Pharmacokinetics in drug development problems and challenges in oncology, volume 4

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Peter L. Bonate · Danny R. Howard Editors Pharmacokinetics in Drug Development Problems and Challenges in Oncology, Volume Pharmacokinetics in Drug Development Peter L Bonate • Danny R Howard Editors Pharmacokinetics in Drug Development Problems and Challenges in Oncology, Volume Editors Peter L Bonate Pharmacokinetics/Modeling/Simulation 2N.184 Astellas Global Clinical Pharmacology and Exploratory Development Northbrook, IL, USA Danny R Howard Oncology Clinical Pharmacology Novartis East Hanover, NJ, USA ISBN 978-3-319-39051-2 ISBN 978-3-319-39053-6 DOI 10.1007/978-3-319-39053-6 (eBook) Library of Congress Control Number: 2004051818 © Springer International Publishing Switzerland 2016 This work is subject to copyright All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed The use of general descriptive names, registered names, trademarks, service marks, etc in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer International Publishing AG Switzerland Contents Overview of Oncology Drug Development Laeeq Malik and Steven Weitman Overview of Oncology Biomarkers Mitsukuni Suenaga, Heinz-Josef Lenz, and Stefan J Scherer 29 A Global Perspective on First-in-Man Dose Selection: Oncology and Beyond Peng Zou, Sau Lee, Min Li, Lawrence Yu, and Duxin Sun 39 Controversies in Oncology: Size Based vs Fixed Dosing Peter L Bonate 59 Clinical QTc Assessment in Oncology Margaret R Britto and Nenad Sarapa 77 Expediting Drug Development: Breakthrough Therapy Designation 107 Carmen Ladner Pharmacokinetics and Pharmacodynamics of Tyrosine Kinase Inhibitors 121 Ana Ruiz-Garcia and Kenji Yamazaki Combination Development 151 Annie St-Pierre, Maribel Reyes, and Vincent Duval Role of Pharmacokinetics: Pharmacodynamics in Biosimilar Assessment 175 Antonio da Silva and Didier Renard 10 Pharmacokinetics and Pharmacogenetics of Metronomics 189 Nicolas André, Joseph Ciccolini, Marie Amélie Heng, and Eddy Pasquier v vi Contents 11 Modeling Tumor Growth in Animals and Humans: An Evolutionary Approach 209 Dean C Bottino and Arijit Chakravarty 12 Practical Considerations for Clinical Pharmacology in Drug Development: A Survey of 44 FDA Oncology Approvals 237 Danny R Howard 13 New Advancements in Exposure-Response Analysis to Inform Regulatory Decision Making 303 Liang Zhao, Li Hongshan, Anshu Marathe, Jingyu (Jerry) Yu, Dinko Rekić, Nitin Mehrotra, Vikram Sinha, and Yaning Wang Index 319 Contributors Nicolas André Department of Pediatric Hematology and Oncology, AP-HM, La Timone Hospital, Marseille, France Peter L Bonate Pharmacokinetics/Modeling/Simulation 2N.184 Astellas, Global Clinical Pharmacology and Exploratory Development, Northbrook, IL, USA Dean C Bottino Takeda Pharmaceuticals International Co., Cambridge, MA, USA Margaret R Britto Pharmacokinetics/Pharmacodynamics Quintiles, Inc., Overland Park, KS, USA Arijit Chakravarty Takeda Pharmaceuticals International Co., Cambridge, MA, USA Joseph Ciccolini SMARTc Aix Marseille Université, INSERM, Center for Research in Oncobiology and Oncopharmacology UMR_S 911, Marseille, France Vincent Duval Novartis Pharma AG, Basel, Switzerland Marie Amélie Heng Department of Pediatric Hematology and Oncology, AP-HM, La Timone Hospital, Marseille, France Li Hongshan Division of Pharmacometrics, Office of Clinical Pharmacology, Center for Drug Evaluation and Research, U.S Food and Drug Administration, Silver Spring, MD, USA Danny R Howard Novartis Pharmaceuticals, East Hanover, NJ, USA Carmen Ladner Five Prime Therapeutics Inc., South San Francisco, CA, USA Sau Lee Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA Min Li Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA vii viii Contributors Heinz-Josef Lenz USC Norris Cancer Center, Los Angeles, CA, USA Laeeq Malik Capital Region Cancer Centre, The Canberra Hospital, Garran, Australia Anshu Marathe Division of Pharmacometrics, Office of Clinical Pharmacology, Center for Drug Evaluation and Research, U.S Food and Drug Administration, Silver Spring, MD, USA Nitin Mehrotra Division of Pharmacometrics, Office of Clinical Pharmacology, Center for Drug Evaluation and Research, U.S Food and Drug Administration, Silver Spring, MD, USA Eddy Pasquier Center for Research in Oncobiology and Oncopharmacology UMR_S 911 Aix Marseille Université, Marseille, France Metronomics Global Heath Initiative, Marseille, France Dinko Rekić Division of Pharmacometrics, Office of Clinical Pharmacology, Center for Drug Evaluation and Research, U.S Food and Drug Administration, Silver Spring, MD, USA Didier Renard Advanced Quantitative Sciences, Novartis Pharma AG, Basel, Switzerland Maribel Reyes Novartis Pharma AG, Basel, Switzerland Ana Ruiz-Garcia Clinical Pharmacology, Global Research and Development Pfizer, San Diego, CA, USA Nenad Sarapa Clinical Sciences Oncology, Bayer Healthcare, Inc., Whippany, NJ, USA Stefan J Scherer VP Global Head Correlative Science, Novartis Pharmaceuticals Corporation, One Health Plaza, East Hanover, NJ, USA Antonio da Silva Preclinical Development, Hexal AG, A Sandoz Company Part of the Novartis Group, Holzkirchen, Germany Vikram Sinha Division of Pharmacometrics, Office of Clinical Pharmacology, Center for Drug Evaluation and Research, U.S Food and Drug Administration, Silver Spring, MD, USA Annie St-Pierre Novartis Pharma AG, Basel, Switzerland Duxin Sun Department of Pharmaceutical Sciences, College of Pharmacy, The University of Michigan, Ann Arbor, MI, USA Mitsukuni Suenaga Department of Gastroenterological Chemotherapy, Cancer Institute Hospital of Japanese Foundation for Cancer Research, Koto-ku, Tokyo, Japan Contributors ix Yaning