percentage of cells positive for nuclear translocation of NF-B increased from ~ 20% to ~ 90% after puromycin treatment, and there was no significant difference in such response among different transfection conditions and controls. The nuclear p65 / cytoplasmic p65 ratios were all < 1 in untreated cells, regardless of the transfection conditions. However, when cells were treated with puromycin overnight, this ratio increased to > 1.5 in all cases, indicating effective nuclear translocation of NF-B regardless of the knockdown (Fig. 20 I, blue bars). Together, these results suggest that partial knockdown of p65 had no observable inhibitory effects on nuclear translocation and relative nuclear levels of NF-B p65 in HK-2 cells. 110 Figure 20. Immunostaining of p65 in HK-2 cells (A-F). Green: p65 subunit of NF-B; Blue: DAPI staining of cell nuclei. Panels A-C show images of HK-2 cells without drug treatment. Panels D-F show images of HK-2 cells after overnight exposure to puromycin (100 g/ml). Cells in Panels A and D were transfected with p65-specific siRNA; B and E show cells transfected with non-target (NT) siRNA; C and F show non-transfected cells. G shows the mean fluorescence intensity of p65 protein levels in both cytoplasmic and nuclear regions of untreated or puromycin-treated cells under different transfection conditions. H shows the mean percentage of cells which show positive NF-B translocation. Panel I shows the mean values of nuclear p65 / cytoplasmic p65 ratio under different treatment and transfection conditions. The bars in G-I show mean values ± standard deviation (s.d.; n = 3). 111 Next, I examined the effects of siRNA-mediated knockdown of p65 on puromycin-induced IL6/IL-8 expression by qPCR. Fig. 21 shows relative expression levels of IL-6 and IL-8 in both HK-2 cells (A) and HPTCs (B). All data shown here were normalized to those of untreated control cells, which were set to 1 (not shown). The results show that in puromycin-treated HPTCs, cells transfected with p65-specific siRNA had a significantly higher mean relative expression level of IL-6 (~ 12) than cells transfected with non-target siRNA (~ 4) and nontransfected controls (~ 4). This observation shows that silencing of p65 by siRNA transfection had an enhancing effect on IL-6 expression, which was contradictory to the expected results. On the other hand, mean relative expression level of IL-8 (2.6) was just marginally lower than those of the negative controls (3.1 and 3.5). Results on HK-2 cells were similar to those of HPTCs, except that there was no further enhancement in drug-induced IL-6 expression in p65 siRNAtransfected cells. Together, these results showed that partial silencing of p65 expression did not have a clear inhibitory effect on puromycin-induced IL-6/IL-8 expression. This observation was consistent with the absence of inhibitory effects on nuclear translocation of NF-B in cells transfected with p65-specific siRNA. Therefore, it was tested whether inhibition of nuclear translocation of NF-B would be a better approach for studying the relationships with druginduced IL-6/IL-8 expression in PTCs. 112 A B Figure 21. Marker gene expression levels determined by qPCR in HK-2 cells (A) and HPTC (B) after overnight exposure to puromycin (100 g/ml). Gene expression levels were displayed as fold changes and normalized to untreated cells (set to 1, not shown) under each transfection condition. Cells were transfected with either p65-specific siRNA (blue bars) or non-target (NT) siRNA (red bars). Non-transfected water control cells are represented by green bars. All bars show the mean ± s.d. (n = 3). 113 4.5.3 Effects of inhibition of nuclear translocation of NF-B Since siRNA-mediated silencing of p65 did not have an inhibitory effect on puromycin-induced expression of IL-6 and IL-8, I next performed inhibition of nuclear translocation of NF-B p65 in HK-2 cells and HPTCs. This was achieved by inhibiting IB kinase (IKK), by using commercially available IKK inhibitors BAY 11-7082 and BAY 11-7085. When IKK is inhibited, IBremains unphosphorylated and bound to the NF-B homo- or heterodimeric complexes, masking their nuclear localization sequence (NLS) and preventing their translocation into cell nuclei [148, 149]. First, I confirmed the inhibitory effect of BAY 11-7085 on nuclear translocation of NF-B p65 by immunostaining and subsequent image analysis. Fig. 22 A shows that only ~ 12% of untreated HK-2 cells were positive for nuclear translocation of NF-B p65. Overnight exposure to puromycin (100 g/ml) sharply increased this percentage to ~ 65%. Fig. 22 C shows a corresponding increase in the nuclear p65 / cytoplasmic p65 ratio from 0.6 to 1.4, confirming active nuclear translocation of NF-B p65 in the HK-2 cells exposed to puromycin. In cases where 5 M (grey bars in Fig. 22) and 20 M (white bars) of BAY 11-7085 was added during puromycin treatment, the percentage of positive cells fell slightly from 65% to 55% (Fig. 22 A) and the nuclear / cytoplasmic p65 ratio changed from 1.4 to 1.1 (Fig. 22 C), showing a moderate inhibitory effect of BAY 11-7085 on puromycin-induced nuclear translocation of NF-B in HK2 cells. Furthermore, when tumor necrosis factor alpha (TNF-, 100 ng/ml) was used to enhance the effects of puromycin, I observed further increases in the percentage of positive cells to > 90% and an intensity ratio of 2.4 in the absence of any inhibitor. In this case, both parameters remained at such levels when 5 M of BAY 11-7085 was added to cells. Only a higher 114 concentration of the inhibitor (20 M) was able to reduce the combined effects of puromycin and TNF-, leading to significant decreases in both parameters to 70% and 1.2, respectively. TNF- alone did not have any effects on the nuclear translocation in HK-2 cells when used at this concentration. On the other hand, untreated HPTCs already appeared to be in a partially stimulated state and 40% * * * * * Figure 22. Percentage of cells classified as positive for nuclear translocation of NF-B p65 and nuclear p65 / cytoplasmic p65 ratio in HK-2 cells (A, C) and HPTC (B, D). Cells were treated with puromycin (100 g/ml) or TNF- (100 ng/ml) or both as displayed on the x-axis. BAY 11-7085 was used at 5 M (grey bars) or 20 M (white bars). Black bars represent uninhibited cells. The bars show the mean ± s.d. (n = 3). Asterisks indicate statistical significance (P < 0.05). 115 of the cells were positive for nuclear translocation of NF-B p65 even in the absence of puromycin (Fig. 22 B). The average nuclear NF-B p65 / cytoplasmic NF-B p65 intensity ratio of 0.9 (and 1.3 in the presence of 5 M BAY-11 7085; Fig. 22 D) was also higher than that observed in HK-2 cells (0.4 ~ 0.5). However, when exposed to puromycin, HPTCs were also more sensitive than HK-2 cells: percentage of positive cells and nuclear / cytoplasmic p65 ratio increased to 100% and 2.9 respectively when treated with puromycin (100 g/ml). Addition of 5 M of BAY 11-7085 was able to decrease the intensity ratio to 1.7 but the percentage of positive cells remained at 100%. This observation reflected that though there was a quantitative reduction in nuclear translocation of NF-B p65, there was no qualitative change in terms of classifying the cells as positive or negative (see definition in Materials and Methods). TNF-did not lead to further increases in either endpoint, but again prevented the inhibitor (5 M) from quenching the intensity ratio, which remained > 2.5 when cells were treated with puromycin and TNF-. A higher concentration of BAY 11-7085 (20 M) could not be used in this case as it induced massive cell death in HPTCs (data not shown). Together, these results suggested that BAY 11-7085 indeed had an inhibitory effect on druginduced nuclear translocation of NF-B p65 in human renal proximal tubular cells, and such effect could be antagonized by TNF-, which enhanced the stimulating effect of puromycin. Next, I performed qPCR to examine the effects of IKK inhibitors on drug-induced expression of IL-6 and IL-8. Fig. 23 shows relative gene expression levels of IL-6 and IL-8 in HK-2 cells (Fig. 23 A, B) and HPTCs (Fig. 23 C, D). An additional IKK inhibitor, BAY 11-7082 was used here for comparison (Fig. 23 B, D). All data were normalized to untreated controls. 116 Figure 23. IL-6 and IL-8 gene expression levels determined by qPCR in (A, B) HK-2 cells and (C, D) HPTCs. Gene expression levels were displayed as fold changes and normalized to the untreated cells (set to 1; black bars). White bars show marker expression levels in cells treated with puromycin (100 g/ml) overnight, in the presence or absence of BAY 11-7085 (10 M; A, C) and BAY 11-7082 (5 M; B, D). The bars show the mean ± s.d. (n = 3). In HK-2 cells, there was significant reduction in puromycin-induced expression levels of both IL-6 (from 12.8 to 8.3) and IL-8 (from 7.8 to 4.7) when 10 M of BAY 11-7085 was used (Fig. 23 A). BAY 11-7082 (5 M) also led to similar inhibitory effects on expression levels of both marker genes (Fig. 23 B). In HPTCs, the effects of both inhibitors (BAY 11-7085: 10 M and BAY 11-7082: 5M) were much more drastic. Drug-induced up-regulation of IL-6/IL-8 expression was almost entirely inhibited (Fig. 23 C, D). 117 Together, these data provided strong evidence that nuclear translocation of NF-B p65 was upstream with respect to up-regulation of IL-6 and IL-8 expression on the same pathway, as inhibition of the translocation event by IKK inhibitors could lead to inhibition of IL-6 and IL-8 expression. These results highlight the essential role of the NF-B pathway in drug-induced interleukin expression, especially in human primary renal proximal tubular cells. Together, the results provide mechanistic insights into the pathway activated in human renal proximal tubular cells by nephrotoxic drugs, and at the same time identified useful endpoints for in vitro nephrotoxicology that can be also used for HCS. 118 5. Discussion 5.1 Effects of substrate stiffness on primary human endothelial and renal cells As described in the previous sections, an in vitro model for the prediction of drug-induced nephrotoxicity has been developed. The first step was to determine a suitable culturing substrate for HPTCs. Here I used HUVECs and HPTCs to evaluate various materials of different stiffness and with a variety of other different features [5]. The results revealed that there was strong correlation between substrate stiffness and the performance of these primary cell types. TCPS and CG appeared to be the most suitable materials for the in vitro nephrotoxicity model. Although it was well established that surface chemistry and wettability were the major factors that affect the proliferation and performance of somatic cells and embryonic stem cells [150-157], the effects of substrate stiffness were variable. For example, there have been studies suggesting that high substrate stiffness promotes adhesion of HDFs [158] as well as proliferation and migration of epidermal keratinocytes [159]. In contrast, softer substrates promote myoblast differentiation [160]. More interestingly, while soft materials promotes the differentiation of neural stem cells into neurons, stiffer materials directs formation of glial cells [161]. These findings indicate that different cell types display vastly different stiffness-dependent responses when cultured in vitro. With respect to the results described in Section 4.1, HUVECs displayed high sensitivity to the stiffness of different culturing substrates. Endothelial cells are a mechanosensitive cell type [162, 163] and they are constantly subjected to mechanical stimuli such as shear stress and hydrostatic 119 pressure of blood flow. When cultivated on TCPS and CG, HUVECs could exert greater force on the stiff substrates as compared to softer substrates, and this was reflected by the formation of actin stress fibres (see Section 4.1) similar to those formed in vivo in endothelial cells [111, 112]. It was found that in endothelial cells, actin stress fibres regulate the activation of mechanosensitive channels [164, 165], which play an important role in mechanotransduction. One of the outcomes of mechanotransduction is stabilization of cell adhesion to the substrate [166]. Therefore, the lack of actin stress fibre formation on compliant substrates explains the substantially reduced numbers of cells attached and the compromised cell performance on TX and PC-1 as compared to TCPS and CG. HPTCs are constantly exposed to the fluid shear stress of the glomerular filtrate in vivo. Studies have shown that in mouse renal proximal tubular cells, the brush border microvilli on the apical surface appeared to play an important role in mechanosensing [167, 168]. Adhesion onto a stiff culturing substrate in vitro (in a static culture) can possibly compensate for the force required for actin stress fibre formation and subsequent mechanotransduction events. This could thus explain why, as described earlier, HPTCs performed better on stiff PLA films as compared more compliant electrospun PLA membranes in terms of proliferation and epithelium formation. Although substrate stiffness appeared to be the major factor here affecting the performance of HUVECs and HPTCs in static cultures, it might be that the effects of other parameters such as surface chemistry also play a role. Cells integrate all environmental cues, including physical and chemical properties of the underlying substrates. Proper cell performance can only be achieved if the combinatorial effects from these environmental cues are favorable. My results indicated that 120 simple static cultivation on uncoated TCPS was sufficient for proper differentiation and proliferation of HPTCs, and no other substrates could be identified where HPTC performance was better [5] (see also further explanations on page 45). This conclusion was also repeatedly confirmed by data from colleagues working on different types of gels with tunable stiffness and other synthetic substrates. It regularly turned out that HPTC performance on the TCPS controls was better than on gels with tunable properties and other substrates (unpublished work). However, the work on gels with tunable stiffness confirmed that substrate stiffness is a major determinant of HPTC and HUVEC performance (for full picture with extensive material characterization see [5]), and increasing gel stiffness correlated with improved cell performance. In addition, it is also most straightforward to work with TCPS (readily available in most tissue culture vessels such as multi-well plates). Therefore, I subsequently applied these cell culture conditions in the development of the HPTC-based in vitro model for prediction of nephrotoxicity. 