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10
COMPUTER-ASSISTED HPLC
AND
KNOWLEDGE MANAGEMENT
Yuri Kazakevich, Michael McBrien, and Rosario LoBrutto
10.1 INTRODUCTION
In modern high-performance liquid chromatography (HPLC), computers in a
broad sense are used in every instrumental module and at every stage of analy-
sis. Computers control the flow rate, eluent composition, temperature, injec-
tion volume, and injection process. Detector output signal is converted from
analog form into the digital representation to recognize the presence of peaks,
and then at higher level of computer analysis a chromatogram is obtained.All
these computer-based functions are performed in the background, and the
chromatographer usually does not think about them.
The second level of computer utilization in HPLC is extraction of valuable
analytical and physicochemical information from the chromatogram. This
includes standard analytical procedures of peak integration, calibration and
quantitation, and more complex correlation of the retention dependencies
with variation of selected parameters.
At the third (and probably highest) level, a computer is used for the sophis-
ticated analysis of many different experimental results stored in databases.
This level is usually regarded as a knowledge management level and can have
quite a variety of different goals:
•
Selection of the starting conditions for method development by using
information of similar separations
503
HPLC for Pharmaceutical Scientists, Edited by Yuri Kazakevich and Rosario LoBrutto
Copyright © 2007 by John Wiley & Sons, Inc.
•
Optimization of the existing method,
to speed up the analysis, increase
ruggedness of the chromatographic method, and so on
•
Review of a multitude of data from different experiments and their cor-
relation with information from other physicochemical methods
•
Cross-laboratory information exchange (early drug discovery, preformu-
lation groups,drug metabolism and pharmokinetic groups,drug substance
and drug product groups)
In this chapter the third level of computer-assisted HPLC—the use of expert
systems (like Drylab [1], AutoChrom
TM
[2], and ChromSword
®
[3]) for effec-
tive method development—is discussed.
Computer-assisted method development has received a great deal of atten-
tion from management within the pharmaceutical industry, mainly from the
perspective of cost savings associated with faster and more efficient develop-
ment. Adoption and incorporation of the tools in day-to-day workflows has
been relatively limited due in part to a reluctance of chromatographers to
believe that computers can replace the intuition of the expert chromatogra-
pher. With the present state-of-the-art, there is little question that computers
can play a role in efficient method development. However, it must be accepted
that computers are a supplement to, rather than a replacement for, the knowl-
edge of the method development chromatographer.
Two main types of software tools exist that are directly applicable to the
problem of chromatographic method development.
1. Optimization or experimental design software packages for modeling
the chromatographic response as a function of one or more method vari-
ables. These can also play a key role in data management of the consid-
erable information that results from rigorous method development
exercises.
2. Structure-based prediction software predicts retention times or impor-
tant physicochemical processes based on chemical structures. Applica-
tion databases store chromatographic methods for later retrieval and
adaptation to new samples with similar structures and physicochemical
parameters.
10.2 PREDICTION OF RETENTION AND
SIMULATION OF PROFILES
In Chapters 2, 3, and 4, all aspects of the analyte retention on the HPLC
column are discussed. There are many mathematical functions describing
retention dependencies versus various parameters (organic composition, tem-
perature, pH, etc.). Most of these dependencies rely on empirical coefficients.
Analyte retention is a function of many factors: analyte interactions with the
stationary and mobile phases; analyte structure and chemical properties; struc-
504 COMPUTER-ASSISTED HPLC AND KNOWLEDGE MANAGEMENT
ture and geometry of the column packing material; and many other parame-
ters
. The theoretical functional description of the influence of the eluent com-
position, mobile-phase pH, salt concentration, and temperature, as well as the
influence of the type of organic modifier and type of salt added to the mobile
phase, are discussed in detail in Chapter 2 and 4.