Wang Division of Pharmacometrics, Office of Clinical Pharmacology, Center for Drug Evaluation and Research, U.S Food and Drug Administration, Silver Spring, MD, USA Steven Weitman Institute for Drug Development, Cancer Therapy and Research Center, University of Texas Health Science Center, San Antonio, TX, USA Kenji Yamazaki Pharmacokinetics Drug Metabolism, WW Research and Development, Pfizer, San Diego, CA, USA Lawrence Yu Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA Jingyu (Jerry) Yu Division of Pharmacometrics, Office of Clinical Pharmacology, Center for Drug Evaluation and Research, U.S Food and Drug Administration, Silver Spring, MD, USA Liang Zhao Division of Pharmacometrics, Office of Clinical Pharmacology, Center for Drug Evaluation and Research, U.S Food and Drug Administration, Silver Spring, MD, USA Peng Zou Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA About the Editors Peter L Bonate has acquired over 22 years of industrial experience: 19 years as a clinical pharmacologist/pharmacokineticist and years in drug metabolism and bioanalysis He is currently Executive Director of Pharmacokinetics, Modeling, and Simulation at Astellas He received his Ph.D from Indiana University in Medical Neurobiology with an emphasis on the pharmacokinetics of drugs of abuse He also received an M.S in Statistics from the University of Idaho and an M.S in Pharmacology from Washington State University He is a Fellow of the American College of Clinical Pharmacology and American Association of Pharmaceutical Scientists (AAPS) Within AAPS, he was a founder of the Pharmacometrics focus group, was chair of the Clinical Pharmacology and Translational Research Section, and was AAPS Fellows Committee Chair Dr Bonate is a recipient of the AAPS Research Achievement Award in Clinical Pharmacology and Translational Research He is currently an Associate Editor of the Journal of Pharmacokinetics and Pharmacodynamics He has served or currently serves on the editorial boards for the Journal of Clinical Pharmacology, Pharmaceutical Research, and the AAPS Journal He has written more than 60 peer-reviewed publications and is author of the books Pharmacokinetic-Pharmacodynamic Modeling and Simulation, 2nd edition and Be a Model Communicator (and sell your models to anyone) Danny R Howard received his Bachelor of Science degree in Pharmacy, and Ph.D from the University of Missouri in Kansas City He joined Novartis as the Head of Global Pharmacokinetics and Pharmacodynamics and is currently the Vice President of Oncology Clinical Pharmacology for the Novartis Oncology Business Unit He began working in the pharmaceutical industry first as a biopharmaceutics consultant and then as a pharmaceutical scientist for Marion Merrell Dow, Hoechst Marion Roussel, Aventis, and Quintiles His career has included responsibilities in both clinical and nonclinical pharmacokinetics and pharmacodynamics, bioanalytics, pharmaceutical business operations, and drug metabolism and pharmacokinetics He has worked with numerous worldwide new drug submissions supporting both large and small molecules, within and outside the area of oncology He was a charter member of the Missouri Biotech Association and served as its first Board Chairman xi 13 FDA Exposure-Response Summary 315 E-R analyses have been routinely applied in regulatory reviews to address key questions such as whether the dosing regimen for a new drug is optimal in terms of risk/ benefit for every patient Different from the traditional dose–response analysis, E-R analyses are applied to identify the safe and effective exposure for each patient even though data from most clinical trials are limited to provide sufficient information to derive individual E-R relationships Such an effort is consistent with the Precision Medicine Initiative advocated by President Barack Obama (Obama 2015) Once a drug product is selected to treat a certain disease, drug exposure becomes the most important factor to be optimized in order to achieve balanced risk/benefit at the individual level Despite the challenges to derive the safe and effective exposure for each patient based on the data from the typical clinical trials, advances in E-R analyses, combined with novel clinical trial designs, will take us closer to the era of Precision Medicine when a truly individualized regimen can be applied to treat every patient Acknowledgments Disclaimer: Opinions expressed in this chapter are those of the authors and not necessarily reflect the views or policies of the FDA References Argatroban label (2011) http://www.accessdata.fda.gov/drugsatfda_docs/label/2012/203049s000lbl pdf Accessed June 2015 Bruno R, Lu J-FF, Sun Y-NN, Claret L (2011) A modeling and simulation framework to support early clinical drug development decisions in oncology J Clin Pharmacol 51:6–8 Casak S, Fashoyin-Aje I, Lemery S et al (2015) FDA approval summary: ramucirumab for gastric cancer Clin Cancer Res 21(15):3372–3376 Claret L, Jonsson F, Knight R (2010a) A drug independent tumor burden reduction-survival model in patients with multiple myeloma to support early clinical development decisions Presented at: 6th international symposium on measurement and kinetics of in vivo drug effects; Noordwijkerhout, The Netherlands, April 21–24 2010 Claret L, Girard P, Hoff PM, Van Cutsem E, Zuideveld KP, Jorga K et al (2009) Model-based prediction of phase III overall survival in colorectal cancer on the basis of phase II tumor dynamics J Clin Oncol 27:4103–4108 Claret L, Lu J-FF, Sun Y-NN, Bruno R (2010b) Development of a modeling framework to simulate efficacy endpoints for motesanib in patients with thyroid cancer Cancer Chemother Pharmacol 66:1141–1149 Committee for Medicinal Products for Human