121 5.2 Cell type-specific responses to toxicants in cultured primary cells As mentioned in the Results, layered clays are frequently used as the hemostatic agent in wound dressings [113, 114] and they have profound cytotoxic effects on different cell types [115-117]. MCF-26 demonstrated similar hemostatic potency as commercial hemostatic materials [118] but its cytotoxicity in human cell types has not been well characterized. Here, the cell type-specific cytotoxicity of MCF-26 and layered clays with hemostatic properties has been addressed by using various human primary cell types and two well-characterized standard call lines for comparison. Cell viability was investigated in vitro after exposure to the different compounds. The results based on NRU assays revealed that the cytotoxic effects of test compounds were strongly cell type-dependent. This is in accordance with other studies which showed similar cell type-specific toxic effects of layered clays [115-117]. One limitation of the NRU assay is that the absorbance readings depend on lysosome numbers in the cells, and therefore it might not be suitable to test compounds which affect lysosomal uptake of the dye. However, the NRU assay was chosen to test cell viability in this model due to its greater sensitivity as compared to the standard tetrazolium-based viability assays [104, 169], and it is also recommended by the ISO standard 10993-5:2009 (E) as mentioned before in Section 3.6 of this thesis. The high sensitivity of HUVECs towards the toxic effects of layered clays was not surprising, as previous studies showed that the smectite granule-based WoundStat (WS) has caused endothelial damage as well as other degenerative processes in vivo [170, 171]. WS was approved by the US 122 Food and Drug Administration (FDA) in 2007 but its use was halted soon afterwards due to reported thromboembolic risk [113, 171], which could be associated with the adhesiveness of layered clays as shown by my results in Section 4.2. Also in agreement with my results, it has been previously reported that aluminium silicates used in such hemostatic dressings have cytotoxic effects on HUVECs (and also murine macrophages), whereas epithelial cells were less affected [115]. It was argued that such expected toxic effects indicate that the standard in vitro safety tests based on fibroblast cultures are inadequate [171]. This claim was further confirmed by my results (as described in Section 4.2) which showed that the fibroblastic cell types tested were in general the least sensitive to the cytotoxicity of layered clays. In particular, the response in HDFs when treated with kaolin showed that even the highest concentration tested (250 g/ml) was still in the subtoxic range for HDFs, whereas cell viability was greatly reduced in HUVECs. If only fibroblasts were tested here, the cytotoxic effects of kaolin would have been greatly underestimated. Therefore, it is important to develop test systems that detect the toxicity of these compounds more reliably, as demonstrated here. Similar cell type-specific effects of MCF-26 were also observed here. However, MCF-26 was only mildly cytotoxic to all cell types tested, and the IC50 values were substantially higher than those of the layered clays. This strongly supports the hypothesis that MCF-26 could be a less toxic alternative to currently used hemostatic agents. Currently, the reason for the reduced cytotoxicity of MCF-26 is unclear. It appears that toxicity of layered clays requires direct contact between the clay particles and cell surface, and is not due to leaching of toxic ions or trapping of cell culture medium components 123 by layered clay materials [115]. Indeed, my data and a previous study [116] showed that layered clays adhered strongly to the cell surface. It has been suggested that the negative charge of layered clays played a role in their adherence onto cell surface and cytotoxicity [116]. My results showed that MCF-26 adhered less strongly to the cell surface despite the fact that its zeta potential was similar to kaolin and bentonite in cell culture medium (data not shown), and it was less cytotoxic. As such, weaker adherence of MCF-26 onto cell surface, which appeared unaffected by surface charges, might provide an explanation why it was less toxic to various cell types. In addition, the absence of cellular uptake of MCF-26 might also contribute to its reduced cytotoxicity. On the other hand, kaolin was rapidly taken up by HDFs and HUVECs. However, only HUVECs displayed susceptibility to the toxic effects of kaolin, suggesting that cell typespecific response is not solely due to differences in cellular uptake of test compounds. This also applies to Ag NPs, which were taken up by both HDFs and HUVECs, but in this case cell typespecific cytotoxicity was less clear due to larger batch-to-batch variation within HDFs. Nevertheless, with respect to all the cell types tested, clear cell type-specific cytotoxic effects were also observed with Ag NPs: HEKs were the most resistant to the cytotoxic effects in comparison to other cell types such as HUVECs or HDFs. This was in agreement with a previous in vivo study which reported that Ag NPs promoted proliferation and migration of keratinocytes during wound healing and caused significant cell death only when applied at concentrations above 100 M [172]. The same study also showed that although Ag NPs induced the differentiation of fibroblasts into myofibroblasts, which are often involved in the wound healing 124 process, there was a reduction in the cell number of fibroblasts in the presence of Ag NPs [172]. This was also in agreement with the observed cell death in HDFs (Section 4.2). Such cell typedependent sensitivity towards the toxic effects of Ag NPs appears to be related to the structural and functional aspects of the two cell types. HEKs are the predominant cell type of the epidermis, the protective outermost layer of the skin, whereas HDFs are found in the underlying dermis layer [173]. This could explain why Ag NPs can be widely used in topically applied antibacterial products without overwhelming safety issues [174-176]. Ag NPs are known to exert cytotoxic effects in a number of other cell types, supposedly due to generation of reactive oxygen species and release of Ag ions [177-179]. One of the reasons behind the cell type-specific cytotoxicity could be due to differences in antioxidant capacities of various cell types [180], and such intrinsic differences between cell types could in turn be a result of differential expression patterns of, for example, enzymes, membrane receptors and/or transporters. It is therefore essential to address such cell type-dependent responses towards drugs and chemicals, and to make sure that the most relevant cell type is employed in the development of in vitro models for the detection/prediction of organ-specific toxicity, including nephrotoxicity. 125 5.3 Validation of an in vitro method for the prediction of drug-induced nephrotoxicity As explained above, here I have developed an in vitro model for the prediction of drug-induced nephrotoxicity in humans. The model was based on HPTCs, which are the most relevant cell type for such application. Standard human and animal cell lines, HK-2 and LLC-PK1 cells were also used for comparison. The endpoints used were expression levels of IL-6 and IL-8, and 41 test compounds were used to validate the model. Results revealed that when three batches of HPTCs were used, the mean and median values for the major performance metrics (see Section 4.3) were highest and ranging between 0.76 and 0.85. These values suggested high predictivity of the model and it would be expected that 76% ~ 85% of the predictions made regarding the PTspecific toxicity of test compounds would be accurate. The high predictivity of this model could be mainly attributed to the use of HPTCs. My results showed that the major performance metrics were substantially lower (between 0.60 and 0.79) when HK-2 or LLC-PK1 cell lines were used. This could be probably due to the differences between expression patterns of drug transporters and metabolic enzymes between HPTCs and the cell lines, as described in the Introduction of this thesis. In addition, rat primary renal tubular or cortical cells and tissues also have long been used as a model cell type for nephrotoxicology [181-183], but similar to many other studies (as outlined in Introduction), very limited numbers of drugs were tested and no useful data on predictivity could be obtained regardless of which endpoint was used. However, one problem commonly associated with primary PTCs cultured in vitro is dedifferentiation. There is increasing evidence that after renal injury, surviving PTCs 126 dedifferentiate and expand the cell population again as a repair mechanism [184, 185]. A recent study has shown that such mechanisms are also activated in PTCs cultured in vitro [186], which are subject to physical disruption of renal cortical tissues during the isolation procedures. This is in agreement with my results which showed that the untreated PTCs in vitro already appeared to be in a “pre-injured” state, as indicated by high background expression levels of VIM, NGAL and KIM-1 (see Section 4.3). This could be one of the reasons why there was no significant nephrotoxicant-induced up-regulation of novel AKI biomarkers after treatment with nephrotoxicants in vitro, as found here in agreement with other results6. Currently there is no known in vitro cell culture method which can fully recapitulate the well differentiated in vivolike state of HPTCs. These deviations from physiological conditions are probably one of the main reasons for false-positive and false-negative results obtained with in vitro models. This problem cannot be solved with cell lines, as suggested by the lack of nephrotoxicant-induced upregulation of novel AKI biomarkers in a recently developed PTC line6. Typically, PTC-derived cell lines are also de-differentiated and drug-transporters are down-regulated [59, 187]. Another major contributing factor to the high predictivity of the current model is the choice of endpoints. The sensitivity was only at 42% when cell numbers were used as the endpoint with HPTCs after drug treatment, and the results showed that only strongly toxic compounds could induce significant cell death. It can be argued that higher sensitivity might be possible at later time points (e.g. 72 h), but specificity may also be compromised. Moreover, increasing the length of treatment times would substantially lower the throughput of the model, giving rise to a 6 Predict IV, fourth Annual Report. http://www.predict-iv.toxi.uni-wuerzburg.de/index.php?id=142794&no_cache=1&file=11758&uid=301608 127 disadvantage where rapid experimental procedures are usually preferred for large-scale screenings. A highly sensitive endpoint, on the contrary, should