Currently, eluent composition, column temperature, and eluent pH are the
only continuous parameters used as the arguments in functional optimization
of HPLC retention. However, other parameters such as ionic strength, buffer
concentration and concentration of salts and/or ion-pairing reagents can be
taken into account, and mathematical functions for these can be constructed
and employed.
The simplest and the most widely used forms of retention time prediction
for analytical scale HPLC are based on the empirical linear dependence of the
logarithm of the retention factor on the eluent composition.
10.2.1 General Thermodynamic Basis
Association of the chromatographic retention factor with the equilibrium con-
stant is the basis for all optimization or prediction algorithms. As was shown
in Chapter 2, this association is only very approximate and should be used with
caution.
In short, an approximate mathematical description of the retention factor
dependences on the eluent composition and temperature is written in the
form
(10-1)
where f is the molar fraction of the organic eluent modifier, DG
el.
is the Gibbs
free energy of the organic eluent modifier interaction with the stationary
phase; R is the gas constant; T is the absolute temperature, and DG
an.frag.
is the
Gibbs free energy of the interactions of structural analyte fragments with the
stationary phase.
Equation (10-1) is based on the assumption of simple additivity of all inter-
actions and a competitive nature of analyte/eluent interactions with the sta-
tionary phase. The paradox is that these assumptions are usually acceptable
only as a first approximation, and their application in HPLC sometimes allows
the description and prediction of the analyte retention versus the variation in
elution composition or temperature. For most demanding separations where
discrimination of related components is necessary, the accuracy of such pre-
diction is not acceptable. It is obvious from the exponential nature of equa-
tion (10-1) that any minor errors in the estimation of interaction energy, or
simple underestimation of mutual influence of molecular fragments (neglected
in this model), will generate significant deviation from predicted retention
factors.
k
G
RT
G
RT
=−
∑
exp
∆
∆
an.frag.
el.
f
PREDICTION OF RETENTION AND SIMULATION OF PROFILES 505
10.2.2 Structure–Retention Relationships
Many attempts to correlate the analyte structure with its HPLC behavior have
been made in the past [4–6].
The Quantitative structure–retention relation-
ships (QSRR) theory was introduced as a theoretical approach for the pre-
diction of HPLC retention in combination with the Abraham and co-workers
adaptation of the linear solvation energy relationship (LSER) theory to chro-
matographic retention [7, 8].
The basis of all these theories is the assumption of the energetic additivity
of interactions of analyte structural fragments with the mobile phase and the
stationary phase, and the assumption of a single-process partitioning-type
HPLC retention mechanism. These assumptions allow mathematical repre-
sentation of the logarithm of retention factor as a linear function of most con-
tinuous parameters (see Chapter 2). Unfortunately, these coefficients are
mainly empirical, and usually proper description of the analyte retention
behavior is acceptable only if the coefficients are obtained for structurally
similar components on the same column and employing the same mobile
phase.
To date, the shortcomings in the theoretical [22] and functional description
of HPLC column properties make all these theories insufficient for practical
application to HPLC method design and selection.
In the past, several theoretical models were proposed for the description of
the reversed-phase retention process. Some theories based on the detailed
consideration of the analyte retention mechanism give a realistic physico-
chemical description of the chromatographic system, but are practically inap-
plicable for routine computer-assisted optimization or prediction due to their
complexity [9, 10]. Others allow retention optimization and prediction within
a narrow range of conditions and require extensive experimental data for the
retention of model compounds at specified conditions [11].
Probably the most widely studied is the solvophobic theory [12] based on
the assumption of the existence of a single partitioning retention mechanism
and using essentially equation (10-1) for the calculation of the analyte reten-
tion. Carr and co-workers adapted the solvophobic theory [12, 13] and LSER
theory [11, 14–17] to elucidate the retention of solutes in a reversed-phase
HPLC system on nonpolar stationary phases.