Use (CHMP) (2014) Guideline on the investigation of subgroups in confirmatory clinical trials http://www.ema.europa.eu/ema/doc_index jsp?curl=pages/includes/document/documentdetail.jsp?webContentId=WC500160523&m url=menus/document_library/document_library.jsp&mid=0b01ac058009a3dc Accessed June 2015 Douillard JY, Ostoros G, Cobo M, Ciuleanu T (2014) First-line gefitinib in Caucasian EGFR mutation-positive NSCLC patients: a phase-IV, open-label, single-arm study Br J Cancer 110:55–62 316 L Zhao et al Giaccone G, Herbst RS, Manegold C et al (2004) Gefitinib in combination with gemcitabine and cisplatin in advanced non-small-cell lung cancer: a phase III trial—INTACT J Clin Oncol 22(5):777–784 Herbst RS, Giaccone G, Schiller JH et al (2004 Mar 1) Gefitinib in combination with paclitaxel and carboplatin in advanced non-small-cell lung cancer: a phase III trial—INTACT J Clin Oncol 22(5):785–94 Goldstein D, El-Maraghi RH (2015) Nab-paclitaxel plus gemcitabine for metastatic pancreatic cancer: long-term survival from a phase III trial J Natl Cancer Inst 107:1–10 Gridelli C, Marinis DF, Maio DM, Cortinovis D (2011) Gefitinib as first-line treatment for patients with advanced non-small-cell lung cancer with activating epidermal growth factor receptor mutation: review of the evidence Lung Cancer 71:249–257 Hoffmann-La Roche (2011) A study of trastuzumab emtansine in comparison with treatment of physician’s choice in patients with HER2-positive breast cancer who have received at least two prior regimens of HER2-directed therapy (TH3RESA) https://clinicaltrials.gov/ct2/show/ NCT01419197 Accessed June 2015 Jin R, Li H, Zhang L et al (2015) Exposure-response (E-R) and case-control analyses of ramucirumab leading to recommendation for dosing optimization in patients with gastric cancer Abstract presented at ASCO Lagakos SW (2006) The challenge of subgroup analyses-reporting without distorting N Engl J Med 354:1667–1669 Lynch TJ, Bell DW, Sordella R (2004) Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib N Engl J Med 350:2129–2139 Madabushi R, Cox DS, Hossain M et al (2011) Pharmacokinetic and pharmacodynamic basis for effective argatroban dosing in pediatrics J Clin Pharmacol 51:19–28 Mager DE, Wyska E, Jusko WJ (2003) Diversity of mechanism-based pharmacodynamic models Drug Metab Dispos 31:510–519 Minasian L, Rosen O, Auclair D, Rahman A (2014) Optimizing dosing of oncology drugs Clin Pharmacol Therapeut 96:572–579 Obama BH (2015) The State of the Union Address https://www.whitehouse.gov/the-pressoffice/2015/01/20/remarks-president-state-union-address-january-20-2015 Accessed June 2015 O’Shaughnessy J, Schwartzberg L (2014) Phase III study of iniparib plus gemcitabine and carboplatin versus gemcitabine and carboplatin in patients with metastatic triple-negative breast cancer J Clin Oncol 32:3840–3847 Promacta label (2014) http://www.accessdata.fda.gov/drugsatfda_docs/label/2014/022291s012lbl pdf Accessed June 2015 Thornton K, Kim G, Maher VE, Chattopadhyay S (2012) Vandetanib for the treatment of symptomatic or progressive medullary thyroid cancer in patients with unresectable locally advanced or metastatic disease: U.S Food and Drug Administration Drug Approval Summary Clin Cancer Res 18:3722–3730 U.S FDA (2010) Fingolimod Clinical Pharmacology Review http://www.accessdata.fda.gov/ drugsatfda_docs/nda/2010/022527Orig1s000clinpharmr.pdf Accessed June 2015 U.S FDA (2003) Guidance for Industry: Exposure-Response Relationships—Study Design, Data http://www.fda.gov/Drugs/ Analysis, and Regulatory Applications GuidanceComplianceRegulatoryInformation/Guidances/UCM072109 Accessed June 2015 U.S FDA (2014) Kadcyla approval letter http://www.accessdata.fda.gov/drugsatfda_docs/ label/2014/022291s012lbl.pdf Accessed June 2015 Wang J, Song P, Schrieber S et al (2014a) Exposure-response relationship of T-DM1: insight into dose optimization for patients with HER2-positive metastatic breast cancer Clin Pharmacol Therapeut 95:558–564 Wang Y, Harigaya Y, Cavaillé-Coll M et al (2014b) Justification of non-inferiority margin: methodology considerations in an exposure-response analysis Clin Pharmacol Therapeut 97:404–410 13 FDA Exposure-Response 317 Wang Y, Sung C, Dartois C et al (2009) Elucidation of relationship between tumor size and survival in non-small-cell lung cancer patients can aid early decision making in clinical drug development Clin Pharmacol Therapeut 86:167–174 Yang J, Zhao H, Garnett C, Rahman A et al (2013) The combination of exposure-response and case-control analyses in regulatory decision making J Clin Pharmacol 53:160–166 Yusuf S, Wittes J, Probstfield J, Tyroler HA (1991) Analysis and interpretation of treatment effects in subgroups of patients in randomized clinical trials JAMA 266:93–98 Index 0-9, and Symbols + “Up & Stop” design, 6–7 A Abiraterone, 246, 265 Abiraterone (an antiandrogen), 258 Absolute neutrophil counts (ANC), 182, 185 Absorption acid-reducing agent interaction, 267–272 high-fat food interaction, 259–265 non-high-fat meals, 265–267 Accelerated Titration Designs, 9–10 Acid-reducing agent interaction cabozantinib, 271 ceritinib, 271 coadministration, 270 crizotinib, 272 dabrafenib, 271 DDI, 271, 272 drug interaction, 267 evaluation, 267–269 FDA reviewer, 270 gated approach, 271 H2-blockers, 271 idelalisib, 270, 271 panobinostat, 270 PBPK, 270 pharmacometric analyses, 270 pH-dependent solubility, 267 ponatinib, 272 PopPK, 270 PPI, 271 PPIs, 267, 271 vandetanib, 270 vismodegib, 271, 272 Adaptive Randomization Design, 14–15 Ado-trastuzumab, 252 Ado-trastuzumab emtansine, 252, 307, 308 Afatinib, 255, 256 Akaike Information Criterion (AICc) score, 222 Alkylating agents, 198, 199 American Society of Clinical Oncology (ASCO), 72 Anaplastic lymphoma