be able to detect less drastic toxic effects. In the case of IL-6 and IL-8, a broad variety of stimuli and injury mechanisms can induce their up-regulation in a large number of cell types [188-191]. This includes PT and PTderived cells, which have been shown to express both inflammatory cytokines in vivo and in vitro [64, 130-133]. Studies have also shown that IL-6 are IL-8 were increased in injured and diseased kidneys [80-82] and these pro-inflammatory cytokines play an important role in the pathophysiology of AKI (nephrotoxicant-induced or otherwise) [7, 98]. Furthermore, significant up-regulation of IL-6 was induced by nephrotoxicants in a PT-based kidney culture model [135]. These findings are in agreement with my results which showed that IL-6 and IL-8 were specifically up-regulated in vitro by PT-specific nephrotoxicants. One feature of this model is that test drugs were used at much higher concentrations as compared to their therapeutic doses and could be orders of magnitude higher than serum concentrations in vivo. For in vitro assays it is important to work at concentrations where a high sensitivity can be obtained without compromising specificity. The high sensitivity and specificity of this model show that the concentration range used was appropriate. Typically, it is recommended to work at concentrations of up to 100 times of Cmax [192]. This often exceeds the maximum concentration used in this study (1000 g/ml). However, despite the high predictivity of this in vitro model developed here, only binary information (positive and negative) on the PT-specific nephrotoxicity of test compounds can be derived from test results. Although clinical data are available for all of the compounds tested, 128 dose-related evaluation of the compounds requires more detailed quantitative correlation analysis of in vitro results. My preliminary data suggested that the current IL-6/IL-8-based endpoint appeared to be unsuitable for such analysis due to variations in magnitudes of gene expression levels (but not in overall expression patterns, data not shown). Such variations could be attributed to fluctuations in background expression levels of IL-6/IL-8 between different experiments (not shown). Also, the large step size between test concentrations do not allow accurate calculation of the IC50 values of test compounds. Due to low throughput of the qPCRbased method, it is inappropriate to include large numbers of concentrations in order to accurately determine the IC50 values. Therefore, for a comprehensive investigation on the dosedependent relationships between in vitro and clinical data, it is important to identify additional appropriate endpoints. For example, our group is also developing an alternative model based on high content screening (HCS), which allows fast screening of large numbers of test compounds at more refined concentration intervals. The current endpoint used in the HCS-based model is nuclear translocation of NF-B, which was also shown to be functionally linked to the upregulation of IL-6/IL-8 in Section 4.5. In addition, a green fluorescence protein (GFP)-IL-6 reporter cell line is also currently under development in order to improve the throughput of the interleukin-based model. The reporter cell line would also allow rapid screening of more concentrations as drug-induced GFP expression can be efficiently measured using a plate reader or the HCS system. Nevertheless, the binary information provided by the IL-6/IL-8-based endpoint would be useful for a fast and accurate pre-clinical classification of large amounts of drug candidates into the positive and negative groups, which would greatly facilitate subsequent stages of drug evaluation. 129 This would provide important early information on drug candidates and allow to either reject compounds or to follow up by including additional relevant tests. Reliable early predictions of PT-specific toxicity would also help in making well-informed decisions such as whether patients with pre-existing conditions (which increase the risk of nephrotoxicity) should be excluded from Phase II studies. For example, though tetracycline was consistently tested to be positive in this thesis, it usually has no prominent nephrotoxic effects per se, and only in patients with preexisting kidney disease it can induce AKI and ESRD [193, 194]. In addition, the analysis using threshold-based classifier method was not optimal. In order to improve this aspect, all qPCR data obtained were re-analyzed using computerized automated classification by machine learning, which lead to even higher predictivity [195]. This analysis method was established by our collaborators at the Bioinformatics Institute (BII, Singapore) using the same qPCR dataset as presented here. Nevertheless, even with a sub-optimal analysis method the predictivity of the models developed by me was already high when HPTCs were used. Though my results showed that HPTCs were a more appropriate cell type than standard cell lines for such an in vitro model, one major limitation is that primary cells are inevitably affected by inter-donor variability in their responses towards toxicants. Further, the source for normal human kidney tissue is limited. In view of these problems, stem cell-derived HPTC-like cells [9] appear to be the most promising alternative as discussed hereinafter. 130 5.4 Application of stem cell-derived HPTC-like cells As mentioned earlier, the use of HPTCs is often associated with limited cell sources, inter-donor variability, and functional changes due to passaging. In order to address these problems, human stem cell-derived HPTC-like cells were applied in the model described in Section 4.3. The differentiation protocol was developed based on hESCs by our group in collaboration with Prof. Jackie Ying’s group in IBN [9]. HPTC-like cells obtained with this protocol showed similar marker expression and functionality as HPTCs [9]. As previously mentioned in Section 1.3, a number of alternative protocols have also been developed more recently based on human or murine stem cells [88-93]. However, these protocols recapitulate embryonic kidney development and are not suitable for the generation of monocultures of HPTC-like cells. Therefore, here we employed the protocol as detailed in [9] for the generation of hESC- and hiPSC-derived HPTClike cells which were used for toxicity testing of compounds. The results based on hESC-derived cells were published [10], and this is in fact the first reported application of such stem cellderived HPTC-like cells. Before the hESC- and hiPSC-derived HPTC-like cells were used for toxicity testing, it is critical to assess their state of differentiation by thorough characterization of these cells. As a routine procedure in the lab, marker expression of differentiated cells was regularly analyzed with a panel of marker genes (including drug transporters, renal progenitor markers and markers of other renal cell types, see supplementary data, Figure S3) and compared with that of HPTCs. It is also crucial to monitor cell morphology, epithelium formation (Fig. S3) as well as functional properties. These features are also indications of proper differentiation and affect cellular responses to test compounds and were regularly tested by me and other lab members. 131 Here hESC- and hiPSC-derived HPTC-like cells were applied in the IL-6/IL-8-based in vitro model for the prediction of PT toxicity in humans. AUC (of the ROC curves) values of 0.80 and 0.76 were achieved with hiPSC- and hESC-derived cells, respectively, indicating somewhat lower overall predictivity than that obtained with HPTCs. Reduced overall predictivity was due to the relatively low sensitivity obtained with hESC-derived HPTC-like cells and relatively low specificity obtained with hiPSC-derived HPTC-like cells. These features could be explained by some biological differences compared to HPTCs. For example, hESC-derived HPTC-like cells expressed low levels of megalin [9, 10], which plays an essential role in cellular uptake of aminoglycosides such as gentamicin [25-27]. This is in agreement with the observation that though gentamicin was tested positive with HPTCs, false negative results were obtained with hESC-derived HPTC-like cells. On the other hand, hiPSC-derived HPTC-like cells showed low expression levels of efflux drug transporters (Huang et al., unpublished data). This could contribute to intracellular accumulation of test compounds, giving rise to false positives, which in turn translated into the observed relatively low specificity of hiPSC-derived HPTC-like cells. However, despite the biological differences between HPTCs and HPTC-like cells, the latter still performed better than widely used PT-derived cell lines in terms of almost all major performance metrics (only specificity of hiPSC-derived HPTC-like cells was lower compared to HK-2 cells). In terms of overall predictivity, the different cell types could be grouped and ranked as HPTCs > stem cell-derived HPTC-like cells > PT-derived cell lines. Though currently there seems to be a trade-off between the unlimited cell sources of stem cell-derived HPTC-like cells and their 132 predictivity, it is expected that the predictive performance HPTC-like cells can be further improved by optimizations of the differentiation procedures. Though my results showed that expression levels of IL-6 and IL-8 were highly reliable as predictive endpoints for PT toxicity in humans, it was necessary to address the underlying mechanisms of observed toxic effects. Here, well-established standard in vitro toxicity assays were performed and their results were compared with the IL-6/IL-8 model. The results showed that these assays generally had low sensitivity (~ 50%), and each assay identified a different subset of compounds (with overlaps) as positive. This could be due to the fact that a mechanismspecific endpoint, such as ATP depletion, could only detect the toxic effects mediated by this particular mechanism. For example, my results showed that tacrolimus, which reduces oxidative phosphorylation [196], induced ATP depletion in HPTCs. However, when the full list of 41 test compounds of various toxicity mechanisms was used, the sensitivity of ATP depletion assay was only 48%. This is also in agreement with a recent Pfizer study on organ-specific toxicity [45]. In order to account for variability in ATP depletion among different types of cells, hESC-derived HPTC-like cells were also tested, and similarly low sensitivity (50%) was obtained. Thus, the fact that better results were obtained with the IL6/IL8 assay was not specific for HPTC1. On the other hand, IL-6/IL-8 expression is commonly associated with activation of inflammatory pathways, which occurs in response to different nephrotoxicants both in vivo [7, 134, 197, 198] and in vitro (current thesis and [135]). It appears that such inflammatory reaction is a general response to nephrotoxicants in a relatively unspecific manner, and thus explains why falsenegative rates remained low when IL-6/IL-8 expression levels were used as the endpoints. As 133 such, it would be highly interesting to further explore drug-induced inflammatory pathways in PTCs in order to identify potential alternative endpoints as well as to elucidate the underlying cellular and molecular mechanisms mediating the expression of IL-6 and IL-8. 134 5.5 The role of the NF-B pathway in nephrotoxicant-induced up-regulation of IL-6/IL-8 Here, I investigated the connection between nephrotoxicant-induced nuclear translocation of NFB p65 and up-regulation of IL-6 and IL-8 in human renal PTCs. As mentioned in the Introduction section, it is well established that such cytokines mediate inflammatory or immunologic responses which often play an important role in the pathogenesis of ischemic kidney injury [98, 199], and they are often up-regulated in injured or diseased kidneys [80-82]. In fact, these interleukins, along with IL-18 had been proposed as potential novel biomarkers for the detection of drug-induced nephrotoxicity [83]. Also, interleukin expression is often regulated by the NF-B pathway in different cell types [200-204]. Studies have also shown that the promoter regions of IL-6 and IL-8 contained binding sites for NF-B, and these sites were essential for activating IL-6 and IL-8 in different cell types [144-146]. My RNAi results suggested that the lipofectamine-based transfection of NF-B p65-specific siRNA was more efficient in HK-2 cells, whereas in HPTCs the cellular NF-B p65 level was only reduced moderately. This is in agreement with the common knowledge that primary cells are intrinsically more resistant to foreign genetic materials as compared to established cell lines, possibly due relatively low proliferation rate of primary cells [205] or active degradation of nucleic acids [206]. Efficient transfection of primary cells often requires more rigorous transfection methods such as eletroporation and pre-treatment of cells [207, 208]. The lack of siRNA-induced silencing effects on IL-6 and IL-8 expression could be due to the possibility that the residual amount of NF-B was sufficient to induce the downstream process. 