The free energy of transfer of a molecule from the mobile phase to the sta-
tionary phase, DG, can be regarded as a linear combination of the free reten-
tion energies, DG
i
, arising from various molecular subunits (solvatochromic
parameters). Many solvatochromic parameters for some analytes could be
found in the literature [18–21]. The signs and magnitudes of the coefficients
depict the direction and relative strength of different kinds of solute/station-
ary and solute/mobile phase interactions contributing to the retention in the
investigated matrix [11–15]. The most influential factors governing RP-HPLC
retention on alkyl and phenyl-type bonded phases were determined to be
hydrogen bonding and the solute molecular volume [12, 13, 20, 23].The hydro-
506 COMPUTER-ASSISTED HPLC AND KNOWLEDGE MANAGEMENT
gen bonding is measured as the effect of complexation between hydrogen-
bond acceptor (HB
A) solutes and hydrogen-bond donor (HBD) bulk phases
[24]. The solute molecular volume is comprised of two terms: One measures
the cohesiveness of the chromatographic phases (both the mobile and sta-
tionary phases) and the other is the dispersive term that measures the ability
of the chromatographic phases to interact with solutes via dispersive forces.
10.3 OPTIMIZATION OF HPLC METHODS
10.3.1 Off-Line Optimization
The most common software tools used for chromatographic method develop-
ment are optimization packages. All of these tools take advantage of the fact
that the retention of a given compound will change in a predictable manner
as a function of virtually any continuous chromatographic variable.
The classic example (and certainly most common application) of computer-
assisted chromatographic optimization is eluent composition, commonly
called solvent strength optimization. The chromatographer performs at least
two experiments varying the gradient slope for gradient separations or con-
centration of organic modifier for isocratic separations at a certain tempera-
ture. The system is then modeled for any gradient or concentration of organic
modifier. A simplistic description of the chromatographic zone migration
through the column under gradient conditions is given in Chapter 2. At iso-
cratic conditions the linear dependence of the logarithm of retention factor
on the eluent composition is used for optimization:
(10-2)
where k is the retention factor of the compound, φ is the fraction of organic
solvent in the mobile phase, and A and B are constants for a given compound,
chromatographic column, and solvent system. Based on a few experiments, the
constants in the expression can be extracted, and retention of each compound
can be predicted.
This optimization approach can be used to model both retention times and
selectivities due to the fact that both the A and B terms are unique for a given
analyte.
The typical output from method optimization software is a resolution map,
as shown in Figure 10-1. The map shows resolution of the critical pair (two
closest eluting peaks) as a function of the parameter(s). The example shows
resolution as a function of gradient time (slope of the gradient). The resolu-
tion map has several advantages as an experimental display tool: It forms a
concise summary of experiments performed, it allows the chromatographer to
select areas of interest and communicate the expected result, and it facilitates
the viewing of data that would allow for a more robust separation.
ln kA B
()
=+j
OPTIMIZATION OF HPLC METHODS 507
Optimization of the eluent composition is commonly based on the linear
relationship of ln k to f (10-4) and generally applicable for ideal chromato-
graphic systems with unionizible analytes in methanol/water mixtures
. It is
commonly assumed that:
•
A single partitioning-like equilibrium process dominates in the retention
mechanism.
•
Analyte ionization changes do not occur in the pertinent solvent range.
•
Column property changes do not occur over the course of the experiment.
Like in any optimization tool, the chromatographer should be wary of
extrapolation beyond the scope of the training experiments. Behavior of
certain parameters, like temperature and solvent strength, is fairly easily
modeled. Other parameters, such as buffer concentration and pH, can be much
more difficult to model. In these cases, interpolation between fairly closely
spaced points (actual experiments that were performed) is most appropriate.
Figure 10.2 shows a resolution map for a two-dimensional system in which
solvent composition and trifluoroacetic acid concentration are simultaneously
optimized.The chromatographer has collected systematic experiments at TFA
concentrations of 5, 9, 13,and 17mM and acetonitrile concentrations of 30, 50,
and 70v/v% for a series of small molecules on a Primesep 100 column.
