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Abstract |
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 Top Abstract Materials and MethodsResults and DiscussionReferences |
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of 0.61 and 0.48 for CYP2D6 and CYP3A4, respectively), which
represents an increased rate of identifying the best compounds when compared
with the random rate. This approach represents a valuable computational filter
in early drug discovery to identify compounds that may have P450 inhibition
liabilities prior to molecule synthesis. Such computational filters offer a
new approach in which lead optimization in silico can occur with virtual
molecules simultaneously tested against multiple enzymes implicated in
drug-drug interactions, with a resultant cost savings from a decreased level
of molecule synthesis and in vitro screening.
;
Gao et al., 2002). Although
many of these in vitro tests can be performed in a high-throughput fashion,
they require the molecule to be synthesized or isolated, yet drug discovery
today is becoming reliant on in silico testing of large virtual compound
collections (Leach and Hann,
2000). Predictive computational models that identify liabilities
such as DDIs will have an enormous impact on minimizing the advancement of
undesirable compounds and ultimately will help to shorten drug discovery
timelines when used with other filters for metabolism and toxicology
(Ekins and Rose, 2002).
Therefore, identifying an efficient method of computational filtering for
potential interaction with drug-metabolizing enzymes is of paramount
importance.
One particular area of focus for DDIs is the interactions with the
cytochromes P450 (P450s). The key P450s that have been identified and modeled
extensively include CYP2D6 (Ekins et al.,
1999a) and CYP3A4 (Ekins et
al., 1999b). These enzymes metabolize a vast array of structurally
diverse commercially available drugs and represent major routes of drug
clearance. Therefore, it has become a widely accepted practice that potent
inhibition of these enzymes should be avoided where possible. Many approaches
to predicting inhibition have been taken. A comprehensive analysis of existing
physicochemical data, protein homology, nuclear magnetic resonance,
site-directed mutagenesis, and quantitative structure-activity relationship
(QSAR) approaches for prediction of P450 inhibition have all been modestly
effective (Smith et al.,
1997a,b).
Recently, a neural network model was applied to differentiate between
substrates and inhibitors of CYP3A4 using the data collated in the human P450
metabolism data base (Molnar and Kesuru,
2002). This model was then used to predict previously published
data on structurally diverse CYP3A4 inhibitors
(Ekins et al., 1999b). The
model was not able to correctly rank the inhibition constants and
misclassified a potent inhibitor as a noninhibitor. A major reason for the
failures of these approaches could be due to the generally small sets of
compounds used for model building for which empirical data exist. Even with
this considerable shortcoming, recent reviews have described and compared many
pharmacophores and QSARs for key P450s
(Ekins et al., 2001;
de Groot and Ekins, 2002),
including those for CYP2D6 (Ekins et al.,
1999a) and CYP3A4 (Ekins et
al., 1999b; Riley et al.,
2001). The pharmacophore models have provided structural insight
into key features for inhibition. Despite these relative successes, it is
obvious that there is still a need for a rapid computational approach that
provides a realistic rank ordering of inhibitor potency that is suitable for
early drug discovery. The achievement of such a goal may sacrifice some of the
interpretability of a pharmacophore and homology model but will more than make
up for this in terms of its utility for rapidly scoring large virtual data
bases.
The commercial availability of CYP2D6 and CYP3A4 in recombinant form along
with substrate probes (Crespi et al.,
1997,
1998) has allowed the
development of high-throughput analysis of a large group of diverse molecules.
A set of over 1750 compounds with inhibition data for CYP3A4 and CYP2D6
(Cerep, Redmond, WA) was used to develop computational models using
recursive-partitioning approaches as implemented in commercially available
software. Multiple tree models were generated in Chemtree (Golden Helix Inc.,
Bozeman, MT) using the continuous data set (percentage of inhibition).
Chemtree used over 2500 of the augmented atom descriptors in which the focus
atom and the attached atoms represent a feature, and the through bond count
between these focus atoms is the distance
(Young et al., 2002).
Computational models were created and tested using additional data generated
in our laboratories for a diverse set of 98 molecules purchased from external
vendors. This approach represents, to our knowledge, the largest set of
computational models for P450 inhibition that has been described and validated
to date. The results of this study show that computational models can be
generated to predict percentage of inhibition for both CYP2D6 and CYP3A4, and
ultimately these techniques may be applied to other P450s.
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Materials and Methods |
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TopAbstractMaterials and MethodsResults and DiscussionReferences |
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Enzyme Incubations. Recombinant CYP2D6 (20 nM) was incubated with 5 µM AMMC for 75 min at 37°C in the presence of the test compound (10 µM) in 1.25% final concentration of both acetonitrile and DMSO, with the NADPH-regenerating system (30 µM ß-nicotinamide adenine dinucleotide phosphate, 3.3 mM D-glucose 6-phosphate, and 0.4 U/ml glucose-6-phosphate dehydrogenase). Recombinant CYP3A4 was incubated at 5 nM with 10 µM BFC for 20 min at 37°C in the presence of the test compound (10 µM) in a 1.5% final concentration of both acetonitrile and DMSO with the NADPH-regenerating system (100 µM ß-nicotinamide adenine dinucleotide phosphate, 3.3 mM D-glucose 6-phosphate, and 0.4 U/ml glucose-6-phosphate dehydrogenase). Positive controls contained the complete reaction components and identical incubation conditions with the exception of compound added (1% DMSO added).