135 This is in agreement with another study which showed that the activation of the NF-B signaling pathway did not correlate with the amount of NF-B present in the cells [209]. In addition, while RNAi did not lead to a reduction in puromycin-induced IL-8 expression, IL-6 was even further up-regulated as compared to non-target siRNA controls in both HK-2 cells and HPTCs after transfection with siRNA. This unexpected result could be possibly explained by the interaction between different regulators of IL-6 expression. For instance, it has been shown that CCAAT/enhancer-binding protein beta (CEBPB, also known as nuclear factor for interleukin-6 (NF-IL-6)) could form a complex with the p50/p65 heterodimer of NF-B [210] and the ternary complex had synergistic effects on the expression of inflammatory cytokines such as IL-6 [144]. Another example is the crosstalk between the Smad2/3 pathway and the NF-B pathway, where inhibition of the latter might have led to disinhibition of the former, leading to up-regulation IL-6 but not IL-8 [211, 212]. As such, it would be oversimplified to state that a general reduction in cellular levels of NF-B p65 could directly translate to inhibition of nephrotoxicant-induced IL6/IL-8 up-regulation. For future work, it would be interesting to further investigate the extensive network of different cellular pathways involved. However, these problems were absent when IKK inhibitors were used, suggesting the possibility that such interactions between different cellular pathways were more relevant in the cytoplasm.In case of inhibitors, the reduction in nuclear translocation of NF-B p65 was obvious and wellassociated with the observed negative effects on the up-regulation of IL-6 and IL-8 in both HK-2 cells and HPTCs. These results are in agreement with studies which demonstrated antiinflammatory effects of the BAY 11-7082 and BAY 11-7085 in various cell types [213-215]. In addition, it is interesting to note that while IKK inhibitors only led to moderate negative effects 136 in HK-2 cells, puromycin-induced up-regulation of IL-6 and IL-8 was completely abolished in HPTCs. This observation suggested that HPTCs were more susceptible to the inhibitory effects of the BAY compounds. These results were also in agreement with the observation that BAY 117085 induced a larger decrease in the nuclear NF-B p65/cytoplasmic NF-B p65 ratio in puromycin-treated HPTCs as compared to puromycin-treated HK-2 cells. Though different drugs and chemicals would be expected to stimulate PTCs via vastly different mechanisms, it appears that a large proportion of these compounds active the NF-B pathway, leading to up-regulation of the inflammatory cytokines IL-6 and IL-8. This is currently further evaluated in another study carried out by our group. Our recent findings indicated that when using nuclear translocation of NF-B p65 as an alternative endpoint, high predictivity of human PT toxicity could be achieved (Xiong et al., unpublished data). In this context, my results here provided essential evidence for the connection between the different endpoints by investigating the cellular and molecular mechanism underlying the high predictivity of the IL-6/IL-8-based model. Together, the results showed that the in vitro models established in my thesis allowed for the first time accurate prediction of drug-induced nephrotoxicity in humans. Follow-up projects address currently the further development of the approaches. 137 6. Conclusions This thesis describes the development of human in vitro models for predicting organ-specific toxicity. I first demonstrated that the TCPS was the most suitable culturing substrate that supported optimal performance of HUVEC and HPTC due primarily to its high stiffness (as shown in Section 4.1), and therefore TCPS-based platforms were the most appropriate for developing in vitro models based on these cell types. HUVECs were used as the major cell type in a model for the testing toxicity of hemostatic agents (Section 4.2). For comparison, other relevant skin cell types such as HDF and HEK were also treated with the same test compounds. Cell viability results showed that HUVECs were more sensitive to cytotoxic effects of layered clays with hemostatic properties, highlighting the importance of cell-type specificity in toxicity tests. Using this model, I have demonstrated that MCF-26 could be a much safer hemostatic material than currently used layered clays. On the other hand, as the main focus of this thesis, I developed a first human in vitro model for the prediction of drug-induced nephrotoxicity. The model (as described in Section 4.3) employs HPTC as the major cell type and up-regulation of mRNA expression levels (as determined by qPCR) of IL-6 and IL-8 has been identified as the endpoint. Predictive performance analysis on the qPCR results was based a thresholding method, and accuracy levels of > 80% were obtained with predictions made with respect to 41 well-characterized nephrotoxic or non-nephrotoxic compounds. Predictivity obtained with HPTC was shown to be higher than that obtained with immortalized renal proximal tubular cell lines. 138 In addition, I also applied stem cell-derived HPTC-like cells as an alternative cell type in this model and similar predictivity was achieved (> 75%, Section 4.4). Both hESC- and hiPSCderived HPTC-like cells were used and the results were comparable to those obtained with HPTC. Furthermore, predictivity obtained with the IL-6/IL-8 endpoint (in both HPTC and hESCderived HPTC-like cells) was shown to be substantially higher in comparison to commonly used standard toxicity endpoints. This is also the first successful application of such stem-cell derived renal cells, and these cells could also be highly interesting in the field of in vitro toxicology. Lastly, I have also established that drug-induced up-regulation of IL-6/IL-8 could be functionally correlated to nuclear translocation of NF-B p65 (Section 4.5), a subunit of the NF-B transcription factor which mediates the expression of inflammatory cytokines. This result highlights that the in vitro model developed here could recapitulate the physiological scenario where inflammatory responses often play a role in the pathogenesis of drug-induced AKI. 139 7. Recommendations for future research As discussed earlier, this model was developed based on a sub-optimal data analysis method (thresholding method), which still resulted in high predictivity. An improved, automated analysis method based on machine learning has already been developed by our collaborators at BII [195], and even higher predictivity was achieved with the same qPCR data set as that used in this thesis. Another area of improvement to this model is its throughput. To address this problem, my current work focuses on the development of a GFP-IL-6 reporter cell line, which would allow rapid screening of large number of compounds based on GFP expression that is supposedly linked to IL-6 expression levels. In principle, a similar GFP-IL-8 reporter cell line can also be developed and used in combination with the GFP-IL-6 reporter cells. This reporter system would also be compatible with HCS. Furthermore, it would also be interesting to look at alternative endpoints. My colleagues (S. Xiong, K. G. Eng, and F. Hussain, unpublished results) are currently developing an HCS-based model using nuclear translocation of NF-B p65 as endpoint. Preliminary results showed high correlation with those obtained by the model described in this thesis. For mechanistic insights into the nephrotoxic effects of compounds, more injury mechanism-specific endpoints should also be investigated. Finally, improvements can also be made to the stem cell-derived cell models described here. My colleagues have recently improved the differentiation protocol such that shorter time is required for differentiation and differentiated cells also showed proper proximal tubular cell functions (Kandasamy, Chuah et al., unpublished). On the other hand, it would also be interesting to look 140 into patient-specific iPSC-derived renal-like cells as a model for studying genetically predisposed susceptibility to nephrotoxicity of drugs and chemicals. Together, these future research areas would address many limitations of the current qPCR-based model and broaden the spectrum of research, which would provide mechanistic insights into drug-induced nephrotoxicity as well as pharmacogenetic implications in clinical practice. 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Appendices Appendix i: List of abbreviations 3D: Three-dimensional A*STAR: Agency for Science, Technology and Research Ag NP: Silver nanoparticle AKI: Acute kidney injury AQP: Aquaporin ATCC: American Type Culture Collection ATP: Adenosine triphosphate AUC: Area under the curve BCA: Bicinchoninic acid BCRP: Breast cancer resistance protein BSA: Bovine serum albumin CEBPB: CCAAT/enhancer-binding protein beta CG: Cover glass CKD: Chronic kidney disease DAPI: 4', 6-diamidino-2-phenylindole DMEM: Dulbecco’s Modified Eagles’ Medium DMSO: Dimethyl sulfoxide DOPA: 3, 4-dehydroxy-L-phenylalanine; levadopa ECM: Extracellular matrix ECVAM: The European centre for the validation of alternative test methods ELISA: Enzyme-linked immunosorbernt assay ESC: Embryonic stem cell ESRD: End stage renal disease FDA: United States Food and Drug Administration FITC: Fluorescein isothiocyanate FN: False negative FP: False positive GAPDH: Glyceraldehyde 3-phosphate dehydrogenase GFP: Green fluorescent protein GGT: Gamma-glutamyl transferase GLUT: Glucose transporter GSH: Glutathione HCS: High content screening hESC: Human embryonic stem cell hiPSC: Human induced pluripotent stem cell HDF: Human primary dermal fibroblast HEK: Human primary epidermal keratinocyte HK-2: Human kidney-2 HPTC: Human primary renal proximal tubular cell HPV: Human papilloma virus 162 HUES: Human embryonic stem cell HUVEC: Human primary umbilical vein endothelial cell IBN: Institute of Bioengineering and Nanotechnology ICU: Intensive care unit IB: Inhibitor of kappa B IKK: Inhibitor of IB kinase IL: Interleukin iPSC: Induced pluripotent stem cell KIM-1: Kidney injury molecule-1 LDH: Lactate dehydrogenase LLC-PK1: Lilly Laborateries Cell-porcine kidney 1 MCAM: Melanoma cell adhesion molecule MCF-26: Mesocellular foam-26 MDR: Multidrug resistance MEG: Megalin MES: 2-(N-morpholino)ethanesulfonic acid NF-IL-6: Nuclear factor for interleukin-6 NF-B: Nuclear factor kappa B NGAL: Neutrophil gelatinase-associated lipocalin NLS: Nuclear localization sequence NPV: Negative predictive value NRU: Neutral red uptake NTUC: National Trades Union Congress NUHS: National University Health System NUS: National University of Singapore NUS-IRB: National University of Singapore, Institutional Review Board OAT: Organic anion transporter OCT: Organic cation transporter PBS: Phosphate-buffered saline PC: Polycarbonate PE: Polyethylene PECAM: Platelet endothelial cell adhesion molecule PLA: Poly(lactic acid) PPV: Positive predictive value PT: Proximal tubule PTC: Proximal tubular epithelial cells PVDF: Polyvinylidene fluoride qPCR: Quantitative real-time polymerase chain reaction REACH: Registration, evaluation, authorization and restriction of chemicals REGM: Renal epithelial growth medium RIPA: Radioimmunoprecipitation assay RNAi: RNA interference ROC: Receiver operating characteristic SDS: Sodium dodecyl sulfate TBS: Tris-buffered saline 163 TCPS: Tissue culture polystyrene TN: True negative TNF-: Tumor necrosis factor alpha TP: True positive TEER: Transepithelial electrical resistance TX: Thermanox VIM: Vimentin vWF: von Willebrand factor ZO-1: Zonula occludens-1 164 Appendix ii: Supplementary data Washed Unwashed Figure S1. Adhesion of MCF-26 to HUVECs. Images were captured with the CytoViva® system after fixation. a) Identical image as originally Figure 8c (see Section 4.2). HUVECs were washed with 1X PBS after 10 min exposure to MCF-26 at 1 mg/ml. b) Unwashed HUVECs after 10 min exposure to MCF-26 at 1 mg/ml. Scale bars: 100 m. 165 166 Figure S2. Marker gene expression determined by qPCR in HPTC 1-4. The relative expression levels of 31 marker genes (x-axis) are shown as mean percentage (± s.d., n = 3) of GAPDH expression (y-axis), Epithelial, HPTC-specific, renal and renal injury markers include: aquaporin-1 (AQP1), aminopeptidase N (CD13), zonula occludens 1 (ZO-1), N-cadherin (N-CAD), E-cadherin (E-CAD), -glutamyl transferase (GGT), 25-hydroxyvitamin D3 1α-hydroxylase (VIT D3), glucose transporter 5 (GLUT5), Na+/K+ ATPase, kidney-specific cadherin (KSP-CAD), neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), Wilms’ tumor gene 1 (WT1), paired box gene 2 (PAX2), multidrug resistance gene 1 (MDR1), megalin (MEG), Na+HCO3- co-transporter 1 (NBC1), organic anion transporter 1 (OAT1), OAT3, organic cation transporter 1 (OCT1), organic cation/carnitine transporter 2 (OCTN2), proton-coupled peptide transporter 2 (PEPT2),sodium-dependent glucose co-transporter 2 (SGLT2). Markers specific for other parts of the nephron include odocalyxin-like (PODXL, glomerulus), chloride channel Kb (CLCNKB, distal nephron), thiazide-sensitive sodium-chloride co-transporter (NCCT, distal tubule), Na+/K+/2Cl- cotransporter (NKCC2, thick ascending loop of Henle), uromodulin (UMOD, thick ascending loop of Henle and distal convoluted tubules) and AQP3 (collecting duct). Markers for trans- and de-differentiation: αsmooth muscle actin (SMA) and vimentin (VIM; bottom). Experiments performed by Dr. Karthikeyan Kandasamy. Figure adapted from [8] (Supplementary Information). Reproduced by permission of The Royal Society of Chemistry. 