508 COMPUTER-ASSISTED HPLC AND KNOWLEDGE MANAGEMENT
Figure 10-1. DryLab
®
software version 3.0 modeling the separation of a mixture of
naphthalenes. Resolution of the critical pair (the two peaks that elute closest together)
is denoted as a function of time of gradient. Experimental runs are shown as solid lines
on the resolution map; selected prediction is a dashed line.
Note. T
he type and concentration of the organic eluent can cause a pH shift
of the aqueous portion of the mobile phase as well as change the ionization
state of the analyte in a particular hydro-organic mixture. Temperature can
also lead to change in the ionization constants of analytes.
Even when chromatographers are careful to keep buffer strengths constant
during modification of organic solvent strengths, effective analyte pK
a
changes
and mobile-phase pH changes as a result of solvent strength, which can cause
changes in ionization state of compounds, changes in the resultant mobile-
phase pH, and/or changes in the behavior of chromatographic columns [25].
Departures from linearity can be particularly striking in acetonitrile as
opposed to methanol. For systems in which the greatest possible quality of
method is required in terms of resolution, run time, and robustness, the results
from predictions should be verified against experimental data and, where nec-
essary, nonlinear predictions should be used to refine the model and to locate
the optimal conditions.
Computer-assisted optimization of parameters has not been universally
accepted, primarily due to a lack of ease of use. All compounds must be
tracked across all experiments, and all retention times must be introduced to
the system for each component. This is sometimes difficult because significant
variations in the retention and elution order could be observed for certain ana-
lytes. With diode array detection, even if the different analytes have distinct
OPTIMIZATION OF HPLC METHODS 509
Figure 10-2. ACD/LC Simulator
TM
9.0 modeling the separation of a series of com-
pounds as a function of solvent composition and TFA concentration (mM). Experi-
ments are shown as white dots on the resolution map with the predicted optimal
method shown in yellow. See color plate.
diode array profiles, the analytes with low concentration in the mixture may
still be difficult to track.
The use of MS detection can assist in the detection of
the peaks in the different experiments, with the assumption that they are not
isomers of each other. Software vendors have begun to address much of this
with the implementation of automated peak-tracking systems (see Section
10.3.4.2) and direct transfer of experimental information from chromatogra-
phy data systems.
Advantages of this technique are the efficiency of development of methods,
structured development profiles, and effective reporting of what was per-
formed during the different method development iterations. In addition, it is
possible to model the effect of parameter variation on the robustness of
methods in addition to general chromatographic figures of merit: apparent
efficiency, tailing, resolution of critical pairs, backpressure of system, total
run time.
10.3.2 On-Line Optimization
Recently there has been renewed interest in automated method development
in which the optimization software directly interfaces with the instrument in
order to run or suggest new experiments based on the prior results that gen-
erated the initial resolution maps. In the late 1980s, a number of approaches
to this problem were attempted, but none of these tools prevailed, due in part
to the challenges of tracking peaks between experiments.
The current second-generation tools offer more promise due to (a) a focus
on secondary detection techniques for peak tracking and (b) better automa-
tion tools offered by instrument vendors.
The advantages of on-line automation are the achievement of time savings
in relation to the chromatographic method development time. The software
can make decisions at any time of the day or night and can immediately
communicate this information to the instrument after the completion of the
experiment. There is also a more subtle benefit to the link of optimization
software to the chromatography data system. Method development “wizards”
with drop-down menus/user-defined fields can simplify the process of config-
uring the instrument sequence/method prior to a method development
session.