kinase (ALK), 32 Angiogenesis inhibitors, 192, 200 Antibody–drug conjugates (ADCs), 55, 211 Anti-CD20 rituximab biosimilars, 182 Antimicrotubule agents, 197 Antimitotics, 212 Antitumor agents, 228 Antitumor efficacy, 131–133 Approval database, FDA Breakthrough Therapy Designation, 240 CDER, 239 Drugs@FDA, 239 formulation and route of administration, 246–247 oncology therapeutics, 240–245 patients/healthy volunteers, 240–246 priority review, 240 Arsenic trioxide, 78, 82 Axitinib, 140, 141, 248, 295, 296 B Bayesian designs, 11, 13 BCR-ABL transcript levels, 228 Belinostat, 252 © Springer International Publishing Switzerland 2016 P.L Bonate, D.R Howard (eds.), Pharmacokinetics in Drug Development, DOI 10.1007/978-3-319-39053-6 319 320 Beta-adrenergic positive tumors, 195 Beta-III tubulin isotypes, 195 Bevacizumab, 195 BiaCore®, 181 Binocrit® (epoetin alfa), 184 Biochemical model, 216 Biologics, dose selection strategies antibody-drug, 250 clinical pharmacology, 251 dose– and exposure–response, 250 dose and regimen justification, 250 exposure–response, 250 ipilimumab, 251 monoclonal antibodies, 250 MTD, 250 nivolumab, 250 pembrolizumab, 250 pertuzumab, 251 PFS, 251 protein drugs, 250 therapeutic index, 250 Biomarkers, pharmacology ALK, 32 breast cancer, 32 colon cancer, 31 EGFR signaling pathway, 31 lung cancer, 32–33 measurement, 33–34 predictive marker, 30 predictive” and “prognostic” factors, 30 prognostic biomarkers, 30 Biosimilar assessment analytical considerations, 179, 181 definition, 178 design space, 177 guidance, 178–179 in EU, 180 noninferiority studies, 179 physicochemical and purity profiles, 176 PK/PD studies, 182 recombinant therapeutic proteins, 176 sensitive analytical testing, 176 structural modifications, 176 therapeutic IgG1/antibody, 177 Blinatumomab, 289, 290 Bluetooth-enabled ECG recording devices, 97 Body surface area (BSA), 41 cyclophosphamide, 61 human blood volume, 60 methotrexate, 61 MTD, 61 pharmacokinetic parameters, 61 pharmacokinetic rationale, 62–64 therapeutic dose, 60 Index Bootstrap analyses, 217 Bortezomib, 252 Bosutinib, 263, 292, 295 BRAF V600K or V600E mutations, 152 Breakthrough therapy, 112 Breakthrough Therapy Designation, 111, 238, 240 Breast cancer, 32 Brentuximab vedotin, 252 C Cabazitaxel, 252 Cabozantinib, 246, 248, 254, 265, 271 Calvert formula AUC, 71 chemotherapy experience, 71 concentration-time profiles, 71 GFR, 71 Cancer, 213–216 cell lines, 212 clonal diversity, 211, 212 Darwinian evolution, 211, 213 deep sequencing technology, 211 differential survival, clones upon treatment, 211, 212 mutational spectra, 212 options and limitations biochemical model, 216 cell biological models, 216 Gompertz model, 213 IACUC guidelines, 213 in vivo xenograft setting, 213 mechanistic data, 216 microenvironmental models, 216 Simeoni model, 213 tumor kinetic model schematics and equations, 213–216 polyclonal disease, 212 stochastic disease progression, 212 stochastic progression, 211 xenograft tumors, 212 Cancer Cell Line Encyclopedia (CCLE) project, 154 Cardiac arrhythmias, 80 Cardiac repolarization, 98 Cardiac safety Research Consortium (CSRC), 90 Carrying-capacity-limited growth, 213 CD133+ precursor cells, 193 Cell biological models, 216 Center for Devices and Radiological Health (CDRH), 117 Center for Drug Evaluation and Research (CDER), 111, 239 321 Index Ceritinib, 91, 99, 246, 254, 263, 264, 271 Change in tumor size (CTS), 225, 227 Chemotherapy, 238 CHOP regimen, 252 Chou–Talalay method, 155 Chronic lymphocytic leukemia, 270 Chronic myeloid leukemia (CML), 154, 228 Claret/Bruno TGI model, 230, 231 Classic ICH E14-type model, 86 Clinical trials, QTc assessment arrhythmic events, 101 ECG acquisition, 97 formal analysis, 83 post-approval phase, 80 preapproval, 78, 94 thorough DQT, 95 Colorectal carcinoma, 226 Combination drugs development, 155 in vivo models, 156 RNA sequence libraries, 168 Combination therapy acute childhood leukemia, 152 clinical development, 160 clinical pharmacology, 166–167 codevelopment, 159 imatinib, 152 in vitro assessment, 153–155 in vivo assessment, 156, 158 linear pathway, 152 phase 1, 160–164 phase and 3, 164–165 preclinical development, 153 preclinical safety, 158–159 targeted therapies, 152 trastuzumab, 152 tyrosine kinase inhibitors, 152 Complete Response (CR), 228 Comprehensive in vitro Proarrhythmia Assay (CiPA), 80 Concentration-QTc (C-QTc) advantages, 85, 98, 99 applications, 85 cardiac repolarization, 98 ceritinib, 99 ECG data, 89 estimation, 99 exposure-QTc response, 79 exposure-response modeling, 86 false positive outcome, 98 FDA, 91, 99 FDA’s QT-IRT group, 98 hERG channel trafficking, 99 ICH E14 Q&A R3 document states, 96 ICH regulators, 98 implementation, E14 guidance, 79 IQ-CSRC study, 91 IUT, 98 linear modeling, 99 LME model, 98, 100 magnitude of QTc effect, 99 nonlinear PK/PD models, 99 regulatory reviews, TQT study, 98 relationship, 89 sample sizes, 99 simulate/predict effects, 99 single observation in statistical, 96 single or multiple studies, 92 sufficient data, 100 tests for hysteresis, 99 vandetanib, 99 Continual reassessment method (CRM), 11 Conventional cytotoxic agents, 48 Cox model, 306 Crizotinib, 272 Cycle chemotherapy, 185 Cyclophosphamide, 199 CYP3A4 enzyme, 166–167 Cytochrome P450 iso-enzymes, 166 Cytotoxic agents, 2, 238, 239 Cytotoxic drugs, 248 Cytotoxics, dose selection strategies ado-trastuzumab emtansine, 252 belinostat, 252 brentuximab vedotin, 252 cabazitaxel, 252 cell proliferation and maturation, 251 CHOP regimen, 252 FDA request, 252 HDAC inhibitors, 251, 252 panobinostat, 252 post-marketing request, 253 post-marketing requests, 252 post-marketing study, 252 romidepsin, 252 D Dabrafenib, 166, 271 Dabrafenib pharmacokinetics and pharmacodynamics, 