167 Figure S3. Marker gene expression determined by qPCR in HPTC and hESC-derived HPTC-like cells (bar charts). The relative expression levels of epithelial and HPTC-specific marker genes (x-axis) are shown as mean percentage (± s.d., n = 3) of GAPDH expression (y-axis). Asterisks indicate significant (P < 0.05) differences (which were also >3-fold) between HPTC and HPTClike cells. Experiments were performed by Dr. Karthikeyan Kandasamy. Marker expression was also shown by immunostaining (green; cell nuclei, blue) in HPTC-like cells. Scale bars: 100 m. Figure adapted with permission from [10]. Copyright 2014 American Chemical Society. 168 Table S1. Adapted with permission from [10] (Supplementary Information). Copyright 2014 American Chemical Society. Comp. No. 1 2 3 4 5 6 7 8 9 10 11 Clinical Effects and Mechanistic Insights Acute tubular necrosis, renal tubular dysfunction, PT toxicity; accumulation in PTCs due to uptake by megalin / cubilin endocytotic receptors (this compound is an aminoglycoside antibiotic) Acute tubular necrosis, renal tubular dysfunction, PT toxicity (this compound is an aminoglycoside antibiotic) Inflammation of the tubules, acute tubular necrosis and acute tubulo-interstitial nephritis most frequently observed; multiple renal tubular transport abnormalities; common in intermittent or interrupted therapy suggesting immunological component Tubular damage by the compound in patients with preexisting kidney disease; uptake by organic anion transporters specifically expressed by PTCs Degeneration and/or regeneration of tubular epithelium and multifocal atrophy of glomeruli; PT necrosis; increased urinary albumin and NGAL showing dysfuncion of PT in addition to glomerular injury Cephalosporins are associated with acute tubular necrosis; uptake by PTCs by organic anion transporters PT cell necrosis; compound is eliminated by PTCs in addition to glomerular filtration Necrosis and apoptosis of renal tubular cells; inflammatory response; uptake by PTCs through CTR1 and OCT2 PT dysfunction and persistent Fanconi syndrome; uptake by OCT2 into PTCs; nephrotoxicity probably due to metabolite chloroacetaldehyde (CAA) Acute renal failure, acute tubular necrosis, PT dysfunction, Fanconi syndrome; uptake of compound by PTCs, degeneration of PTCs Acute and chronic renal failure; damage of PT due to their high reabsorptive activity during urinary excretion of the compound Direct Human PT Refs. Toxicity + [1, 2, 27] + [1, 2, 49, 216] Induces inflammatory [217-220] response of PT + [193, 221, 222] + [223-226] + [140, 141] + [227, 228] + [20, 229, 230] + [231-233] + [234236]; [237] + [238-240] 169 12 13 14 15 16 17 18 19 20 21 22 23 Acute toxicity of this compound manifests as nephrotoxicity; strongest enrichment in kidney, acute and chronic renal failure, Fanconi syndrome, PT necrosis, compound binds to metal binding protein in PT and remains bound for long time periods Tubular dysfunction; Cd2+ is enriched and bound by metallothioneins in PT Degeneration of kidney tubule epithelium, kidney failure; uptake by copper transporter CTR1 that is specifically expressed by PTCs Acute renal dysfunction, vacuolar degeneration of renal tubular epithelial cells, distal tubules more strongly affected than PT; no or minor glomerular changes Acute tubular necrosis, PT degeneration, accumulation of gold in mitochondria of PTCs Acute and chronic renal failure; cells of PT most severely affected; nuclear inclusion bodies in PTCs; tubular disruption and dysfunction; renal effects require relatively high and persistent exposure Acute tubular necrosis, acute kidney injury; direct toxic effects on PTCs associated with mitochondrial and lysosomal injury Prerenal injury, thrombotic microangiopathy, tubular toxicity; epithelial vacuolization and direct toxic effects on renal PTCs; altered intraglomerular hemodynamics; tubular epithelial cells, vascular endothelial cells, arteriolar myocytes, and interstitial fibroblasts are all targets for cyclosporine and tacrolimus nephrotoxicity Prerenal injury, thrombotic microangiopathy; epithelial vacuolization and direct toxic effects on renal PTCs; altered intraglomerular hemodynamics, chronic interstitial nephritis; tubular epithelial cells, vascular endothelial cells, arteriolar myocytes, and interstitial fibroblasts are all targets for cyclosporine and tacrolimus nephrotoxicity Cytoplasmic vacuolization of proximal convoluted tubules followed by necrosis; uptake into PTCs by organic anion transporters PT dysfunction associated with acute kidney injury or chronic kidney disease; acute toxic tubular necrosis targeting PT; mitochondrial injury in PTCs; compound is secreted into filtrate by PTCs Nephrotoxicity manifests in humans as acute + [241-243] + [244-247] + [230, 248] + [249, 250] + [216, 251, 252] + [216, 253] + [254-256] + [1, 139] + [139] + [216, 257-259] + [1, 2, 260, 261] NE [2, 262, 170 24 25 26 27 28 29 30 31 32 33 interstitial nephritis; the exact incidence of nephrotoxic effects unclear is when the pure compound is used alone; nephrotoxicity is often associated with preexisiting renal dysfunction or concomitant intake of other nephrotoxic agents Epidemiologic studies revealed that habitual use of phenacetin is associated with the development of chronic renal failure and ESRD; withdrawn from the market; pathophysiology of renal damage unclear; direct PT toxicity unlikely Safe when used at therapeutic doses; renal failure secondary to acetaminophen poisoning is rare (1-2% of patients) and becomes in most cases evident after hepatotoxicity; acute nephrotoxicity manifests as acute tubular necrosis and significant reductions in glomerular filtration rate; chronic interstitial nephritis and renal papillary necrosis/calcification may occur when used chronically in high doses Usually safe at therapeutic doses; excessive abusive consumption can be associated with chronic and/or acute renal papillary necrosis, chronic or acute interstitial nephritis, altered intraglomerular hemodynamics, glomerulonephritis, renal tubular acidosis (proximal or distal). 263] Direct toxicity unlikely PT Renal pathophysiology unclear and chronic effects controversially discussed Pathophysiology of tubular effects is unclear; probably secondary Immune-mediated interstitial inflammation; NE nephrocalcinosis/nephrolithiasis in premature infants Lithium Nephrogenic diabetes insipidus due to effects on nephrotox. collecting duct; chronic tubulointerstitial nephropathy primarily with cysts originating from distal tubules or collecting targets distal ducts; necrosis of distal convoluted tubules; distal and tubule dilatation and microcyst formation; chronic collecting interstitial nephritis, glomerulonephritis tubules It is claimed in the literature that lindane is nephrotoxic. Nephrotoxic effects were observed in NE rats. The effects observed in rats are probably not relevant for humans. Calcium oxalate crystal-induced renal tubular damage that can be associated with acute renal failure Thrombotic microangiopathy - No direct relationship to renal damage, but rare cases of renal dysfunction observed No tubule-toxic effects; rare cases of immune- - [264, 265] and refs. therein [2, 265271] [2, 264, 272] [1, 273] [2, 274276] [277, 278] [279-281] [1, 282, 283] [284] [1, 285171 mediated tubulointerstitial nephritis or crystal-induced nephropathy leading to acute renal failure, reversible upon discontinuation 288] 34 No nephrotoxic effects reported - 35 36 No nephrotoxic effects reported No nephrotoxic effects reported - 37 No nephrotoxic effects reported - 38 No nephrotoxic effects reported - 39 No nephrotoxic effects reported - 40 No nephrotoxic effects reported - 41 Usually no direct negative effects on the kidney; in rare cases acute tubular necrosis secondary to rhabdomyolysis [289]; http://ww w.drugs.c om/pro/ri bavirin.ht ml [290-292] [293] [294]; http://ww w.fda.gov /ICECI/E nforceme ntActions /Warning Letters/uc m201435 .htm [295-297] [298]; http://ww w.drugs.c om/sfx/li othyronin e-sideeffects.ht ml [299, 300]; http://che m.sis.nlm .nih.gov/c hemidplu s/cas/561 80-94-0 [2, 301]; http://ww w.drugs.c om/sfx/at orvastatin -sideeffects.ht 172 ml 173 Tables S2 – S11 Expression levels of IL-6 and IL-8. Three different batches of HPTC (1-3) as well as HK-2 and LLC-PK1 cells were exposed to the 41 test compounds at concentrations of 1 g/ml, 10 g/ml, 100 g/ml and 1000 g/ml (vehicle control: 0 g/ml drug concentration). The vehicle control contained the respective vehicle for the drug tested (see Materials and Methods). The tables list the levels of IL-6/IL-8 expression determined by qPCR. The numbers show the mean fold expression +/- s.d. (n = 3) relative to the vehicle control. In some cases the expression levels were not determined (ND) due to massive cell death. The highest levels of IL-6/IL-8 expression that were determined for a given drug and cell type/batch combination when the whole range of drug concentrations was tested (1 g – 1000 g) are highlighted (bold). These highest expression values obtained with a specific drug and cell type/batch combination were entered into Tables 5 and 6 in the main thesis. Adapted from [8] (Supplementary Information). Reproduced by permission of The Royal Society of Chemistry. 174 Table S2 Compound 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 0 1.0 ± 0.0 1.0 ± 0.2 1.0 ± 0.2 1.0 ± 0.0 1.0 ± 0.1 1.0 ± 0.2 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.3 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.3 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.2 1.1 ± 0.6 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.0 1.0 ± 0.2 1.0 ± 0.3 1.0 ± 0.1 1.1 ± 0.3 1.1 ± 0.5 1.0 ± 0.0 1.0 ± 0.2 1.0 ± 0.2 1.0 ± 0.1 1.0 ± 0.2 1.0 ± 0.1 1.1 ± 0.2 HPTC 1, IL-6 Expression 1 10 100 1.0 ± 0.0 1.2 ± 0.0 2.9 ± 0.1 1.3 ± 0.1 1.5 ± 0.1 1.8 ± 0.3 3.2 ± 0.5 3.1 ± 0.2 1.7 ± 0.9 1.1 ± 0.1 1.4 ± 0.9 6.3 ± 0.7 1.0 ± 0.1 8.5 ± 1.0 5.0 ± 0.4 0.9 ± 0.0 1.0 ± 0.0 0.7 ± 0.1 1.4 ± 0.2 1.7 ± 0.3 2.6 ± 0.1 2.9 ± 0.6 3.8 ± 0.6 1.0 ± 0.2 1.5 ± 0.1 1.7 ± 0.2 1.6 ± 0.1 2.0 ± 0.5 2.4 ± 0.2 3.1 ± 0.7 1.1 ± 0.1 0.8 ± 0.1 0.4 ± 0.0 1.1 ± 0.3 1.1 ± 0.2 1.0 ± 0.4 1.5 ± 0.1 6.6 ± 0.5 2.9 ± 1.5 1.2 ± 0.2 1.4 ± 0.4 0.9 ± 0.1 0.8 ± 0.1 1.0 ± 0.4 1.2 ± 0.0 1.0 ± 0.1 1.7 ± 0.3 0.8 ± 0.1 1.4 ± 0.3 1.8 ± 0.6 2.4 ± 0.3 0.0 ± 0.0 0.3 ± 0.1 0.6 ± 0.1 7.7 ± 1.6 7.7 ± 0.4 11.7 ± 3.5 0.9 ± 0.2 1.1 ± 0.2 0.9 ± 0.1 1.1 ± 0.3 1.1 ± 0.1 0.6 ± 0.1 1.5 ± 0.2 1.6 ± 0.1 1.5 ± 0.1 1.3 ± 0.0 1.4 ± 0.2 1.3 ± 0.3 1.1 ± 0.2 1.2 ± 0.1 1.1 ± 0.2 1.2 ± 0.1 1.3 ± 0.0 1.0 ± 0.1 0.5 ± 0.1 0.8 ± 0.1 0.9 ± 0.1 0.3 ± 0.0 0.4 ± 0.0 0.2 ± 0.0 0.7 ± 0.1 0.4 ± 0.2 0.4 ± 0.0 0.9 ± 0.0 1.0 ± 0.3 1.0 ± 0.2 0.9 ± 0.1 1.0 ± 0.2 1.0 ± 0.1 1.3 ± 0.1 1.7 ± 0.4 1.1 ± 0.1 1.3 ± 0.1 1.1 ± 0.1 1.0 ± 0.0 1.6 ± 0.2 1.4 ± 0.2 2.3 ± 0.2 1.5 ± 0.1 0.9 ± 0.1 0.3 ± 0.0 1000 16.9 ± 0.2 8.0 ± 0.9 38.9 ± 3.0 1.0 ± 0.1 6.8 ± 1.3 3.9 ± 0.1 3.6 ± 0.8 ND 0.8 ± 0.1 2.4 ± 0.5 3.0 ± 0.1 ND ND 10.4 ± 2.9 1.7 ± 0.5 10.0 ± 1.3 3.5 ± 0.4 1.7 ± 0.2 24.6 ± 5.7 0.6 ± 0.2 3.9 ± 0.7 0.8 ± 0.0 0.8 ± 0.2 0.1 ± 0.0 0.2 ± 0.0 2.6 ± 0.8 1.7 ± 0.4 0.1 ± 0.0 0.4 ± 0.0 0.7 ± 0.1 0.7 ± 0.3 0.9 ± 0.1 4.7 ± 0.6 0.3 ± 0.0 175 35 36 37 38 39 40 41 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.0 1.1 ± 0.3 1.0 ± 0.2 1.0 ± 0.4 1.0 ± 0.2 1.5 ± 0.1 0.9 ± 0.0 1.2 ± 0.1 1.1 ± 0.1 1.2 ± 0.1 1.6 ± 0.2 1.0 ± 0.3 1.6 ± 0.2 0.8 ± 0.0 1.0 ± 0.0 1.8 ± 0.0 0.9 ± 0.1 1.0 ± 0.1 0.5 ± 0.1 1.7 ± 0.2 0.9 ± 0.1 0.8 ± 0.0 0.4 ± 0.0 0.4 ± 0.1 0.9 ± 0.1 0.7 ± 0.2 1.4 ± 0.1 0.8 ± 0.1 0.1 ± 0.0 0.3 ± 0.1 3.4 ± 1.0 0.9 ± 0.0 ND 176 Table S3 Compound 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 