Disadvantages of on-line optimization lie primarily in the maturity of this
technology. If manual method development is based on the experience and
intuition, the automated method development in principle should follow the
logic of chromatographic theory, which unfortunately is not yet developed
enough to provide a logical guide for automated optimization. Software and
instrument vendors are relying on the statistical optimization with minimal use
of available theoretical developments and only on the level of simple parti-
tioning mechanism and energetical additivity. The capacity of software inno-
vators to address detection limit, peak-tracking, and artificial intelligence
issues remains in question at present, but the considerable commitment by
510 COMPUTER-ASSISTED HPLC AND KNOWLEDGE MANAGEMENT
instrument and software vendors points to the future value of these tools. As
spectroscopic peak-tracking algorithms mature
, the effectiveness of the tools
will grow considerably.
10.3.3 Method Screening
There are some chromatographic parameters that do not readily lend them-
selves to optimization. There have been some efforts to quantify the selectiv-
ity in chromatographic columns [26, 27], but it is often difficult to achieve
targeted values for each of the parameters involved without custom prepara-
tion of materials.Experimental mobile-phase pH values must typically be very
close together in order to enable subsequent pH optimization. Column and
pH choice are critical to the selectivity of a given system, so it is clear that
their effects should not be ignored. One solution to this problem is to screen
different columns and pH values prior to commencing any kind of optimiza-
tion.The screening results are reviewed, and optimization systems at a particu-
lar pH are designed accordingly.
With the advent of column switchers and more reproducible alternative
column materials, it is now quite feasible to screen multiple pH values—for
example, at high, medium, and low pH—using scouting gradients in order to
choose the column and pH at which to perform further optimization experi-
ments.This is a particularly tempting scenario when few or no chemical struc-
tures are available for the synthetic by-products or degradation products in
the sample, or when samples are particularly complex. Recently there has been
considerable development on systems for selection of optimal pH and type of
column concomitantly [28].
For complex samples, it can be time-consuming and challenging to review
all the results of system screens objectively. In addition, online optimization
precludes the direct involvement of the chromatographer. For this reason, it
is desirable to use some numerical description of the potential effectiveness
of a given set of conditions so the on-line optimization software can trigger
further separations on the chromatographic system.
Screening review tools cannot work solely based on the venerable “resolu-
tion of the critical pair” approach; the results of an initial screen must be able
to give nonzero results even with co-elution of two components,when the reso-
lution of the critical pair will, of course, be zero.
Additionally, a suitability approach involving criteria related to run time is
unwise, since run time can be fine-tuned based on solvent strength or flow rate
in final optimization. Rather, at the screening stage, the chromatographer
should be focused on sufficient selectivity to form the basis of an eventual suc-
cessful separation, and then fine-tuning can be performed.There are a number
of different measures of the desirability of an initial screen, including average
resolution, resolution of critical pair, selectivity of critical pairs, and so on.
The chromatographer need not be intimately familiar with the nuances of
every rating system available. The only key is to be certain that appropriate
OPTIMIZATION OF HPLC METHODS 511
rating systems are used at appropriate times.Table 10-1 shows some common
approaches to the rating of chromatographic column screens [29].
10.3.4
Method Optimization
All approaches to method optimization based on multiple experiments have
the requirement that all components be detected and that they be tracked
between runs. For complex samples, this is typically the most labor-intensive
aspect of method development. For unattended method development, the
instrument is required to monitor the change in retention of each component
automatically. The historical limitations to this technology have been a
key stumbling block in the widespread adoption of automated method
development.