258 Darwinian evolution, 211, 213 Dasatinib, 135 Data-mining approach, 211 Deep sequencing technology, 211 Definitive QTc (DQTc) design, 87 ECG study, 86 322 Definitive QTc (DQTc) (cont.) ECGs, 89 in oncology, 86, 90, 92 pivotal trial in cancer patients, 88 preclinical cardiac safety pharmacology, 102 small sample sizes, 91 thorough, 87–89, 93–96, 99, 100 Density-independent fitness, 217 Digoxin, 195 Dinutuximab, 248 Disease-free survival (DFS), 18 Dose and regimen identification, 250–259 characterizing exposure–response, 248–250 cytotoxic drugs, 248 development of oncology agents, 247 dose selection strategies biologics, 250–251 cytotoxics, 251–253 first-in-human trials, 247 ipilimumab, 247 MTD, 247 non-cytotoxic targeted agents (see Non-cytotoxic targeted agents) OBD, 247 omacetaxine, 247 post-marketing commitments, 247 radium-223, 247 Dose selection strategies biologics, 250–251 cytotoxics, 251–253 Dose-limiting toxicity (DLT), 95, 96 Dosing and schedules, 196 computational pharmacology, 194 D1–D2–D4 basis, 194 drug discovery, 59 levels, 193 P450 inhibitor, 198 pharmacokinetics and oncology, 64–67 pharmacometric approach, 67–69 PK, 197 Drug development ADME studies, 166 anticancer agents, 153 ectopic and orthotopic xenograft, 156 efficacy, 156 oncology, 163 PK, 153 predictive value, 153 Drug repositioning (DR), 190 Drug-related proarrhythmia, 79 Drug-resistant clone, 219, 220 Drug-sensitive clone, 219, 220 Index Durable response model, 221 Duration of response (DoR), 165 E Electrocardiogram (ECG) assessment ceritinib, 91 classic ICH E14-type development program, 86 C-QTc modeling, 92 C-QTc relationship, 89 dedicated QTc study, 89 development programs, 94 dose reduction, 94 DQT study, 86 drug classification, 87 drug development plan, 85 FDA, 93 flexibility and frequent assessment, 94 front-loaded development program, 89–92 full-development program, 92–94 HDAC, 85 healthy-volunteer thorough DQT study, 88 IQ-CSRC study, 91 Late Phase (Phase 3) development, 89 late-phase trials, 91 monitoring, 87 MTD, 90 nonclinical ADME, 87 nonclinical cardiovascular safety testing, 86 PK exposures, 88 POC study, 94 ponatinib, 91 pooling of data, 92 positive control, 88 preapproval clinical trials, 94 preapproval oncology trials, 85 predose, 90 proarrhythmic risk, 87 proof-of-concept study, 90 RD, 90 registration trials, 94 RTK inhibitors, 85 safe and cost-effective, 91 serial time-matched baseline, 91 single-/multiple-dose trials, 86 sponsor financial resources, 92 stress, 94 supratherapeutic doses, 88 thorough DQT study, 87–89 Vandetanib, 93 Vismodegib, 87 Electrolyte imbalance, 95 Emax models, 94 Index Enzalutamide, 246, 258, 259 Epidermal growth factor receptor (EGFR, 31 Erlotinib, 141 European Medicines Agency (EMA), 159, 183, 267 Everolimus (EVR), 264, 295, 309 Evolutionary dynamics model, 228 clinical development antitumor agents, 228 CR, 228 PD, 228 PR, 228 RECIST, 228 SD, 228 SLD, 228 population biology model, 216 small-molecule drugs, 216 tumor kinetic (see Tumor kinetic modeling) xenograft studies, 217–218 Evolutionary model parameters, 224 Expedited access PMA (EAP) program, 117 Expedited Programs for Serious Conditions— Drugs and Biologics, 118 Exposure-QTc response, 79 Exposure-Response (E-R) analyses, 252 case–control studies, 306 count-annualized relapse, 304 dosing recommendation, 303 drug efficacy, 308 noninferiority trial, 308, 310 pairwise comparisons, 304 pharmacokinetics, 304 phase trials, 304 regulatory setting, 305 subgroup analysis, 311, 312 Exposure–response relationship, 248–250 F Familial adenomatous polyposis (FAP), 31 FDA Clinical Pharmacology reviewer, 256 FDA Guidance for Industry, 272 FDA Safety and Innovation Act (FDASIA), 110 FDA’s QT-IRT group, 98 Filgrastim, 185 Financial considerations, 85 First in human (FIH) studies, 153 anti-cancer drug, 40 cytotoxic agents, 47–50 EMA guideline, 47 NOAEL Approach, 41–44 Regulatory Guidance, 41–46 323 STD10 and HNSTD approaches, 40 TGN1412, 46 First-In-Human Clinical Trials, 5–12 Fixed doses, 60 Fixed-dose combinations, 159 fluorouracil (5-FU), Food and Drug Administration (FDA), 304 and alignment, 112 application of processes, 298 Approval Letter, 239 approval paths, 109 biologic license, 111 biomedical innovation, 109 biopharmaceutics, 297 Breakthrough Therapy Designation, 238 breakthrough therapy designation program, 113 CDER Manual, 119 Clinical Pharmacology, 297 Clinical Pharmacology review, 257 Clinical Pharmacology reviewer, 256 CMC components, 116 CMC development, 115 codevelopment, drug, 116 collaboration, 240 communication, 240 daclatasvir, 118 drug development, 115 Drugs@FDA database, 239 efficacy/safety, 113 expectations, 115 expedited programs, 108 exposure–response analyses, 248 exposure–response analysis, 252 FDASIA and PDUFA V legislation, 118 feasibility and reproducibility issues, 109 food-effect guidance, 265 guidance, 240 Guidance for Industry, 272 interaction, 240 interactive communication, 115 new oncology therapeutics, 241–245 oncology drugs approved, 240 oral oncology agents, 260 phase 1-2-3 process, 110 prioritization, 118 public health priorities, 119 review, 251 reviewer’s independent analysis, 296 reviewer’s independent central tendency analysis, 296 reviewers, 248, 270, 271, 291 and sponsors, 110, 252 targeted therapies, 109 324 Food and Drug Administration (FDA) (cont.) therapy designation, 110 time period, 240 website, 239 Food and Drug Administration Modernization Act (FDAMA), 109 Fridericia’s correction (QTcF), 97 Front-loaded development program, 89–92 G Garbage-in-garbage-out (GIGO) variety, 211 GastroPlus®, 270 Gazyva®, 111, 112, 240 Gefitinib, 312 Gemcitabine, 312–313 Genomic biomarkers, 19 Glomerular filtration rate (GFR), 71 Gompertz model, 213 