0 1.0 ± 0.3 1.0 ± 0.2 1.0 ± 0.4 1.0 ± 0.0 1.0 ± 0.0 1.0 ± 0.3 1.0 ± 0.0 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.2 1.0 ± 0.0 1.0 ± 0.0 1.0 ± 0.0 1.0 ± 0.2 1.0 ± 0.2 1.0 ± 0.0 1.0 ± 0.1 1.1 ± 0.5 1.1 ± 0.4 1.0 ± 0.0 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.3 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.2 1.1 ± 0.4 1.0 ± 0.0 1.0 ± 0.3 1.0 ± 0.2 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.3 1.0 ± 0.1 1.0 ± 0.2 HPTC 1, IL-8 Expression 1 10 100 1000 0.9 ± 0.0 0.8 ± 0.1 0.7 ± 0.0 8.6 ± 1.3 1.2 ± 0.1 1.2 ± 0.1 1.5 ± 0.3 8.9 ± 1.2 6.9 ± 1.0 6.0 ± 0.7 4.1 ± 1.4 110.8 ± 39.0 0.9 ± 0.0 1.2 ± 0.1 1.5 ± 0.1 0.8 ± 0.0 0.3 ± 0.0 9.5 ± 0.6 8.8 ± 0.9 4.8 ± 0.5 0.9 ± 0.1 1.0 ± 0.1 2.2 ± 1.1 2.3 ± 0.2 1.2 ± 0.2 1.2 ± 0.2 2.4 ± 0.2 2.2 ± 0.4 13.6 ± 2.0 20.1 ± 2.2 13.4 ± 3.2 ND 1.4 ± 0.1 1.5 ± 0.2 1.5 ± 0.1 0.9 ± 0.2 2.6 ± 1.7 2.3 ± 0.1 5.9 ± 0.8 16.3 ± 4.6 1.1 ± 0.1 1.6 ± 0.2 0.8 ± 0.0 6.0 ± 0.1 1.6 ± 0.3 6.1 ± 0.5 11.9 ± 6.2 ND 1.9 ± 0.1 2.4 ± 0.1 9.7 ± 2.5 ND 1.4 ± 0.2 1.7 ± 0.4 3.6 ± 0.3 7.5 ± 4.4 0.8 ± 0.1 1.4 ± 0.3 1.5 ± 0.1 2.9 ± 1.7 0.8 ± 0.0 2.2 ± 0.7 1.4 ± 0.4 0.5 ± 0.1 1.2 ± 0.3 1.5 ± 0.2 2.6 ± 0.4 3.3 ± 0.1 0.5 ± 0.1 3.8 ± 1.0 2.2 ± 0.4 1.3 ± 0.4 3.5 ± 0.8 2.8 ± 0.2 3.9 ± 1.3 2.7 ± 1.1 1.0 ± 0.2 1.1 ± 0.2 2.6 ± 1.9 5.6 ± 1.3 1.3 ± 0.4 1.1 ± 0.2 0.7 ± 0.1 2.3 ± 0.7 4.1 ± 1.0 4.3 ± 0.4 4.0 ± 0.5 1.3 ± 0.0 1.3 ± 1.0 1.5 ± 0.3 1.6 ± 0.3 1.1 ± 0.2 1.2 ± 0.1 1.3 ± 0.1 1.0 ± 0.2 0.0 ± 0.0 1.4 ± 0.2 1.5 ± 0.1 1.1 ± 0.1 0.4 ± 0.1 0.3 ± 0.0 0.5 ± 0.1 0.2 ± 0.0 3.1 ± 1.4 0.5 ± 0.0 0.4 ± 0.0 0.2 ± 0.0 1.9 ± 0.5 0.5 ± 0.1 0.5 ± 0.1 0.2 ± 0.0 0.2 ± 0.0 1.0 ± 0.1 1.1 ± 0.3 1.1 ± 0.4 0.7 ± 0.1 1.0 ± 0.2 1.1 ± 0.1 1.0 ± 0.2 0.9 ± 0.0 1.1 ± 0.1 1.3 ± 0.1 1.2 ± 0.0 1.2 ± 0.1 0.8 ± 0.0 1.2 ± 0.0 1.0 ± 0.0 0.9 ± 0.0 1.5 ± 0.2 0.9 ± 0.1 0.6 ± 0.1 5.3 ± 1.1 0.4 ± 0.0 1.4 ± 0.1 0.9 ± 0.0 0.6 ± 0.1 1.9 ± 0.1 1.7 ± 0.1 2.0 ± 0.1 2.0 ± 0.1 177 36 37 38 39 40 41 1.0 ± 0.0 1.0 ± 0.0 1.1 ± 0.3 1.0 ± 0.1 1.0 ± 0.0 1.0 ± 0.1 0.7 ± 0.1 1.5 ± 0.3 0.8 ± 0.0 1.8 ± 0.1 1.5 ± 0.1 1.2 ± 0.3 0.8 ± 0.1 1.3 ± 0.1 1.3 ± 0.1 1.3 ± 0.0 0.8 ± 0.0 0.6 ± 0.1 1.0 ± 0.0 0.7 ± 0.0 1.3 ± 0.1 0.9 ± 0.1 0.8 ± 0.1 3.6 ± 1.2 1.1 ± 0.1 0.0 ± 0.0 0.3 ± 0.0 1.6 ± 0.5 0.7 ± 0.1 ND 178 Table S4 Compound 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 0 1.0 ± 0.0 1.0 ± 0.0 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.0 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.2 1.0 ± 0.0 1.0 ± 0.0 1.0 ± 0.0 1.0 ± 0.0 1.0 ± 0.1 1.0 ± 0.0 1.0 ± 0.1 1.0 ± 0.0 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.0 1.1 ± 0.4 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.0 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.2 1.0 ± 0.0 1.0 ± 0.1 HPTC 2, IL-6 Expression 1 10 100 0.9 ± 0.0 1.0 ± 0.0 1.0 ± 0.0 1.1 ± 0.1 0.9 ± 0.0 1.2 ± 0.1 1.7 ± 0.2 1.8 ± 0.1 1.2 ± 0.0 0.8 ± 0.1 1.0 ± 0.0 2.0 ± 0.1 1.2 ± 0.3 2.2 ± 0.2 49.9 ± 3.2 1.1 ± 0.0 1.1 ± 0.1 1.6 ± 0.1 2.1 ± 0.1 2.5 ± 0.1 4.8 ± 0.6 8.3 ± 0.2 9.3 ± 0.5 27.9 ± 0.8 0.9 ± 0.1 1.3 ± 0.3 1.2 ± 0.1 0.9 ± 0.1 1.4 ± 0.1 4.4 ± 0.6 1.1 ± 0.1 0.2 ± 0.0 1.6 ± 0.0 0.8 ± 0.0 0.9 ± 0.0 1.6 ± 0.3 1.3 ± 0.0 12.9 ± 0.6 3.2 ± 0.1 1.2 ± 0.1 1.1 ± 0.0 5.4 ± 0.1 1.3 ± 0.1 1.1 ± 0.1 1.6 ± 0.2 1.1 ± 0.0 1.2 ± 0.1 3.1 ± 0.4 0.9 ± 0.0 1.1 ± 0.0 2.0 ± 0.4 0.8 ± 0.0 0.2 ± 0.0 0.8 ± 0.0 1.4 ± 0.1 0.9 ± 0.0 37.6 ± 0.7 0.8 ± 0.1 3.4 ± 0.7 0.9 ± 0.0 0.9 ± 0.0 0.8 ± 0.1 0.3 ± 0.1 0.7 ± 0.0 0.7 ± 0.0 0.6 ± 0.0 1.4 ± 0.2 1.2 ± 0.2 1.1 ± 0.2 0.6 ± 0.0 0.8 ± 0.1 0.6 ± 0.0 1.4 ± 0.1 1.3 ± 0.2 1.6 ± 0.4 1.6 ± 0.0 1.7 ± 0.1 4.4 ± 0.1 2.4 ± 0.1 2.4 ± 0.0 2.6 ± 0.2 1.1 ± 0.0 1.0 ± 0.1 1.5 ± 0.1 0.8 ± 0.0 0.7 ± 0.0 0.7 ± 0.0 1.8 ± 0.1 1.3 ± 0.1 1.2 ± 0.1 1.2 ± 0.1 1.1 ± 0.1 1.2 ± 0.1 1.3 ± 0.1 1.2 ± 0.1 1.0 ± 0.0 1.3 ± 0.1 1.1 ± 0.1 0.9 ± 0.1 1.2 ± 0.1 0.8 ± 0.0 0.5 ± 0.1 2.7 ± 0.8 1.6 ± 0.1 1.5 ± 0.2 1000 1.7 ± 0.1 1.4 ± 0.0 3.6 ± 0.7 2.3 ± 0.3 79.5 ± 1.7 3.6 ± 0.3 1.2 ± 0.4 3.6 ± 0.7 1.3 ± 0.1 7.5 ± 0.0 ND 1.9 ± 0.3 3.1 ± 0.0 12.4 ± 1.4 3.6 ± 0.2 13.6 ± 1.0 8.6 ± 0.4 ND 15.8 ± 0.6 1.4 ± 0.1 1.3 ± 0.1 0.6 ± 0.0 1.3 ± 0.1 0.1 ± 0.0 2.9 ± 0.2 16.1 ± 1.2 0.6 ± 0.1 17.4 ± 0.8 0.8 ± 0.0 1.1 ± 0.0 1.6 ± 0.3 1.2 ± 0.1 0.4 ± 0.1 0.7 ± 0.1 0.2 ± 0.0 179 36 37 38 39 40 41 1.0 ± 0.0 1.0 ± 0.0 1.0 ± 0.1 1.0 ± 0.0 1.0 ± 0.0 1.0 ± 0.0 2.7 ± 0.1 1.1 ± 0.3 1.0 ± 0.0 1.7 ± 0.5 1.1 ± 0.0 1.0 ± 0.0 2.4 ± 0.1 0.8 ± 0.0 0.8 ± 0.1 1.2 ± 0.1 1.2 ± 0.0 0.7 ± 0.1 1.8 ± 0.5 0.7 ± 0.0 1.2 ± 0.2 1.8 ± 0.3 1.1 ± 0.1 0.3 ± 0.1 0.3 ± 0.0 0.3 ± 0.1 0.9 ± 0.1 3.1 ± 0.3 1.1 ± 0.0 ND 180 Table S5 Compound 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 0 1.0 ± 0.0 1.0 ± 0.0 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.0 1.0 ± 0.2 1.1 ± 0.3 1.0 ± 0.1 1.0 ± 0.1 1.1 ± 0.3 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.0 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.2 1.0 ± 0.1 1.1 ± 0.3 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.0 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.0 1.0 ± 0.1 HPTC 2, IL-8 Expression 1 10 100 1000 1.1 ± 0.0 1.3 ± 0.0 1.4 ± 0.1 2.0 ± 0.1 1.3 ± 0.1 1.2 ± 0.1 1.3 ± 0.1 1.5 ± 0.1 1.9 ± 0.2 1.9 ± 0.0 1.8 ± 0.0 13.1 ± 3.0 0.7 ± 0.1 0.9 ± 0.0 2.2 ± 0.1 3.6 ± 0.8 1.4 ± 0.4 21.3 ± 4.7 89.1 ± 4.5 146.1 ± 3.1 1.6 ± 0.1 1.6 ± 0.2 3.3 ± 0.2 9.2 ± 0.2 1.9 ± 0.2 1.8 ± 0.2 1.7 ± 0.2 3.8 ± 0.9 4.6 ± 0.2 5.0 ± 0.2 32.7 ± 0.9 3.9 ± 0.7 1.2 ± 0.0 1.4 ± 0.1 1.8 ± 0.2 1.8 ± 0.4 1.0 ± 0.2 1.3 ± 0.2 5.7 ± 0.8 13.2 ± 0.3 1.2 ± 0.1 2.3 ± 0.5 ND 25.7 ± 3.8 1.2 ± 0.1 1.3 ± 0.0 3.3 ± 0.7 22.5 ± 2.0 1.9 ± 0.0 165.2 ± 14.7 3.1 ± 0.5 3.9 ± 0.1 1.4 ± 0.1 1.4 ± 0.2 119.0 ± 5.4 11.3 ± 0.7 1.5 ± 0.1 1.3 ± 0.1 1.7 ± 0.3 5.7 ± 0.2 1.4 ± 0.1 1.3 ± 0.2 2.2 ± 0.2 8.3 ± 0.5 1.2 ± 0.1 1.6 ± 0.2 4.1 ± 1.2 23.8 ± 2.9 1.8 ± 0.0 2.0 ± 0.1 ND 2.7 ± 0.3 0.9 ± 0.1 0.7 ± 0.0 29.3 ± 4.3 15.1 ± 0.3 1.7 ± 0.2 6.5 ± 1.6 5.5 ± 0.7 67.4 ± 3.1 1.6 ± 0.1 1.3 ± 0.1 1.8 ± 0.2 4.9 ± 0.1 0.9 ± 0.0 0.7 ± 0.0 0.5 ± 0.0 1.0 ± 0.0 1.5 ± 0.4 1.5 ± 0.3 1.5 ± 0.2 1.8 ± 0.4 0.6 ± 0.0 0.4 ± 0.0 0.4 ± 0.1 0.9 ± 0.2 1.4 ± 0.1 1.5 ± 0.1 1.5 ± 0.3 2.6 ± 0.1 0.6 ± 0.0 0.4 ± 0.0 1.1 ± 0.2 4.3 ± 0.3 2.4 ± 0.0 2.3 ± 0.1 2.3 ± 0.1 2.6 ± 0.2 1.1 ± 0.1 1.0 ± 0.1 0.6 ± 0.1 20.5 ± 0.8 0.9 ± 0.1 0.8 ± 0.0 0.8 ± 0.1 1.5 ± 0.1 1.4 ± 0.1 1.3 ± 0.0 1.2 ± 0.0 2.0 ± 0.1 0.9 ± 0.1 0.9 ± 0.1 0.9 ± 0.1 1.2 ± 0.2 1.3 ± 0.1 1.3 ± 0.1 1.2 ± 0.0 1.4 ± 0.1 1.1 ± 0.1 1.0 ± 0.0 0.7 ± 0.1 1.6 ± 0.2 1.2 ± 0.1 0.9 ± 0.0 0.8 ± 0.0 1.4 ± 0.1 1.1 ± 0.1 1.1 ± 0.1 0.3 ± 0.0 1.3 ± 0.0 181 36 37 38 39 40 41 1.0 ± 0.0 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.0 1.0 ± 0.0 2.8 ± 0.0 1.2 ± 0.1 1.2 ± 0.0 1.6 ± 0.2 1.1 ± 0.0 1.6 ± 0.1 2.9 ± 0.0 1.0 ± 0.0 1.1 ± 0.2 1.3 ± 0.0 1.3 ± 0.0 1.1 ± 0.2 2.4 ± 0.7 0.7 ± 0.0 3.1 ± 0.2 2.0 ± 0.3 1.2 ± 0.0 0.9 ± 0.4 0.5 ± 0.0 1.4 ± 0.2 2.1 ± 0.1 24.0 ± 3.4 1.2 ± 0.0 ND 182 Table S6 Compound 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 0 1.0 ± 0.2 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.0 1.0 ± 0.1 1.1 ± 0.4 1.0 ± 0.2 1.0 ± 0.0 1.0 ± 0.1 1.0 ± 0.0 1.0 ± 0.1 1.1 ± 0.4 1.0 ± 0.2 1.0 ± 0.0 1.0 ± 0.0 1.0 ± 0.0 1.0 ± 0.1 1.0 ± 0.0 1.0 ± 0.0 1.0 ± 0.0 1.0 ± 0.2 1.2 ± 0.6 1.6 ± 1.1 1.0 ± 0.1 1.1 ± 0.3 1.1 ± 0.3 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.2 1.0 ± 0.0 1.1 ± 0.3 1.1 ± 0.4 1.0 ± 0.2 1.0 ± 0.0 HPTC 3, IL-6 Expression 1 10 100 0.8 ± 0.1 1.6 ± 0.1 0.9 ± 0.1 1.1 ± 0.0 1.4 ± 0.1 1.1 ± 0.2 1.4 ± 0.3 5.3 ± 0.4 2.9 ± 0.8 1.5 ± 0.2 1.4 ± 0.1 23.6 ± 2.8 0.7 ± 0.1 9.3 ± 0.7 59.2 ± 2.6 1.1 ± 0.0 1.2 ± 0.0 1.4 ± 0.1 7.5 ± 1.0 5.9 ± 0.2 12.0 ± 1.8 11.3 ± 0.7 12.1 ± 0.4 11.9 ± 0.9 1.3 ± 0.1 1.1 ± 0.1 1.1 ± 0.0 1.1 ± 0.1 1.4 ± 0.0 4.4 ± 0.1 0.9 ± 0.0 0.2 ± 0.1 31.4 ± 12.8 1.5 ± 0.1 1.3 ± 0.0 1.3 ± 0.1 2.7 ± 0.2 10.8 ± 1.2 3.5 ± 0.1 0.4 ± 0.1 0.5 ± 0.0 0.3 ± 0.0 1.3 ± 0.1 1.1 ± 0.1 1.5 ± 0.0 1.2 ± 0.1 1.1 ± 0.0 0.9 ± 0.1 1.2 ± 0.1 1.3 ± 0.2 1.1 ± 0.2 0.5 ± 0.0 0.6 ± 0.2 0.3 ± 0.1 0.9 ± 0.0 1.0 ± 0.0 38.1 ± 1.2 0.9 ± 0.1 0.9 ± 0.1 1.3 ± 0.1 0.8 ± 0.0 0.8 ± 0.0 1.2 ± 0.3 0.9 ± 0.0 1.1 ± 0.1 1.0 ± 0.1 0.7 ± 0.1 1.0 ± 0.4 0.9 ± 0.0 3.9 ± 0.5 3.1 ± 0.1 6.2 ± 1.0 0.6 ± 0.1 0.8 ± 0.1 0.6 ± 0.1 1.4 ± 0.6 0.5 ± 0.2 0.9 ± 0.2 8.1 ± 0.2 9.0 ± 0.6 8.4 ± 0.3 0.6 ± 0.1 1.1 ± 0.2 0.9 ± 0.1 0.9 ± 0.2 0.9 ± 0.1 0.7 ± 0.1 0.9 ± 0.1 0.9 ± 0.1 0.9 ± 0.1 1.2 ± 0.2 1.1 ± 0.0 1.4 ± 0.2 0.4 ± 0.1 1.0 ± 0.2 0.4 ± 0.0 1.1 ± 0.2 0.4 ± 0.0 ND 0.5 ± 0.2 1.2 ± 0.3 0.4 ± 0.0 0.8 ± 0.0 1.0 ± 0.1 0.9 ± 0.1 1000 0.9 ± 0.1 1.2 ± 0.0 ND 3.5 ± 0.1 80.4 ± 2.7 0.2 ± 0.0 0.5 ± 0.1 5.2 ± 0.5 2.9 ± 0.3 11.8 ± 0.5 ND 4.0 ± 0.1 6.0 ± 0.4 0.9 ± 0.2 2.2 ± 0.2 5.8 ± 2.5 2.3 ± 0.2 0.4 ± 0.0 8.3 ± 0.3 ND 2.3 ± 0.3 0.8 ± 0.0 0.6 ± 0.1 0.6 ± 0.4 0.2 ± 0.0 2.9 ± 0.1 0.6 ± 0.2 0.2 ± 0.0 0.9 ± 0.2 1.0 ± 0.2 1.4 ± 0.2 0.5 ± 0.1 2.9 ± 0.1 0.7 ± 0.3 0.9 ± 0.1 183 36 37 38 39 40 41 1.0 ± 0.2 1.0 ± 0.0 1.1 ± 0.3 1.0 ± 0.1 1.0 ± 0.0 1.0 ± 0.1 1.4 ± 0.2 0.9 ± 0.1 0.6 ± 0.1 1.2 ± 0.1 1.3 ± 0.1 0.7 ± 0.0 1.0 ± 0.1 0.8 ± 0.0 0.3 ± 0.0 1.2 ± 0.1 1.0 ± 0.1 0.5 ± 0.1 1.1 ± 0.1 0.9 ± 0.2 0.8 ± 0.0 0.1 ± 0.0 0.3 ± 0.0 0.2 ± 0.0 2.8 ± 0.4 3.4 ± 0.3 1.0 ± 0.1 1.0 ± 0.1 20.6 ± 1.9 128.5 ± 21.1 184 Table S7 Compound 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 0 1.0 ± 0.2 1.0 ± 0.0 1.2 ± 0.5 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.2 1.0 ± 0.2 1.1 ± 0.3 1.0 ± 0.0 1.0 ± 0.1 1.0 ± 0.0 1.0 ± 0.1 1.1 ± 0.4 1.1 ± 0.3 1.0 ± 0.0 1.0 ± 0.0 1.0 ± 0.1 1.0 ± 0.0 1.0 ± 0.0 1.0 ± 0.1 1.0 ± 0.0 1.1 ± 0.3 1.3 ± 0.6 1.2 ± 0.5 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.2 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.0 HPTC 3, IL-8 Expression 1 10 100 1000 1.9 ± 0.3 0.8 ± 0.1 0.8 ± 0.0 1.2 ± 0.1 2.0 ± 0.1 1.3 ± 0.2 1.3 ± 0.0 1.8 ± 0.2 3.2 ± 0.6 3.0 ± 0.4 7.3 ± 2.7 ND 1.6 ± 0.4 1.3 ± 0.1 35.5 ± 4.4 8.2 ± 0.9 0.9 ± 0.0 18.9 ± 0.4 29.1 ± 3.6 46.4 ± 1.2 1.1 ± 0.0 1.1 ± 0.1 2.4 ± 0.6 0.1 ± 0.0 2.2 ± 0.2 2.6 ± 0.1 5.4 ± 0.4 4.4 ± 0.5 17.2 ± 2.6 21.9 ± 0.8 18.8 ± 0.5 3.2 ± 0.5 1.5 ± 0.1 1.3 ± 0.1 1.1 ± 0.0 2.6 ± 0.2 0.8 ± 0.1 1.0 ± 0.0 3.3 ± 0.1 9.7 ± 0.2 0.7 ± 0.0 5.3 ± 0.3 22.8 ± 4.0 ND 1.4 ± 0.2 1.2 ± 0.1 1.5 ± 0.3 9.6 ± 1.8 2.2 ± 0.3 13.3 ± 1.4 2.4 ± 0.1 1.5 ± 0.1 0.6 ± 0.1 1.5 ± 0.2 9.9 ± 1.1 2.6 ± 0.2 1.3 ± 0.1 1.1 ± 0.0 1.5 ± 0.2 3.4 ± 0.5 1.1 ± 0.1 1.3 ± 0.0 3.5 ± 1.0 1.0 ± 0.2 1.3 ± 0.2 1.3 ± 0.2 1.1 ± 0.2 2.8 ± 0.4 1.6 ± 0.3 2.2 ± 0.3 2.2 ± 0.4 10.1 ± 0.9 1.1 ± 0.0 1.2 ± 0.0 25.0 ± 0.8 9.5 ± 0.3 1.2 ± 0.1 1.2 ± 0.1 17.8 ± 1.6 ND 1.2 ± 0.0 1.1 ± 0.1 1.5 ± 0.1 1.3 ± 0.0 1.4 ± 0.1 1.7 ± 0.1 2.0 ± 0.2 1.0 ± 0.2 0.8 ± 0.2 0.7 ± 0.0 0.6 ± 0.1 0.5 ± 0.0 1.4 ± 0.1 1.8 ± 0.1 3.6 ± 0.3 2.3 ± 0.7 0.9 ± 0.0 0.9 ± 0.0 0.6 ± 0.0 0.2 ± 0.0 0.7 ± 0.3 0.8 ± 0.1 1.5 ± 0.3 0.3 ± 0.1 8.2 ± 0.7 7.5 ± 0.3 5.8 ± 0.1 3.8 ± 0.6 1.3 ± 0.3 0.9 ± 0.1 0.8 ± 0.0 0.5 ± 0.0 1.1 ± 0.2 1.0 ± 0.0 0.8 ± 0.0 1.2 ± 0.0 1.2 ± 0.3 0.8 ± 0.2 0.9 ± 0.2 1.1 ± 0.1 1.2 ± 0.2 1.1 ± 0.1 1.3 ± 0.2 1.0 ± 0.1 1.1 ± 0.2 0.8 ± 0.1 0.8 ± 0.0 0.8 ± 0.1 0.8 ± 0.1 0.5 ± 0.1 ND 3.2 ± 0.3 1.0 ± 0.1 0.6 ± 0.1 0.8 ± 0.2 1.7 ± 0.4 1.0 ± 0.1 0.8 ± 0.1 0.8 ± 0.1 1.2 ± 0.1 185 36 37 38 39 40 41 1.0 ± 0.1 1.0 ± 0.0 1.0 ± 0.2 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.2 1.7 ± 0.3 1.3 ± 0.1 1.0 ± 0.3 0.4 ± 0.0 1.2 ± 0.1 0.4 ± 0.0 1.0 ± 0.2 1.0 ± 0.0 1.1 ± 0.1 1.2 ± 0.1 0.9 ± 0.0 1.3 ± 0.0 0.9 ± 0.0 1.2 ± 0.0 1.3 ± 0.2 0.4 ± 0.0 1.2 ± 0.3 5.1 ± 0.3 1.1 ± 0.1 1.3 ± 0.1 1.3 ± 0.1 0.4 ± 0.1 27.7 ± 0.8 38.2 ± 7.1 186 Table S8 Compound 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 0 1.0 ± 0.0 1.0 ± 0.0 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.2 1.0 ± 0.1 1.0 ± 0.2 1.0 ± 0.2 1.0 ± 0.2 1.0 ± 0.0 1.0 ± 0.1 1.1 ± 0.3 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.2 1.0 ± 0.1 1.0 ± 0.2 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.4 ± 0.8 1.1 ± 0.3 1.0 ± 0.1 1.1 ± 0.5 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.2 1.1 ± 0.3 1.0 ± 0.1 1.1 ± 0.3 1.0 ± 0.1 1.0 ± 0.1 HK-2, IL-6 Expression 1 10 100 1000 0.9 ± 0.1 1.2 ± 0.1 1.1 ± 0.2 1.0 ± 0.1 1.0 ± 0.0 1.4 ± 0.2 0.9 ± 0.0 0.9 ± 0.2 8.3 ± 0.2 7.6 ± 0.3 6.1 ± 0.1 12.8 ± 1.7 1.4 ± 0.3 0.9 ± 0.0 8.6 ± 1.3 4.0 ± 0.7 0.7 ± 0.2 3.6 ± 0.2 25.2 ± 2.7 120.5 ± 26.1 1.3 ± 0.1 1.0 ± 0.1 1.0 ± 0.0 2.6 ± 0.3 1.5 ± 0.0 3.6 ± 0.8 2.2 ± 0.2 2.8 ± 0.0 ND 1.7 ± 0.1 1.1 ± 0.1 0.2 ± 0.1 0.8 ± 0.0 1.2 ± 0.1 0.7 ± 0.2 0.5 ± 0.2 1.1 ± 0.1 1.0 ± 0.0 1.2 ± 0.1 0.8 ± 0.1 0.3 ± 0.0 1.6 ± 1.0 4.0 ± 1.4 5.9 ± 0.5 0.7 ± 0.1 0.5 ± 0.0 0.8 ± 0.4 1.3 ± 0.0 1.5 ± 0.0 2.3 ± 0.0 2.8 ± 0.8 2.6 ± 0.0 1.0 ± 0.1 0.9 ± 0.0 0.5 ± 0.0 16.1 ± 1.1 0.7 ± 0.1 0.7 ± 0.1 0.6 ± 0.0 0.3 ± 0.0 1.6 ± 0.3 1.1 ± 0.2 1.1 ± 0.1 3.5 ± 0.5 1.4 ± 0.2 0.9 ± 0.1 1.5 ± 0.2 2.0 ± 0.4 0.1 ± 0.0 0.1 ± 0.0 0.6 ± 0.0 ND 4.0 ± 0.3 5.1 ± 0.2 7.8 ± 0.4 5.8 ± 1.6 0.8 ± 0.1 0.6 ± 0.1 1.1 ± 0.1 1.2 ± 0.1 1.5 ± 0.0 1.4 ± 0.0 1.3 ± 0.0 14.7 ± 1.5 1.1 ± 0.0 2.0 ± 0.3 1.8 ± 0.1 2.0 ± 0.1 0.7 ± 0.1 0.8 ± 0.0 0.6 ± 0.0 0.4 ± 0.0 0.4 ± 0.1 0.7 ± 0.1 0.5 ± 0.0 0.2 ± 0.0 0.6 ± 0.1 1.2 ± 0.2 0.6 ± 0.1 0.7 ± 0.1 ND 0.9 ± 0.5 0.3 ± 0.1 0.3 ± 0.0 0.2 ± 0.0 1.0 ± 0.1 0.7 ± 0.1 0.5 ± 0.0 0.4 ± 0.0 1.6 ± 0.0 1.1 ± 0.0 0.9 ± 0.1 0.3 ± 0.0 1.5 ± 0.6 0.1 ± 0.0 0.1 ± 0.0 0.9 ± 0.0 1.3 ± 0.1 1.0 ± 0.2 1.0 ± 0.1 