10.3.4.1 Peak Matching in Method Optimization. An initial solution to the
problem of peak tracking across multiple experiments was the isolation of
each impurity on a preparative or semiprep scale, followed by injection of each
component individually. The chromatographic world has essentially rejected
this concept outright.Very few chromatographers have the time or willingness
to isolate standards for each component.The use of crude samples and mother
512 COMPUTER-ASSISTED HPLC AND KNOWLEDGE MANAGEMENT
TABLE 10-1. Numerical Approaches to Ranking Separations
Approach Basis Application
Minimum resolution Resolution of closest-eluting peaks Final model of
(resolution of the (R
CP
) separation
critical pair)
Method suitability Product or minimum of various Final model of
criteria: run time, resolution of separation
critical pair, and resistance of (customizable)
viability to small changes in
conditions
Mean resolution Average resolution Assessment of
selectivity
Run time (RT) versus N = 1 if RT < Target; Evaluating suitability
Target (t) and N = 0 if RT > maximum; of solvent strength
Maximum (M) N = 1 − (RT − t)/(M − t) and column choice
Equidistance deviation from equal peaks resolution Comparison of
starting systems
Resolution score average value of normalized Comparison of
resolutions between all the peaks starting systems
detected on a chromatogram
RsScore =
−
∑
Rs
N
n
1
N
t
n
=
−
−
RunTime
0
1
[...]... daunting Before embarking on choosing the optimal conditions for optimization, generally a pH screen (at least five pH values) in either gradient or isocratic mode is performed to determine the most suitable pH ranges for the active pharmaceutical ingredient (at least one unit below or above the target analyte pKa in a particular hydro-organic system) This results in at least five experi- OPTIMIZATION OF HPLC. .. the neutral form of the basic compound and the neutral form of the acidic compound For basic compounds (or basic functionalities) the lower the pH, the more the ionic equilibrium is shifted toward the protonated form of the analyte, which continually increases its concentration in the aqueous phase and decreases its content in oil phase Therefore there is no plateau region at low pH However, for an acidic... data for original traces is sorted by the chromatographic method, tracing for which sample/condition set the data were collected In the project architecture, information is grouped according to experimental conditions, or “experiments.” Multiple detector traces are arranged for each subsample, with subsamples organized by experiment Experiments are grouped according to waves that are designed for optimization... could be defined, then scientists can save time in their method development journey Programs that allow for structures or partial structures searching can be used to assist with the selection of starting points These data could be easily searched for The method development work that a chromatographer plans to 518 COMPUTER-ASSISTED HPLC AND KNOWLEDGE MANAGEMENT employ may have been performed prior in early... pointer to new opportunities Structure-based separation databases integrated with other analytical and pharmaceutical information provides a basis for a significant increase of development efficiency If analytical chemists from the various areas of drug development (drug metabolism, preformulation, formulation, drug substance) enter their separations of the target compounds into the database and link... be made between chemical formulae and chemical structures For databases with any type of diversity to be realized, the chemical formula cannot provide effective retrieval of compounds Structure-based searches can take three different approaches: 520 • • • COMPUTER-ASSISTED HPLC AND KNOWLEDGE MANAGEMENT Structure Substructure Structure similarity [35] Structure searches look for molecules that are identical...OPTIMIZATION OF HPLC METHODS 513 liquors enriched with synthetic byproducts for initial method development of drug substance is recommended Another approach is to look at the molecule of interest and predict most probable degradation product(s) and use forced degraded samples for initial method development For example, if a compound contains ester functionality,... to use methods developed in the past as a knowledge base for the determination of a starting point Stored methods are retrieved, and method development sessions can be designed based on the past work performed in different line units of the organization (early drug discovery, preformulation group, DS and DP groups) A key point here is the need for chemical structures to assist with locating similar... separate components in the forced degradation samples as if they were all present in the same sample The development of methods for these “composite samples” is typically required to be exceedingly rigorous Columns, solvent systems, and pH values will be screened, and multidimensional optimization performed The software tools that have been discussed in this chapter are invaluable for this kind of project... setup of the system If the approach is not combined with instrument control, then a process must be devised for efficient transfer of information to the data system STRUCTURE-BASED TOOLS 517 10.4 STRUCTURE-BASED TOOLS It is uncommon for the method development chromatographer to have absolutely zero information with regard to the chemical structures present in a given sample Typically, at least one or more . goals:
•
Selection of the starting conditions for method development by using
information of similar separations
503
HPLC for Pharmaceutical Scientists, Edited by Yuri Kazakevich. functions for these can be constructed
and employed.
The simplest and the most widely used forms of retention time prediction
for analytical scale HPLC are
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