Granulocyte colony-stimulating factor (G-CSF), 185 Growth rate-based estimate of T/C, 217 H H2-blockers, 271 Hazard ratio, 226 Hedgehog medulloblastoma, 195 Heparin-induced thrombocytopenia (HIT), 314 High-fat food interaction abiraterone, 265 administering drugs, 264 bosutinib, 263 cabozantinib, 265 ceritinib, 263, 264 and characterization, 260–262 coadministration, 259 drug absorption and pharmacokinetic variability, 260 drugs measurement, 263 everolimus, 264 ibrutinib, 264, 265 oral oncology agents, 260 oral oncology drugs, 260 orally administered drug, 259 palbociclib, 264 panobinostat, 264 pomalidomide, 263, 264 post-marketing clinical study, 263 regorafenib, 263, 264 specification-limit, 260 vemurafenib, 263, 264 Histone deacetylase (HDAC) inhibitors, 85, 251, 252 Index Hodgkin lymphoma, Holter monitoring, 97 HT1080 fibrosarcoma-bearing rats, 198 Human equivalent dose (HED), 162 Human ether-a-go-go-related gene (hERG) potassium channels, 84 Hypertension, 152 I Ibrutinib, 246, 247, 257, 264, 265, 294 ICH regulators, 98 ICH S7A, 80 ICH S7B, 80 Idelalisib, 257, 270, 271, 295 Imatinib, 133–135 Immunoglobulin (IgG)-based therapeutics, 176 Immunotherapies, 238 In vitro companion diagnostic (IVD), 117 Iniparib, 313 Innovation Quality (IQ) Consortium, 90 Institutional Animal Use and Care Committee (IACUC) guidelines, 213 Institutional review boards (IRBs), 82 Intersection–union test (IUT), 89, 98, 99 Investigational Device Exemption (IDE) applications, 117 Investigational New Drug Application (IND), 4–5 Ipilimumab, 247, 251, 290 Irinotecan, 196 K Kaplan–Meier curves, 254, 307 Ketoconazole, 295 L Lenvatinib, 142, 248, 254, 292, 295 Leukemia, 239 Light-meal, 267 Linear mixed effects (LME) models, 98 Liver transaminases (AST and ALT), 289 Lung Cancer, 32–33 M MABEL Approach, 44 Mammalian target of rapamycin (mTOR), 155 Mantle cell lymphoma (MCL), 257 Mathematical modeling, 210 Maximally tolerated dose (MTD), 190, 254–259 Index non-cytotoxic, small molecule targeted agents approved at benefit–risk ratio, 254 cabozantinib, 254 ceritinib, 254 dose-optimization, 254 lenvatinib, 254 ponatinib, 255 post-marketing evaluation, 254 vandetanib, 254 non-cytotoxic, small molecule targeted agents approved below afatinib, 255, 256 compounds, 255 Dabrafenib pharmacokinetics and pharmacodynamics, 258 dose-escalation study for trametinib, 257 enzalutamide, 258, 259 FDA Clinical Pharmacology reviewer, 256 ibrutinib, 257 idelalisib, 257 MCL, 257 pazopanib, 256 PBPK model, 259 PK/PD modeling, 255 QD regimen, 256 QW and QD regimens, 259 renal cell carcinoma, 256 vismodegib, 256 Maximum administered dose (MAD), 51 Maximum target inhibition (MTI), 11 Maximum tolerated dose (MTD), 60, 237 antitumor therapy, 163 assay sensitivities, 162 clinical pharmacology, 162 codevelopment, 160 concentration–effect relationship, 163 dose-escalation methods, 160 dose–toxicity probability curves, 161 historical data, 162 in vitro/in vivo cell assays, 163 model-based designs, 161 phase trials, 161 PI3K/AKT/mTOR, 162 QTc assessment, 81 RP2D dose, 161 rule-based designs, 160 safety and PK, 160 systemic exploration, 162 Mechanistic data, 216 Mechanistic insights, 211 Medical Policy Council (MPC), 115 Medullary thyroid cancer (MTC), 304 325 Metastatic gastric cancer (mGC), 306 Methotrexate, Metronomic chemotherapy (MC), 190 Metronomics administration, 190 antiangiogenic paradigm, 191 antiangiogenic properties, 190 antiangiogenic therapies, 194 chemotherapy, 194–195 doses, 193–194 drug repositioning, 190 mechanisms of action, 193 personalized therapy, 194 pharmacogenetics, 199, 200 pharmacogenomics, 199, 200 phase I/II studies, 196 progression, 190 proimmune properties, 192 thrombospondin 1, 195 tumor initiating cells, 192 Microenvironmental models, 216 Mismatch repair deficiency (dMMR), 31 Model-fitted estimate of T/C (treated/control growth), 217 Modification of Diet in Renal Disease (MDRD) equation, 71 Molecularly targeted agent, 40 Monoclonal antibodies (mAbs), 52, 64, 65, 84 Moxifloxacin, 81 Myelogenous leukemia, N Nab-Paclitaxel, 312–313 National Cancer Institute (NCI) CTCAE, 95 National Cancer Institute (NCI) organ dysfunction working group, 272 National Cancer Institute 60 (NCI60), 153 National Health and Nutrition Examination Survey (NHANES), 69 Neoplastic disease, 81 Neupogen®, 185 Neutropenia, 253 New molecular entities (NMEs), 126 Nivolumab, 250 NLME mixture modeling, 228 NOAEL approach, 43 Non-antiarrhythmic drugs, 77–78 Nonclinical ADME, 87 Nonclinical cardiovascular safety testing, 86 Non-cytotoxic targeted agents, 254–259 exposure–response relationship, 248–250, 253 pharmacokinetic data, 253 326 Non-cytotoxic targeted agents (cont.) single dose and regimen, 253 small molecule below MTD, 255–259 MTD, 254–255 Non-high-fat meals axitinib, 267 compositions, various meal types, 265, 266 idelalisib, clinical pharmacology studies for, 267 and light-meal, 267 macronutrient distribution ranges, 265 moderate-fat and low-fat, 265 sponsor, everolimus, 267 vismodegib, 267 Non-Hodgkin’s lymphoma, 270 Nonlinear mixed effects (NLME) framework, 222 Nonlinear relationship, 311 Non-small cell lung cancer (NSCLC), 109 Nutrient-limited growth, 218 O Obesity, 72 Objective response rate (ORR), 305 Ofatumumab, 65, 294 Olaparib, 246, 248, 295 Omacetaxine, 247 Omnitrope®, 178 Oncology animal models, 156 biomarkers, 314 combination chemotherapy, 152 dose reductions, 159 drug development, 156 pharmacodynamics markers, 314 phase 1, 160–164 Tbo-filgrastim (Granix), 313 Oncology drug development biologic therapies, 3–4 chemotherapy, EGFR mutations, Two-Stage Designs, 13 Optimal biological dose (OBD), 163, 247 Organ impaired populations blinatumomab, 290 bosutinib, 292 clinical pharmacokinetic data, 290 clinical studies, 