0.8 ± 0.0 1.4 ± 0.0 0.7 ± 0.0 0.6 ± 0.0 0.8 ± 0.1 0.7 ± 0.0 0.9 ± 0.2 0.8 ± 0.0 ND 1.1 ± 0.1 0.8 ± 0.0 0.9 ± 0.1 0.7 ± 0.0 1.3 ± 0.1 0.8 ± 0.1 0.6 ± 0.1 1.3 ± 0.0 1.3 ± 0.1 1.1 ± 0.0 0.8 ± 0.3 187 36 37 38 39 40 41 1.0 ± 0.2 1.1 ± 0.4 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 0.5 ± 0.1 1.5 ± 0.3 0.9 ± 0.1 1.2 ± 0.1 2.2 ± 0.1 0.7 ± 0.1 0.3 ± 0.0 0.7 ± 0.2 0.5 ± 0.0 0.9 ± 0.1 0.9 ± 0.0 0.6 ± 0.1 0.2 ± 0.0 0.6 ± 0.0 0.4 ± 0.1 0.9 ± 0.1 0.7 ± 0.0 0.5 ± 0.0 0.8 ± 0.0 0.2 ± 0.0 2.4 ± 0.6 0.8 ± 0.1 35.0 ± 4.3 188 Table S9 Compound 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 0 1.0 ± 0.0 1.0 ± 0.0 1.0 ± 0.1 1.0 ± 0.0 1.0 ± 0.0 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.2 1.0 ± 0.2 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.2 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.0 1.0 ± 0.1 1.0 ± 0.0 1.0 ± 0.0 1.0 ± 0.2 1.0 ± 0.0 1.0 ± 0.0 1.0 ± 0.1 1.1 ± 0.3 1.0 ± 0.1 1.0 ± 0.0 1.0 ± 0.2 1.0 ± 0.0 1.0 ± 0.0 1.0 ± 0.2 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.2 1.0 ± 0.0 1.0 ± 0.1 HK-2, IL-8 Expression 1 10 100 1000 1.1 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.3 ± 0.5 1.1 ± 0.1 0.8 ± 0.1 0.8 ± 0.1 0.7 ± 0.0 5.1 ± 0.2 4.3 ± 0.1 3.5 ± 0.1 4.1 ± 0.2 1.6 ± 0.6 1.0 ± 0.1 13.9 ± 2.6 18.8 ± 5.3 1.3 ± 0.4 5.0 ± 1.1 13.5 ± 1.8 30.5 ± 2.7 1.2 ± 0.2 1.0 ± 0.1 1.0 ± 0.1 1.9 ± 0.3 9.0 ± 0.7 8.6 ± 0.5 14.3 ± 1.2 0.9 ± 0.0 ND 2.5 ± 0.1 2.2 ± 0.1 1.6 ± 0.1 1.7 ± 0.1 0.8 ± 0.0 0.6 ± 0.1 0.7 ± 0.0 1.2 ± 0.1 0.9 ± 0.0 0.9 ± 0.1 1.4 ± 0.5 0.2 ± 0.0 2.0 ± 1.0 1.3 ± 0.5 1.0 ± 0.1 1.3 ± 0.1 1.2 ± 0.1 1.2 ± 0.1 3.0 ± 0.0 0.9 ± 0.1 3.7 ± 0.7 2.3 ± 0.4 2.5 ± 0.0 1.0 ± 0.0 0.8 ± 0.1 0.2 ± 0.0 1.2 ± 0.1 1.5 ± 0.2 1.5 ± 0.2 1.5 ± 0.1 1.1 ± 0.2 0.4± 0.0 1.9 ± 0.4 1.5 ± 0.3 1.1 ± 0.2 1.4 ± 0.1 1.2 ± 0.2 2.1 ± 0.1 2.1 ± 0.4 0.5 ± 0.1 0.6 ± 0.1 0.4 ± 0.0 ND 2.8 ± 0.1 2.1 ± 0.2 2.6 ± 0.3 2.5 ± 0.5 0.6 ± 0.1 0.5 ± 0.0 1.3 ± 0.2 1.6 ± 0.3 1.1 ± 0.0 1.1 ± 0.0 0.8 ± 0.1 1.7 ± 0.0 3.9 ± 0.5 3.8 ± 0.4 3.1 ± 0.5 1.2 ± 0.2 1.2 ± 0.2 0.8 ± 0.1 0.7 ± 0.1 0.7 ± 0.1 4.7 ± 0.6 3.6 ± 0.8 2.2 ± 0.0 0.5 ± 0.3 1.2 ± 0.1 1.2 ± 0.1 1.1 ± 0.1 1.1 ± 0.1 ND 0.5 ± 0.2 0.3 ± 0.0 0.4 ± 0.1 8.3 ± 0.7 5.4 ± 0.2 3.2 ± 0.2 2.4 ± 1.0 1.4 ± 0.1 1.3 ± 0.1 1.4 ± 0.1 0.8 ± 0.1 0.3 ± 0.1 0.1 ± 0.0 0.4 ± 0.1 0.5 ± 0.0 1.6 ± 0.1 1.2 ± 0.2 1.1 ± 0.1 1.3 ± 0.0 1.7 ± 0.3 1.1 ± 0.1 1.0 ± 0.1 1.0 ± 0.0 1.1 ± 0.1 1.0 ± 0.1 1.1 ± 0.2 1.1 ± 0.0 0.9 ± 0.1 1.0 ± 0.1 1.2 ± 0.1 ND 1.2 ± 0.1 1.3 ± 0.1 0.8 ± 0.0 0.5 ± 0.0 1.4 ± 0.1 0.9 ± 0.1 0.9 ± 0.0 1.5 ± 0.0 189 36 37 38 39 40 41 1.0 ± 0.0 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.0 1.0 ± 0.0 1.0 ± 0.0 0.8 ± 0.1 0.3 ± 0.0 1.1 ± 0.0 0.4 ± 0.0 1.0 ± 0.0 4.3 ± 0.4 0.6 ± 0.1 0.3 ± 0.0 0.8 ± 0.1 0.4 ± 0.0 0.9 ± 0.0 1.7 ± 0.1 0.5 ± 0.0 0.4 ± 0.0 0.2 ± 0.0 0.3 ± 0.0 0.7 ± 0.0 1.2 ± 0.1 0.4 ± 0.0 10.2 ± 3.5 0.9 ± 0.1 0.9 ± 0.1 2.1 ± 0.1 82.4 ± 8.2 190 Table S10 Compound 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 0 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.2 1.0 ± 0.1 1.0 ± 0.0 1.1 ± 0.3 1.0 ± 0.0 1.0 ± 0.2 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.0 1.0 ± 0.1 1.0 ± 0.0 1.0 ± 0.1 1.0 ± 0.1 1.1 ± 0.3 1.0 ± 0.0 1.0 ± 0.0 1.0 ± 0.1 1.0 ± 0.2 1.0 ± 0.1 1.0 ± 0.2 1.0 ± 0.2 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.2 1.0 ± 0.0 1.0 ± 0.1 1.0 ± 0.2 1.0 ± 0.2 1.0 ± 0.0 LLC-PK1, IL-6 Expression 1 10 100 1000 0.7 ± 0.2 2.3 ± 0.8 1.8 ± 0.5 5.2 ± 1.0 0.9 ± 0.1 0.9 ± 0.0 1.0 ± 0.0 1.2 ± 0.0 0.5 ± 0.0 0.3 ± 0.1 1.4 ± 0.2 0.7 ± 0.1 1.1 ± 0.1 1.9 ± 0.7 1.0 ± 0.0 4.8 ± 0.3 1.2 ± 0.2 15.1 ± 2.7 313.7 ± 31.4 180.5 ± 16.1 0.6 ± 0.1 0.5 ± 0.0 0.9 ± 0.0 1.1 ± 0.0 1.6 ± 0.0 1.8 ± 0.1 1.4 ± 0.0 1.8 ± 0.1 1.4 ± 0.1 1.7 ± 0.1 1.1 ± 0.1 ND 0.8 ± 0.0 0.9 ± 0.0 1.0 ± 0.1 0.8 ± 0.1 1.1 ± 0.2 2.8 ± 0.3 4.7 ± 0.5 6.3 ± 0.3 0.3 ± 0.0 0.5 ± 0.0 ND 0.9 ± 0.1 0.9 ± 0.0 1.1 ± 0.1 1.3 ± 0.1 1.8 ± 0.1 2.2 ± 0.1 12.9 ± 7.2 ND ND 1.1 ± 0.0 1.1 ± 0.1 3.3 ± 0.5 3.7 ± 0.2 0.8 ± 0.0 0.9 ± 0.0 1.0 ± 0.0 2.4 ± 0.0 1.3 ± 0.0 1.1 ± 0.1 1.2 ± 0.1 2.8 ± 0.0 0.9 ± 0.0 1.3 ± 0.0 1.4 ± 0.0 2.5 ± 0.2 0.9 ± 0.0 0.5 ± 0.0 0.7 ± 0.1 0.9 ± 0.0 1.5 ± 0.1 1.5 ± 0.0 1.4 ± 0.3 7.4 ± 1.0 0.7 ± 0.1 1.1 ± 0.3 2.2 ± 0.2 2.4 ± 0.6 0.9 ± 0.1 1.0 ± 0.1 0.9 ± 0.0 1.7 ± 0.5 1.5 ± 0.1 1.5 ± 0.1 1.4 ± 0.0 1.4 ± 0.0 0.9 ± 0.1 0.7 ± 0.0 0.8 ± 0.1 0.9 ± 0.1 0.7 ± 0.0 0.8 ± 0.1 1.3 ± 0.1 0.8 ± 0.0 0.9 ± 0.1 1.3 ± 0.1 1.7 ± 0.4 2.3 ± 0.4 0.3 ± 0.0 0.3 ± 0.0 0.4 ± 0.1 2.1 ± 0.5 1.8 ± 0.1 1.9 ± 0.2 1.6 ± 0.2 2.6 ± 0.1 0.8 ± 0.0 1.0 ± 0.0 0.9 ± 0.0 1.0 ± 0.0 0.6 ± 0.1 0.5 ± 0.1 0.3 ± 0.1 0.9 ± 0.2 0.8 ± 0.0 1.0 ± 0.1 1.1 ± 0.1 0.8 ± 0.1 1.1 ± 0.1 1.2 ± 0.1 1.4 ± 0.2 1.6 ± 0.3 1.6 ± 0.1 1.6 ± 0.1 1.1 ± 0.2 0.9 ± 0.1 0.9 ± 0.0 0.9 ± 0.1 1.1 ± 0.2 8.2 ± 2.6 0.7 ± 0.0 0.5 ± 0.0 0.6 ± 0.1 1.8 ± 0.0 0.7 ± 0.0 0.7 ± 0.0 0.7 ± 0.0 0.9 ± 0.1 191 36 37 38 39 40 41 1.0 ± 0.0 1.3 ± 0.7 1.0 ± 0.0 1.0 ± 0.0 1.0 ± 0.1 1.0 ± 0.2 0.7 ± 0.0 0.5 ± 0.1 0.7 ± 0.0 0.6 ± 0.1 1.0 ± 0.0 0.9 ± 0.0 0.7 ± 0.0 2.4 ± 0.3 0.7 ± 0.1 0.5 ± 0.0 1.1 ± 0.0 0.8 ± 0.0 0.7 ± 0.1 1.1 ± 0.7 0.8 ± 0.0 0.7 ± 0.1 1.1 ± 0.0 0.4 ± 0.0 0.8 ± 0.0 1.1 ± 0.5 0.7 ± 0.0 7.7 ± 0.6 1.1 ± 0.0 0.5 ± 0.1 192 Table S11 Compound 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 0 1.0 ± 0.1 1.0 ± 0.0 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.0 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.2 1.0 ± 0.1 1.1 ± 0.3 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.0 1.0 ± 0.1 1.0 ± 0.0 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.2 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.1 ± 0.3 1.0 ± 0.0 1.0 ± 0.1 1.0 ± 0.2 1.0 ± 0.1 1.1 ± 0.2 1.0 ± 0.0 1.0 ± 0.1 1.0 ± 0.0 1.0 ± 0.2 1.0 ± 0.1 1.0 ± 0.1 LLC-PK1, IL-8 Expression 1 10 100 1000 1.3 ± 0.1 1.1 ± 0.3 2.1 ± 0.5 2.7 ± 0.1 1.0 ± 0.0 1.3 ± 0.1 1.8 ± 0.2 2.3 ± 0.2 7.8 ± 1.5 3.9 ± 0.4 2.1 ± 0.0 8.4 ± 0.1 0.9 ± 0.1 1.2 ± 0.4 1.0 ± 0.0 8.9 ± 0.3 0.9 ± 0.1 23.1 ± 3.9 413.7 ± 28.8 839.4 ± 305.9 0.7 ± 0.1 0.6 ± 0.0 0.8 ± 0.0 1.6 ± 0.2 9.1 ± 0.4 1.8 ± 0.0 10.1 ± 0.7 8.1 ± 0.7 1.0 ± 0.4 0.7 ± 0.3 ND 1.2 ± 0.4 1.5 ± 0.1 1.2 ± 0.1 1.7 ± 0.0 1.5 ± 0.2 0.5 ± 0.1 1.5 ± 0.1 15.8 ± 2.1 20.3 ± 2.0 0.8 ± 0.4 6.9 ± 0.3 0.4 ± 0.0 ND 0.7 ± 0.0 0.9 ± 0.0 1.7 ± 0.1 4.3 ± 0.9 2.1 ± 0.1 12.2 ± 4.3 ND ND 1.2 ± 0.1 1.8 ± 0.3 1.6 ± 0.1 37.0 ± 4.4 0.7 ± 0.1 1.0 ± 0.1 1.3 ± 0.1 2.5 ± 0.1 0.9 ± 0.1 1.2 ± 0.2 0.7 ± 0.0 1.6 ± 0.3 1.0 ± 0.1 1.6 ± 0.0 2.0 ± 0.2 16.3 ± 4.3 0.5 ± 0.0 0.4 ± 0.0 0.4 ± 0.0 1.4 ± 0.2 5.9 ± 0.4 7.1 ± 0.5 301.3 ± 27.5 47.9 ± 6.0 1.2 ± 0.3 0.7 ± 0.2 3.9 ± 0.1 4.9 ± 1.2 2.2 ± 0.2 1.8 ± 0.1 1.2 ± 0.2 2.6 ± 0.3 2.4 ± 0.2 1.5 ± 0.1 4.0 ± 0.3 2.2 ± 0.1 0.6 ± 0.1 0.7 ± 0.1 1.2 ± 0.1 0.4 ± 0.0 7.1 ± 1.3 1.4 ± 0.1 16.1 ± 0.4 8.4 ± 0.7 0.9 ± 0.1 1.1 ± 0.0 0.5 ± 0.1 1.8 ± 0.3 0.7 ± 0.1 0.5 ± 0.1 0.9 ± 0.0 2.9 ± 1.0 7.6 ± 1.1 5.7 ± 0.7 0.4 ± 0.0 13.7 ± 1.2 0.6 ± 0.1 0.9 ± 0.1 0.9 ± 0.1 1.0 ± 0.1 0.9 ± 0.2 0.8 ± 0.1 0.6 ± 0.1 1.2 ± 0.1 1.6 ± 0.3 1.5 ± 0.0 2.6 ± 0.1 1.8 ± 0.6 0.8 ± 0.0 1.0 ± 0.0 1.3 ± 0.1 1.3 ± 0.0 1.1 ± 0.2 0.7 ± 0.0 2.1 ± 0.1 1.3 ± 0.1 0.9 ± 0.0 0.8 ± 0.1 0.8 ± 0.0 9.1 ± 0.6 0.7 ± 0.0 0.5 ± 0.0 0.8 ± 0.0 0.7 ± 0.1 0.7 ± 0.1 0.7 ± 0.1 0.7 ± 0.0 1.0 ± 0.0 193 36 37 38 39 40 41 1.0 ± 0.1 1.2 ± 0.5 1.0 ± 0.0 1.0 ± 0.0 1.0 ± 0.1 1.1 ± 0.2 0.6 ± 0.0 0.7 ± 0.1 0.9 ± 0.1 0.8 ± 0.0 1.0 ± 0.1 2.4 ± 0.3 0.6 ± 0.1 2.6 ± 0.2 1.0 ± 0.2 0.7 ± 0.0 1.0 ± 0.0 1.8 ± 0.1 0.7 ± 0.1 1.3 ± 0.7 0.8 ± 0.1 0.7 ± 0.1 1.0 ± 0.0 2.0 ± 0.1 0.6 ± 0.0 0.9 ± 0.3 1.1 ± 0.0 164.4 ± 4.0 1.1 ± 0.0 11.0 ± 1.0 194 Table S12, S13 IL-6 and IL-8 expression levels. hESC-derived HPTC-like cells (batch 1) were exposed to the 41 test compounds at concentrations of 1 g/ml, 10 g/ml, 100 g/ml and 1000 g/ml. The column “0” (0 g/ml compound concentration) displays the values obtained with the vehicle control. The table lists the levels of IL-6/IL-8 expression determined by qPCR. The numbers show the mean fold expression +/- s.d. (n = 3) relative to the vehicle control. In one case the expression levels were not determined (ND) due to massive cell death. The highest levels of IL-6/IL-8 expression that were determined for a given compound when the whole range of concentrations was tested (1 g/ml – 1000 g/ml) are highlighted (bold). These highest expression values obtained with a specific compound were entered into Table 13. Adapted with permission from [10] (Supplementary Information). Copyright 2014 American Chemical Society. 195 Table S12 Compound 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 0 1 1.0 ± 0.0 1.3 ± 0.0 1.1 ± 0.3 1.5 ± 0.3 1.0 ± 0.1 2.6 ± 1.0 1.0 ± 0.1 0.8 ± 0.1 1.0 ± 0.0 17.7 ± 1.5 1.0 ± 0.1 0.9 ± 0.0 1.0 ± 0.2 1.9 ± 0.0 1.0 ± 0.1 2.1 ± 0.0 1.0 ± 0.1 1.1 ± 0.1 1.0 ± 0.0 1.2 ± 0.0 1.0 ± 0.0 0.5 ± 0.0 1.0 ± 0.0 1.1 ± 0.0 1.0 ± 0.0 7.6 ± 0.4 1.0 ± 0.0 1.2 ± 0.1 1.0 ± 0.2 1.0 ± 0.1 1.1 ± 0.4 0.8 ± 0.1 1.0 ± 0.0 1.1 ± 0.2 1.0 ± 0.0 0.2 ± 0.0 1.0 ± 0.1 3.4 ± 0.8 1.0 ± 0.1 1.6 ± 0.5 1.0 ± 0.1 1.1 ± 0.0 1.0 ± 0.2 1.5 ± 0.2 1.0 ± 0.2 0.7 ± 0.3 1.0 ± 0.0 0.6 ± 0.0 1.0 ± 0.0 0.9 ± 0.1 1.0 ± 0.1 0.3 ± 0.0 1.2 ± 0.4 1.4 ± 0.2 1.0 ± 0.1 1.1 ± 0.1 1.0 ± 0.2 0.2 ± 0.1 1.0 ± 0.0 1.1 ± 0.0 1.0 ± 0.1 1.0 ± 0.0 1.0 ± 0.1 1.3 ± 0.1 1.0 ± 0.1 1.1 ± 0.0 1.0 ± 0.0 1.1 ± 0.1 1.0 ± 0.0 1.1 ± 0.2 1.0 ± 0.1 1.0 ± 0.0 1.0 ± 0.2 0.7 ± 0.1 IL-6 Expression 10 100 1000 1.3 ± 0.0 1.4 ± 0.1 1.9 ± 0.1 1.0 ± 0.1 1.1 ± 0.2 1.0 ± 0.0 3.3 ± 0.4 3.7 ± 0.6 14.9 ± 5.3 0.7 ± 0.0 8.2 ± 0.2 16.5 ± 5.4 1252.9 ± 126.5 509.6 ± 58.3 335.1 ± 15.4 1.0 ± 0.1 1.1 ± 0.1 6.8 ± 0.4 1.7 ± 0.1 1.1 ± 0.1 2.1 ± 0.3 1.8 ± 0.2 1.3 ± 0.5 ND 1.2 ± 0.0 1.0 ± 0.0 1.2 ± 0.1 1.5 ± 0.1 3.7 ± 0.1 8.4 ± 1.5 1.9 ± 0.1 1.9 ± 0.5 8.7 ± 0.2 1.2 ± 0.1 3.0 ± 0.3 42.7 ± 2.5 3.2 ± 0.3 1.7 ± 0.2 3.7 ± 0.4 1.7 ± 0.1 8.4 ± 2.5 10.2 ± 0.6 1.0 ± 0.1 1.0 ± 0.1 0.6 ± 0.0 1.2 ± 0.3 3.6 ± 0.4 46.4 ± 5.6 1.8 ± 0.0 2.4 ± 0.1 27.8 ± 3.0 0.9 ± 0.2 1.3 ± 0.2 1.5 ± 0.3 1.6 ± 0.1 45.2 ± 13.8 3.3 ± 1.8 1.7 ± 0.4 36.1 ± 10.4 10.3 ± 2.7 1.1 ± 0.0 1.0 ± 0.2 6.0 ± 1.4 1.4 ± 0.1 1.5 ± 0.1 0.7 ± 0.0 1.0 ± 0.1 1.1 ± 0.0 2.4 ± 0.2 0.6 ± 0.1 0.0 ± 0.0 0.7 ± 0.3 0.8 ± 0.0 0.5 ± 0.0 0.1 ± 0.0 0.3 ± 0.0 0.3 ± 0.0 0.7 ± 0.1 1.1 ± 0.1 0.9 ± 0.1 1.6 ± 0.0 0.8 ± 0.2 0.5 ± 0.2 0.5 ± 0.0 0.3 ± 0.0 0.4 ± 0.1 0.9 ± 0.4 1.1 ± 0.0 1.2 ± 0.1 1.2 ± 0.0 1.9 ± 0.6 1.4 ± 0.1 3.2 ± 0.1 1.2 ± 0.1 1.2 ± 0.1 1.2 ± 0.1 1.8 ± 0.1 1.3 ± 0.1 54.8 ± 2.9 1.0 ± 0.1 0.8 ± 0.0 0.8 ± 0.0 1.0 ± 0.1 0.8 ± 0.0 0.8 ± 0.0 0.9 ± 0.0 0.7 ± 0.1 1.0 ± 0.1 0.5 ± 0.1 0.1 ± 0.0 1.0 ± 0.1 196 38 39 40 41 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.2 ± 0.6 1.3 ± 0.0 0.2 ± 0.0 1.2 ± 0.2 1.1 ± 0.1 1.3 ± 0.1 0.5 ± 0.0 1.0 ± 0.1 1.1 ± 0.0 1.2 ± 0.0 1.8 ± 0.1 1.1 ± 0.1 12.7 ± 0.8 3.8 ± 0.2 1.6 ± 0.7 0.8 ± 0.0 0.6 ± 0.1 197 Table S13 Compound 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 0 1 1.0 ± 0.0 1.0 ± 0.1 1.1 ± 0.3 0.3 ± 0.1 1.1 ± 0.3 3.0 ± 0.5 1.0 ± 0.1 0.7 ± 0.1 1.0 ± 0.0 169.6 ± 27.8 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.8 ± 0.3 1.0 ± 0.1 1.4 ± 0.2 1.0 ± 0.0 0.9 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.0 0.4 ± 0.0 1.0 ± 0.1 1.0 ± 0.0 1.0 ± 0.0 7.4 ± 0.5 1.0 ± 0.1 1.8 ± 0.0 1.0 ± 0.1 1.1 ± 0.1 1.0 ± 0.0 1.3 ± 0.1 1.0 ± 0.0 1.0 ± 0.1 1.0 ± 0.1 0.7 ± 0.1 1.0 ± 0.2 2.2 ± 0.5 1.0 ± 0.1 2.8 ± 1.1 1.0 ± 0.1 1.2 ± 0.1 1.0 ± 0.2 1.3 ± 0.2 0.9 ± 0.2 0.8 ± 0.1 1.1 ± 0.3 1.1 ± 0.2 1.0 ± 0.2 1.1 ± 0.2 1.1 ± 0.3 0.2 ± 0.0 1.8 ± 0.9 6.8 ± 1.3 1.0 ± 0.0 1.0 ± 0.3 1.0 ± 0.2 0.1 ± 0.0 1.0 ± 0.0 1.0 ± 0.1 1.0 ± 0.2 0.8 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.0 1.0 ± 0.1 1.0 ± 0.0 1.0 ± 0.1 1.0 ± 0.2 0.8 ± 0.0 1.0 ± 0.0 1.1 ± 0.0 1.0 ± 0.2 0.9 ± 0.1 1.0 ± 0.0 0.9 ± 0.0 IL-8 Expression 10 100 1000 1.0 ± 0.1 1.0 ± 0.0 0.9 ± 0.0 0.3 ± 0.0 0.4 ± 0.0 0.4 ± 0.0 2.6 ± 1.2 2.6 ± 0.6 87.5 ± 38.8 0.3 ± 0.1 0.9 ± 0.4 29.0 ± 10.4 1471.4 ± 170.9 1988.2 ± 193.0 2765.7 ± 23.6 0.9 ± 0.1 1.8 ± 0.1 3.2 ± 0.5 1.4 ± 0.3 1.6 ± 0.5 4.3 ± 0.2 1.0 ± 0.1 ND 2.3 ± 0.2 0.9 ± 0.1 0.7 ± 0.0 0.4 ± 0.0 0.9 ± 0.1 1.0 ± 0.1 1.8 ± 0.5 0.1 ± 0.0 0.2 ± 0.1 0.5 ± 0.0 1.0 ± 0.0 3.7 ± 0.3 36.1 ± 1.2 0.4 ± 0.0 0.2 ± 0.0 0.4 ± 0.0 3.7 ± 0.2 0.9 ± 0.2 7.9 ± 0.4 1.0 ± 0.1 1.0 ± 0.2 0.7 ± 0.0 1.4 ± 0.7 2.1 ± 0.4 71.6 ± 26.2 0.6 ± 0.0 1.3 ± 0.1 3.9 ± 0.1 0.5 ± 0.1 0.3 ± 0.0 0.9 ± 0.1 1.8 ± 0.2 549.3 ± 46.2 258.5 ± 144.9 2.7 ± 0.4 100.3 ± 9.3 242.2 ± 4.8 1.3 ± 0.3 0.7 ± 0.2 3.5 ± 1.8 1.2 ± 0.2 1.8 ± 0.3 3.0 ± 0.2 1.0 ± 0.1 0.8 ± 0.1 3.5 ± 0.4 1.1 ± 0.4 0.7 ± 0.1 1.2 ± 0.6 0.8 ± 0.1 0.6 ± 0.1 0.3 ± 0.1 0.1 ± 0.0 0.1 ± 0.0 0.4 ± 0.0 6.5 ± 2.7 3.0 ± 0.6 7.4 ± 1.3 1.0 ± 0.2 0.6 ± 0.3 0.5 ± 0.2 0.1 ± 0.0 0.0 ± 0.0 0.1 ± 0.0 1.0 ± 0.0 1.0 ± 0.1 1.1 ± 0.1 0.9 ± 0.0 1.0 ± 0.0 2.1 ± 0.1 0.9 ± 0.0 0.9 ± 0.0 0.7 ± 0.1 1.2 ± 0.0 1.1 ± 0.1 21.3 ± 0.9 0.8 ± 0.0 0.5 ± 0.1 0.3 ± 0.0 0.7 ± 0.0 0.5 ± 0.0 0.7 ± 0.0 0.9 ± 0.0 0.8 ± 0.1 0.8 ± 0.0 1.2 ± 0.3 0.8 ± 0.1 2.2 ± 0.5 0.7 ± 0.0 0.3 ± 0.0 0.1 ± 0.0 198 39 40 41 1.0 ± 0.2 1.0 ± 0.1 1.0 ± 0.2 0.1 ± 0.0 1.0 ± 0.1 1.1 ± 0.2 0.1 ± 0.0 0.9 ± 0.1 0.2 ± 0.0 0.2 ± 0.0 1.1 ± 0.1 2.3 ± 0.4 1.6 ± 0.7 1.1 ± 0.1 6.6 ± 1.4 199 Table S14. IL-6 and IL-8 expression levels in batch 2 of HPTC-like cells. A second batch of HPTC-like cells was