272 compounds, 291 creatinine clearance, 290 definitions, 272 evaluation, 272–288 Index FDA Guidance for Industry, 272 hepatic, 272, 289 idelalisib, 291 inclusion and exclusion criteria, 292 ipilimumab, 290 lenvatinib, 292 liver transaminases (AST and ALT), 289 monoclonal antibodies, 289 ponatinib, 291 PopPK assessments, 272 population pharmacokinetic assessment, 289, 290 post-marketing request, 291 ramucirumab, 290 renal, 272 renal impairment, 292 safety data, 291 small molecules, 291 sponsors, 289 TB, 289 vandetanib, 292 Other People’s Data (OPD), 211 Overall response rate (ORR), 165 Overall survival (OS), 165, 177, 224 P Paclitaxel, 198 Palbociclib, 264, 296 Panobinostat, 248, 252, 264, 270 Parsimonious models, 211 Partial Response (PR), 228 Patient-derived tumor xenografts (PDTX), 156 Patient-reported outcomes (PROs, 17 Pazopanib, 247, 256 PBPK model, 270 Pediatrics, 72–73 Pembrolizumab, 250 Personalized medicine, 18 Pertuzumab, 251 Pharmaceutical Researchers and Manufacturers of America (PhRMA), 118 Pharmacodynamics (PD), 153, 163 assessments, 182 biosimilars, 181–183 in drug development, 182 evaluations, 184 markers, 183–185 multiple dose levels, 183 therapeutic mAbs, 184 Pharmacokinetic/pharmacodynamic (PK/PD) relationships, 157 Pharmacokinetics (PK), 153, 195–199 Index biosimilars, 181–183 drug interactions, 80 Pharmacometrics, 210 Phase Clinical Trials, Phase doses (RP2D), 196 phase I clinical trials, Phase Ib Combination Trial Designs, 12 Phase I-Phase II-Phase III, 238 Phosphatidylinositol 3-kinase (PI3K), 155 Physiologically based pharmacokinetic (PBPK) model, 259, 270 Pictilisib (GDC0941), 129 PKPD models drug exposure-biomarker, 126–127 exposure-antitumor effect relationships, 127–128 PK-tumor kinetic modeling, 218 Polyclonal disease, 212 Pomalidomide, 248, 263, 264 Ponatinib, 91, 248, 255, 272, 291 Population pharmacokinetic (PopPK) assessments, 272 Population pharmacokinetic assessment, 289, 290 Population-specific correction method, 97 Post-marketing requirement (PMR), 119, 306 Practical challenges, oncology clinical pharmacology, 247–272 absorption (see Absorption) approval database, 239–247 benefit–risk ratios, 238 Breakthrough Therapy Designation, 238 chemotherapy, 238 clinical safety, 237 cytotoxic agents, 238, 239 drug treatments, 238 FDA Breakthrough Therapy Designation, 238 identification of dose and regimen (see Dose and regimen identification) immunotherapies, 238 indication selection, 238 industrial, 239 leukemia, 239 MTD, 237 optimizes dose and regimen, 238 organ impaired populations, 272–293 pharmacokinetics, 238 Phase I-Phase II-Phase III, 238 phase-based mentality in drug development, 238 QTc prolongation, 293–297 safety, 237, 238 tolerability, 237 327 Practical considerations, QTc assessment Bluetooth-enabled ECG recording devices, 97 clinical trials, 95 C-QTc, 96 digital ECG data, 96 DLT, 95, 96 ECG, 96, 97 electrolyte imbalance, 95 FDA, 96 Fridericia’s correction (QTcF), 97 heart rate, 97 Holter monitoring, 97 inclusion/exclusion criteria, 95 measurement, QTc interval, 95 NCI CTCAE, 95 population-specific correction method, 97 rigorous control and standardization, 96 thorough DQT study, 96 TQT study, 96 Predictive Marker, 30 Prescription Drug User Fee Act (PDUFA), 107 Primary efficacy variable, 246 Proarrhythmic effects, 80, 84 Proarrhythmic risk, 87 Prognostic biomarkers, 30 Progression-free survival (PFS), 16, 119, 165, 177, 246 Progressive Disease (PD), 228 Proof-of-concept study, 90 Prophylactic G-CSF, 253 Protein tyrosine kinases (PTKs) “druggable” targets, 122 transduction pathways, 122 Proton pump inhibitors (PPIs), 267, 271 Q QTc assessment, 81–94, 98–100, 293–297 anticancer agent for proarrhythmic effect, 80 anticancer agents, 80 cancer types, 80 cardiac arrhythmias, 80 characterization, 78 CiPA, 80 clinical benefit/risk ratio, anticancer agent, 84 C-QTc modeling, 84 development plan, 83 development program, 84 ECG testing, 84 financial considerations, 85 hERG potassium channels, 84 328 QTc assessment (cont.) monoclonal antibodies, 84 pharmaceutical Sponsors of oncology trials, 83 proarrhythmic effects, 84 sample sizes, 84 size and class of molecule, 84 sponsors, 83 clinical assessments, 80 clinical ECG assessment, 79 concentration (see Concentration-QTc (C-QTc)) C-QTc analysis, 79 differences and challenges arsenic trioxide, 82 cancer patients, 82 clinical development practices, 81 clinical trials, 81 ECGs, 81 healthy volunteers, 81 ICH E14 guidance, 81 IRBs, 82 moxifloxacin, 81 MTD, 81 placebo treatments, 81 population characteristics, 81 positive control, 81 REMS, 83 supratherapeutic doses, 81 vandetanib, 82 drug-related proarrhythmia, 79 ECG, 78 ECG testing, 79 exposure-QTc response, 79 ICH E14 guidance, 79 ICH S7A, 80 ICH S7B, 80 outcome, 100–101 practical considerations, 94–97 preclinical testing and culminating, 78 preclinical testing, proarrhythmic effect, 80 proarrhythmic effect, 80 prolongation analysis and collection of data, 293 axitinib, 295, 296 bosutinib, 295 clinical pharmacology objectives, 293 clinical safety data, 293, 295 concentration-dependent, 296 dedicated TQT studies, 295 drug concentrations and ECG parameters, 294 drug interaction/food effect studies, 295 ECG information, 296 Index everolimus, 295 exposure-QTc analysis, 296 exposure–response analysis, 296 FDA reviewer’s independent analysis, 296 FDA reviewer’s independent central tendency analysis, 296 guidelines, 293 ibrutinib, 294 ICH-E14 guideline, 294 idelalisib, 295 lenvatinib, 295 ofatumumab, 294 olaparib, 295 palbociclib, 296 post-marketing commitment, 294 radium-223, 294 REMS, 297 romidepsin, 294 sponsor ponatinib, 294 TQT studies, 293 TQT study, 293 vandetanib, 297 vismodegib, 295 torsade de pointes (TdP), 78 TQT study, 