differentiated from hESC. These HPTC-like cells were exposed to test compounds 6, 19 and 40 at concentrations of 1 g/ml, 10 g/ml, 100 g/ml and 1000 g/ml. The column “0” (0 g/ml compound concentration) displays the values obtained with the vehicle control. The table lists the levels of IL-6 and IL-8 expression determined by qPCR. The numbers show the mean fold expression +/- s.d. (n = 3) relative to the respective vehicle control. In one case the expression levels were not determined (ND) due to massive cell death. The highest levels of IL-6 and IL-8 expression that were determined for a given compound when the whole range of concentrations was tested (1 g/ml – 1000 g/ml) are highlighted (bold). These highest expression values obtained with a specific compound were entered into Table 17. Adapted with permission from [10] (Supplementary Information). Copyright 2014 American Chemical Society. Compound 6 19 40 Marker IL-6 IL-8 IL-6 IL-8 IL-6 IL-8 0 1.0 ± 0.2 1.0 ± 0.3 1.0 ± 0.2 1.0 ± 0.0 1.0 ± 0.4 1.0 ± 0.1 1 10 100 0.7 ± 0.6 0.4 ± 0.3 3.9 ± 1.0 1.3 ± 0.9 2.4 ± 0.8 67.8 ± 23.1 7.5 ± 0.2 10.0 ± 1.3 70.5 ± 10.5 9.2 ± 2.0 7.8 ± 0.1 206.1 ± 51.5 1.1 ± 0.3 1.2 ± 0.2 1.9 ± 0.2 1.4 ± 0.6 1.5 ± 0.6 2.1 ± 0.4 1000 2.8 ± 2.0 4.5 ± 2.8 ND ND 2.4 ± 0.8 3.7 ± 1.2 200 Table S15, S16 IL-6 and IL-8 expression levels. hiPSC-derived HPTC-like cells were exposed to the 41 test compounds at concentrations of 1 g/ml, 10 g/ml, 100 g/ml and 1000 g/ml. The column “0” (0 g/ml compound concentration) displays the values obtained with the vehicle control. The table lists the levels of IL-6/IL-8 expression determined by qPCR. The numbers show the mean fold expression +/- s.d. (n = 3) relative to the vehicle control. In one case the expression levels were not determined (ND) due to massive cell death. The highest levels of IL-6/IL-8 expression that were determined for a given compound when the whole range of concentrations was tested (1 g/ml – 1000 g/ml) are highlighted (bold). These highest expression values obtained with a specific compound were entered into Table 18. Table S15 Compound 1 2 3 4 5 6 7 8 9 10 11 12 13 14 hiPSC-derived HPTC-like cells, IL-6 Expression 0 1 10 100 1000 1.0 ± 0.1 1.1 ± 0.0 1.3 ± 0.0 5.3 ± 0.2 86.0 ± 1.5 1.0 ± 0.1 0.8 ± 0.0 0.9 ± 0.1 1.9 ± 0.1 36.5 ± 7.0 1.0 ± 0.2 11.8 ± 0.7 10.6 ± 0.8 6.1 ± 0.3 27.3 ± 2.7 1.0 ± 0.1 1.1 ± 0.0 1.9 ± 0.3 6.2 ± 2.0 60.9 ± 15.6 1.0 ± 0.1 2.7 ± 0.4 266.0 ± 22.2 56.0 ± 4.4 40.2 ± 6.7 1.0 ± 0.0 1.0 ± 0.0 0.9 ± 0.1 1.4 ± 0.1 4.0 ± 0.3 1.1 ± 0.3 12.6 ± 0.8 12.5 ± 0.6 10.0 ± 1.1 2.0 ± 0.3 1.1 ± 0.5 9.4 ± 1.1 8.0 ± 1.7 15.2 ± 1.5 20.0 ± 1.6 1.0 ± 0.2 0.9 ± 0.0 1.1 ± 0.1 1.1 ± 0.1 1.2 ± 0.2 1.0 ± 0.0 0.9 ± 0.1 1.2 ± 0.1 2.3 ± 0.2 3.1 ± 0.9 1.0 ± 0.1 0.7 ± 0.0 1.4 ± 0.3 ND 3.5 ± 0.5 1.0 ± 0.1 1.0 ± 0.1 2.4 ± 0.3 13.3 ± 3.4 31.8 ± 2.0 1.0 ± 0.1 3.4 ± 0.8 5.2 ± 0.9 4.6 ± 1.4 11.0 ± 1.3 1.0 ± 0.1 1.2 ± 0.2 1.6 ± 0.1 1.6 ± 0.2 96.5 ± 13.8 201 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 1.1 ± 0.2 1.0 ± 0.0 1.0 ± 0.1 1.0 ± 0.2 1.1 ± 0.3 1.0 ± 0.1 1.0 ± 0.2 1.0 ± 0.2 1.1 ± 0.4 1.1 ± 0.3 1.0 ± 0.1 1.0 ± 0.1 1.1 ± 0.3 1.0 ± 0.2 1.0 ± 0.2 1.0 ± 0.0 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.2 1.0 ± 0.2 1.0 ± 0.0 1.1 ± 0.1 0.4 ± 0.1 4.4 ± 0.5 4.7 ± 0.2 1.6 ± 0.2 0.6 ± 0.3 1.4 ± 0.1 1.0 ± 0.0 1.1 ± 0.1 0.2 ± 0.0 1.2 ± 0.1 1.2 ± 0.0 0.3 ± 0.0 0.9 ± 0.1 1.4 ± 0.1 1.1 ± 0.1 0.9 ± 0.1 1.1 ± 0.1 1.3 ± 0.1 1.1 ± 0.2 1.9 ± 0.1 1.1 ± 0.1 0.4 ± 0.1 1.0 ± 0.0 5.0 ± 0.5 1.0 ± 0.2 1.0 ±0.1 1.5 ± 0.1 0.1 ± 0.0 3.2 ± 0.5 5.7 ± 0.3 1.6 ± 0.2 0.9 ± 0.2 1.2 ± 0.0 0.9 ± 0.2 1.0 ± 0.1 0.3 ± 0.0 1.2 ± 0.1 1.3 ± 0.1 0.4 ± 0.0 0.8 ± 0.1 1.6 ± 0.1 1.2 ± 0.1 1.1 ± 0.1 0.8 ± 0.0 1.4 ± 0.1 1.0 ± 0.1 1.4 ± 0.1 1.1 ± 0.1 0.3 ± 0.0 0.9 ± 0.0 4.1 ± 0.5 1.6 ± 0.5 2.3 ± 0.5 6.5 ± 1.4 23.0 ± 3.7 3.4 ± 0.2 19.8 ± 0.7 0.6 ± 0.3 0.7 ± 0.1 57.8 ± 3.4 61.7 ± 3.4 10.4 ± 2.0 49.1 ± 6.4 3.0 ± 0.0 5.2 ± 0.3 1.5 ± 0.3 2.3 ± 0.5 1.7 ± 0.1 2.2 ± 0.1 0.2 ± 0.0 1.8 ± 0.3 0.7 ± 0.0 0.1 ± 0.0 0.4 ± 0.0 2.5 ± 0.4 0.5 ± 0.0 2.1 ± 0.4 1.0 ± 0.1 0.6 ± 0.1 0.9 ± 0.1 1.6 ± 0.2 0.7 ± 0.1 0.8 ± 0.0 1.8 ± 0.2 3.5 ± 0.4 1.6 ± 0.3 2.7 ± 0.4 1.6 ± 0.1 80.1 ± 7.6 0.6 ± 0.0 0.4 ± 0.0 1.2 ± 0.0 1.9 ± 0.1 0.9 ± 0.0 0.7 ± 0.0 0.6 ± 0.1 0.2 ± 0.0 0.4 ± 0.1 1.6 ± 0.3 0.8 ± 0.0 0.8 ± 0.0 1.0 ± 0.1 1.0 ± 0.1 12.4 ± 3.4 72.2 ± 14.6 202 Table S16 Compound 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 0 1.0 ± 0.2 1.0 ± 0.2 1.0 ± 0.1 1.5 ± 0.9 1.0 ± 0.0 1.0 ± 0.1 1.2 ± 0.5 1.1 ± 0.4 1.0 ± 0.2 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.2 1.0 ± 0.2 1.0 ± 0.2 1.1 ± 0.4 1.2 ± 0.4 1.0 ± 0.2 1.1 ± 0.4 1.1 ± 0.3 1.0 ± 0.2 1.2 ± 0.4 1.2 ± 0.4 1.1 ± 0.4 1.1 ± 0.3 1.1 ± 0.3 1.0 ± 0.2 1.0 ± 0.2 1.1 ± 0.3 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.2 1.0 ± 0.1 1.1 ± 0.3 1.0 ± 0.0 hiPSC-derived HPTC-like cells, IL-6 Expression 1 10 100 1000 1.1 ± 0.1 1.5 ± 0.1 4.8 ± 0.6 860.1 ± 30.9 1.0 ± 0.2 1.2 ± 0.3 1.6 ± 0.2 86.3 ± 20.4 14.2 ± 1.7 14.2 ± 2.2 15.3 ± 4.2 23.2 ± 1.5 0.7 ± 0.1 1.3 ± 0.1 2.4 ± 0.6 4710.3 ± 1107.7 193.4 ± 11.7 499.1 ± 83.5 615.2 ± 45.7 964.2 ± 34.2 1.0 ± 0.1 0.8 ± 0.2 2.5 ± 0.3 58.4 ± 16.5 1.8 ± 0.4 1.7 ± 0.4 2.3 ± 0.4 11.2 ± 1.3 0.1 ± 0.0 0.1 ± 0.0 0.1 ± 0.0 0.3 ± 0.0 1.0 ± 0.0 1.6 ± 0.5 1.3 ± 0.4 1.9 ± 0.1 1.0 ± 0.0 1.2 ± 0.1 3.1 ± 0.3 3.3 ± 0.7 1.2 ± 0.1 0.5 ± 0.0 ND 3.8 ± 0.1 7.0 ± 2.3 84.9 ± 19.2 1311.2 ± 84.1 4321.1 ± 254.3 9.9 ± 2.2 ND ND 11.1 ± 0.9 0.9 ± 0.2 1.7 ± 0.0 12.8 ± 1.7 13.8 ± 1.2 0.8 ± 0.2 0.7 ± 0.1 1.8 ± 0.4 2.0 ± 0.6 1.5 ± 0.1 ND ND 2.3 ± 0.2 0.8 ± 0.1 1.9 ± 0.3 6.1 ± 0.9 58.7 ± 14.5 0.2 ± 0.0 0.2 ± 0.0 0.1 ± 0.1 1.4 ± 0.3 0.6 ± 0.2 0.5 ± 0.2 8.4 ± 2.1 48.5 ± 3.3 1.2 ± 0.3 2.2 ± 0.7 25.4 ± 11.8 1246.9 ± 70.5 2.0 ± 0.4 1.9 ± 0.1 2.4 ± 1.2 19.2 ± 7.7 0.1 ± 0.0 0.0 ± 0.0 0.1 ± 0.0 0.3 ± 0.2 0.7 ± 0.1 0.9 ± 0.2 0.7 ± 0.1 2.8 ± 0.0 1.7 ± 0.3 2.0 ± 0.4 1.5 ± 0.1 3.0 ± 1.2 0.8 ± 0.2 0.6 ± 0.1 0.6 ± 0.1 1.0 ± 0.2 0.1 ± 0.0 0.1 ± 0.0 0.4 ± 0.1 0.4 ± 0.1 1.5 ± 0.1 2.1 ± 0.9 1.4 ± 0.2 2.8 ± 0.9 1.0 ± 0.0 1.4 ± 0.4 1.0 ± 0.1 1.9 ± 0.3 0.0 ± 0.0 0.1 ± 0.0 0.1 ± 0.0 1.1 ± 0.1 1.0 ± 0.2 0.8 ± 0.2 0.9 ± 0.2 1.2 ± 0.1 1.2 ± 0.2 1.1 ± 0.1 3.1 ± 0.4 20.0 ± 1.8 1.0 ± 0.0 1.3 ± 0.2 1.7 ± 0.5 9.2 ± 2.5 1.3 ± 0.2 1.2 ± 0.3 2.4 ± 0.4 1140.9 ± 74.3 0.8 ± 0.1 0.5 ± 0.1 0.4 ± 0.0 1.1 ± 0.2 1.1 ± 0.1 1.5 ± 0.2 1.8 ± 0.1 6.1 ± 1.0 203 36 37 38 39 40 41 1.0 ± 0.0 1.1 ± 0.3 1.0 ± 0.2 1.0 ± 0.2 1.0 ± 0.2 1.4 ± 0.7 1.0 ± 0.2 1.8 ± 0.1 0.8 ± 0.3 0.4 ± 0.1 0.7 ± 0.1 1.1 ± 0.4 0.8 ± 0.0 1.4 ± 0.2 0.6 ± 0.0 0.3 ± 0.1 0.6 ± 0.0 1.4 ± 0.4 1.0 ± 0.2 0.8 ± 0.2 0.9 ± 0.1 2.8 ± 0.2 1.0 ± 0.0 86.1 ± 35.9 1.2 ± 0.1 1.7 ± 0.3 1.8 ± 0.3 0.5 ± 0.1 1.3 ± 0.1 46.1 ± 11.5 204 Appendix iii: List of publications 1. K. Kandasamy, J. K. C. Chuah, R. Su, P. Huang, K. G. Eng, S. Xiong, Y. Li, C. S. Chia, L. H. Loo, and D. Zink, "Prediction of drug-induced nephrotoxicity and injury mechanisms with human induced pluripotent stem cell-derived cells and machine learning methods," (2014) manuscript submitted 2. R. Su, Y. Li, D. Zink and L. H. Loo, "Supervised prediction of drug-induced nephrotoxicity based on interleukin-6 and -8 expression levels," BMC Bioinformatics, 15 (2014) S16 3. Y. Li, K. Kandasamy, J. K. C. Chuah, Y. N. Lam, W. S. Toh, Z. Y. Oo, and D. Zink, "Identification of Nephrotoxic Compounds with Embryonic Stem Cell-Derived Human Renal Proximal Tubular-Like Cells," Molecular Pharmaceutics, 11 [7] (2014) 1982-1990 4. H. Y. Tiong, P. Huang, S. Xiong, Y. Li, A. Vathsala, and D. Zink, "Drug-Induced Nephrotoxicity: Clinical Impact and Pre-Clinical In Vitro Models," Molecular Pharmaceutics, 11 [7] (2014) 1933-1948 5. Y. Li, Z. Y. Oo, S. Y. Chang, P. Huang, K. G. Eng, J. L. Zeng, A. J. Kaestli, B. Gopalan, K. Kandasamy, F. Tasnim and D. Zink, "An In Vitro Method for the Prediction of Renal Proximal Tubular Toxicity in Humans," Toxicology Research, 2 (2013) 352-365 6. Y. Li, A. M. Sawvel, Y.-S. Jun, S. Nownes, M. Ni, D. Kudela, G. D. Stucky and D. Zink, "Cytotoxicity and Potency of Mesocellular Foam-26 in Comparison to Layered Clays Used as Hemostatic Agents," Toxicology Research, 2 (2013) 136-144 7. Y. Li, Y. Zheng, K. Zhang, J. Y. Ying, and D. Zink, "Effects of Quantum Dots on Different Renal Proximal Tubule Cell Models and on Gel-Free Renal Tubules Generated In Vitro," Nanotoxicology, 6 (2012) 121-133 8. M. Ni, P.K. Zimmermann, K. Kandasamy, W. Lai, Y. Li, M.F. Leong, A.C.A. Wan, D. Zink, "The Use of a Library of Industrial Materials to Determine the Nature of SubstrateDependent Performance of Primary Adherent Human Cells," Biomaterials, 33 (2012) 353-364 9. F. Tasnim, R. Deng, M. Hu, S. Liour, Y. Li, M. Ni, J. Y. Ying and D. Zink, "Achievements and Challenges in Bioartificial Kidney Development," Fibrogenesis and Tissue Repair, 3 [14] (2010) DOI: 10.1186/1755-1536-3-14 205 [...]... cells for the development of in vitro models. 19 1.4 Role of inflammation in drug-induced nephrotoxicity in humans In the development of in vitro models for the prediction of drug-induced nephrotoxicity, it is important to understand the cellular pathways and molecular processes underlying the potential endpoints In another study from our group, it was found that the nuclear factor kappa B (NF-B) often... nephrotoxicity, and in phase 3 clinical trials this percentage increases drastically to 19% [4] The major reason for this is the low predictivity of animal tests, usually due to interspecies variability Therefore, in vitro models based on human cells are recently gaining more interests among drug developers 11 1.2 In vitro models for the prediction of drug-induced nephrotoxicity The interest in in vitro. .. material for the in vitro nephrotoxicology model In addition to cell types and cell culture substrates, another important aspect of developing an in vitro nephrotoxicology model is to identify appropriate endpoints For toxicity testing, endpoints that measure general cytotoxicity are frequently used These include cell death, metabolic activity or ATP depletion However, use of such endpoints for determining... http://www.predict-iv.toxi.uni-wuerzburg.de/periodic_reports/ 16 for the detection of nephrotoxicant-induced AKI [83] It is thus interesting to further evaluate these biomarkers in cultured PTCs treated with PT -specific nephrotoxicants to address their usefulness in the prediction of drug-induced nephrotoxicity For screening of large numbers of new drug candidates, it is also important that the in vitro model is economically self-sustainable and compatible... models Therefore it remains challenging to accurately detect drug-induced nephrotoxicity in early stages of drug development [4] With respect to human cells, the human kidney-2 (HK-2) cell line is commonly used for nephrotoxicity testing (for example, see [56, 57]) However, one major problem with the use of cell lines is the functional changes that had occurred in the cells during immortalization HK-2... and validated an in vitro model for the prediction of drug-induced nephrotoxicity using HPTCs cultured on polystyrene-based multiwall plates The endpoints used were increased expression levels of the pro-inflammatory cytokines IL-6 and IL-8 The relationships between the up-regulation of these cytokines and the nuclear translocation of NF-B p65 in human proximal tubular cells were investigated The... Cyclosporin A Citrinin Tenofovir No 23 24 25 26 27 28 29 30 31 32 33 Group 2 Non-PT -specific nephrotoxicants Compound Vancomycin Phenacetin Acetaminophen Ibuprofen Furosemide Lithium Chloride Lindane Ethylene glycol Valacyclovir Lincomycin Ciprofloxacin No 34 35 36 37 38 39 40 41 Group 3 Non-nephrotoxic compounds Compound Ribavirin Glycine Dexamethasone Melatonin Levodopa (DOPA) Triiodothyronine Acarbose... data indicated that under static conditions, the stiffness of the underlying substrate seemed to play a dominant role in supporting primary cell performance [5] However, the impact of material stiffness on the performance of cultured primary human soft tissue cells has not been systematically characterized before Therefore it is important to 15 investigate how this factor can affect the performance of. .. pharmacokinetics modeling and better absorption models (for example, the Caco-2 cell line as a model for intestinal epithelial permeability [48]) On the contrary, toxicity prediction could not be improved and still remains as the major reason for drug attrition [46, 47] Nevertheless, bioavailability and biodistribution principles have limited relevance to in vitro toxicity models, where the choice of cell... wound sites In addition to such cell types HPTC were included for comparison Cell type -specific responses were investigated by comparing the toxic effects of different hemostatic agents in various cell types This work was done in collaboration with Professor Galen Stucky’s team (University of California, Santa Barbara (UCSB)) 22 3 Identify suitable endpoints (with the use of HPTCs) for an in vitro model