79 tyrosine kinase inhibitors, 80 Quantitative models disease progression, 210 R Radium-223, 247, 294 Ramucirumab, 290, 308 Randomized Phase II Design, 14 Real-time video microscopy, 212 Receptor tyrosine kinase (RTK) inhibitors, 85 Recommended dose (RD), 90 Recommended phase dose (RP2D), 5, 160 Regorafenib, 246, 263, 264 Regulatory and drug development scientists, 238 drug and placebo, 295 expectations, 239, 297 Renal cell carcinoma, 256 Response Evaluation Criteria In Solid Tumors (RECIST) disease progression, 218 Rheumatoid arthritis (RA), 183 Risk Evaluation and Mitigation Strategy (REMS), 83, 297 Rituximab, Rolling Six Design, 10 Romidepsin, 251, 252, 294 ROS oncogene (ROS1), 130 Index S Simcyp®, 270 Simeoni model, 213 Size-based dosing in obesity, 72 in pediatrics, 72–73 Small molecule targeted agents, 254–255 non-cytotoxic MTD, 254–255 Small-molecule tyrosine kinase inhibitors, 84 Somatic cell mutations, 212 Sovaldi®, 118 Stable Disease (SD), 228 STD10, 42, 44–46, 49 Steady-state exposure, 246 Subependymal giant-cell astrocytoma (SEGA), 314 Sum of the longest diameters (SLD), 228 Sunitinib, 135, 136, 138, 139 Supratherapeutic doses, 81, 88 T Target-mediated drug disposition (TMDD), 306 Temozolomide, 199 Therapeutic and supratherapeutic doses, 86–87 Therapeutic index, 250 Thorough DQT study, 87–89, 91, 93–96, 99, 100 Thorough QTc (TQTc) advantage, 100 cardiac arrhythmia, 81–82 classic design, 78 in clinical development, 79 digital ECGs, 96 drug characteristics, 87 ECG schedules, 89 IUT analysis, 98 methodology, 78 outcome, 101 placebo-controlled study, 87 preclinical and clinical development, 78 regulatory authorities, 90, 91 regulatory reviews, 98 serial ECG monitoring, 85 standard, 79, 88 structured ECG monitoring, 78 UIT analysis, 100 Time to growth (TTG), 224, 225 Time to progression (TTP), 165 Time-to-treatment failure (TTF), 18, 228 Topotecan, 197 Torsade de pointes (TdP), 78, 80, 87, 101 Total bilirubin (TB), 289 Total body weight (TBW), 60 Toxic dose low (TDL), 51 Trametinib, 166, 257 329 Translational pharmacology, 122–126 Trastuzumab, 306 Tumor cell killing, 52 Tumor growth modeling, 216–229 ADCs, 211 cancer, 211–216 cancer biology, 210 data-mining approach, 211 drug discovery and development setting, 211 evolutionary dynamics (see Evolutionary dynamics model) Frequent Clone, 230 GIGO variety, 211 HIV infection, 210 Kinetic Model Formulations, 230–231 mathematical modeling, 210 mechanistic insights, 211 OPD, 211 parsimonious model, 211 perception, 210 pharmacometrics, 210 PK replaced with Cave/AUC, 229 quantitative models, disease progression, 210 Resistant Clone, 230 Tumor kinetic modeling, 219, 221–227 burden time course, 218 clinical datasets, 228 clonal compartments, 219 drug concentration, 218 drug-resistant clone, 219, 220 drug-sensitive clone, 219, 220 exponential growth, 218 folding, 218 growth/shrinkage rate, 219 measurements, 218 parameter identifiability AICc score, 222 durable response model, 221–224 exposure-growth function, 221 full two-population model, 222–224 growth rate of sensitive cells falls, 222 NLME framework, 222 primary resistance curve, 219 primary resistance model, 221–224 sensitivity analysis, 222–224 settings and counts, 222 tumor trajectories to parameter space, 221 PK, 218 RECIST disease progression, 218 survival benefit and RECIST response criteria, 226 CTS, 225, 227 evolutionary model parameters, 224 hazard ratio, 226 HR calculation, 226, 227 330 Tumor kinetic modeling (cont.) OS, 224, 226 sensitive clone, 226 sensitivity analysis, HR, 227 true day of death, 225 TTG, 224, 225 tumor size, 224 tumor size-based time of death, 225 tumor size assessments, 219 Tyrosine kinase inhibitors (TKIs), 80 cardiovascular safety, 142, 143 PKPD, 128–130 U “Up-and-Down” designs, US Food and Drug Administration (FDA), 152, 178 V Vandetanib, 82, 93, 99, 254, 270, 292, 297 Vemurafenib, 263, 264 Index Vinorelbine, 197 Vismodegib (Erivedge®), 87, 109, 246, 256, 267, 271, 272, 295 X Xenograft advantages, 217 bootstrap analyses, 217 exponential model, 217 growing and shrinking clones, 217 growth rate-based estimate of T/C, 217 model-fitted estimate of T/C (treated/control growth), 217 nutrient-limited growth, 218 pharmacokinetic (PK) model, 218 population biology, 217 short-term experiments, 217 Z Zarxio®, 184–186 .. .Pharmacokinetics in Drug Development Peter L Bonate • Danny R Howard Editors Pharmacokinetics in Drug Development Problems and Challenges in Oncology, Volume Editors Peter L Bonate Pharmacokinetics/ Modeling/Simulation... Aventis, and Quintiles His career has included responsibilities in both clinical and nonclinical pharmacokinetics and pharmacodynamics, bioanalytics, pharmaceutical business operations, and drug. .. for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA Min Li Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, US Food and Drug Administration,
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Xem thêm: Pharmacokinetics in drug development problems and challenges in oncology, volume 4 , Pharmacokinetics in drug development problems and challenges in oncology, volume 4 , 1 Conventional 3 + 3 “Up & Stop” design, 6 Bayesian Designs (Continual Reassessment Method and Related Designs), Chapter 4: Controversies in Oncology: Size Based vs. Fixed Dosing, 2 Evaluation of Drug Exposure-Antitumor Effect Relationships, 4 Extrapolation of Antitumor Efficacy from Nonclinical Models to the Clinical Setting, 1 Applying a Tumor Growth Model to Xenograft Studies, 3 Summary: Applying Tumor Growth Models to Clinical Development, 1 Use of Patients and/or Healthy Volunteers, 2 Utility of E-R Analysis to Derive the Effect Size for the Noninferiority Trial

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