Analysis. The detection of the metabolites for AMMC and BFC, namely AHMC and HFC, respectively, were assessed by spectrofluorimetric measurement at an excitation wavelength of 405 nm for both AHMC and HFC and emission wavelengths of 460 nm for AHMC and 490 nm for HFC. All measurements were performed with a PerkinElmer Wallac Victor 5 multiplate reader (PerkinElmer Wallac, Gaithersburg, MD). The fluorescent intensity (fu) measured at (t = 0) was subtracted from that measured after the appropriate incubation time (t = 75 min for CYP2D6 and t = 20 min for CYP3A4). The ratio of signal-to-noise was calculated by comparing the fluorescence in incubations containing the test compound with the control samples containing the same solvent vehicle. The percentage of control activity was then calculated. Subsequently, the percentage of inhibition is calculated by subtracting the percentage of control activity from 100.
Computational Methods. The Chemtree recursive-partitioning software
used was run on a Pentium 3 processor. Two data sets of percent-inhibition
data were generated under the same conditions as described above and purchased
from Cerep for CYP2D6 (1759 molecules) and CYP3A4 (1756 molecules). The
molecules and experimental data for each enzyme were separately imported as an
.sdf file in Chemtree to generate over 2500 augmented atom descriptors
(Young et al., 2002), which
were used along with the actual percent-inhibition value to generate 20 random
tree models (with the following options: p value threshold for
splits, 0.99; maximal segments, 3; parallel threads, 1; and resampling
iterations, 10,000).
External Validation of Computational Models and Statistical
Analysis. The 98 molecules used to generate percent-inhibition data for
both CYP2D6 and CYP3A4 in our laboratories were input as an .sdf file into the
Chemtree software, and predictions were made with the appropriate set of 20
models coinciding with the same enzyme to generate computational predictions.
The in vitro observed data and computationally predicted data for the average
of the 20 tree models were compared and assessed using a number of statistical
approaches, including the correlation of Spearman's
(Ekins et al., 2002), available
in JMP 4.5 (SAS Institute Inc., Cary, NC). This test represents a correlation
coefficient on the relative rank order of the data and not on the values
themselves. This test also provides a statistical significance result, which
can be expressed as the p value, where an observed value of <0.05
is meaningful. In addition, the observed and predicted data can be considered
as binary values using a cutoff of less than 40% inhibition to represent
desirable molecules and greater than 40% inhibition to indicate undesirable
molecules. The molecules could be ranked from lowest- to highest-predicted
percentage of inhibition, and then the rate of finding these poor inhibitors
was assessed. This rate was compared with the random and the ideal rate of
finding all the poor inhibitors at five data-point intervals and plotted
accordingly. Such an approach represents selection of the molecules with the
lowest percentage of inhibition first. The standard deviations for replicate
empirical determinations of percent-inhibition data were also determined.
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Results and Discussion |
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TopAbstractMaterials and MethodsResults and DiscussionReferences |
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,
2001). In the present study, we
have described models using a commercially available software package for
molecular descriptor generation and computational QSAR model building. We have
coupled this software with a large set of experimentally determined CYP2D6 and
CYP3A4 inhibition data from a commercial source to generate models. In
addition, we have tested these computational models with data generated by our
own laboratory but not included in model generation or resembling those
molecules in the model training sets.
The average of the 20 CYP2D6 Chemtree models resulted in an observed versus
predicted correlation of r2 = 0.88 for the training set
(Fig. 1a). The average of the
20 CYP3A4 Chemtree models resulted in an observed versus predicted correlation
of r2 = 0.82 for the training set
(Fig. 1b). Percent-inhibition
data were generated for 98 diverse commercially available molecules with both
CYP2D6 and CYP3A4 and were compared with the predictions generated with the
respective Chemtree models. The average of the 20 CYP2D6 Chemtree models was
able to generate a statistically significant rank ordering of the
percent-inhibition data based on the Spearman's rank of 0.61, p
= 0.0001. This represents a significant improvement over the random
identification of the molecules with less than 40% inhibition. When ranked in
order of increasing percentage of inhibition of CYP2D6, virtually all of the
first 50 compounds were predicted correctly
(Fig. 2a). The average of the
20 CYP3A4 Chemtree models was to a lesser extent also able to generate a
statistically significant rank ordering of the percent-inhibition data based
on the Spearman's rank of 0.49, p = 0.0001. This was also an
improvement over random identification of the molecules with less than 40%
inhibition (Fig. 2b) but was
not of the same magnitude as the predictions for CYP2D6
(Fig. 2a). A ranking of such
inhibition data may be all that is required for early drug discovery in order
to select the best molecules with the greatest odds of progressing without
failure due to interactions with these enzymes.
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The current models generated are only as good as the data used in terms of both training and testing. The experimental error of 10% for the empirical data must be considered when the predictions are assessed and compared with experimental values, as this suggests that a cutoff for inhibition used as a go/no go decision (for example, less than 40% in this example) should have some degree of flexibility. Typically, results with greater than 20% standard deviation were observed with compounds that were noted as having solubility issues. Further limitations with the predictive powers of our generated models appear to be related to the training data set. These sets were inadvertently heavily skewed toward the low-percent inhibition molecules (Fig. 1, a and b), and it is likely that this is representative of the actual case for available drug-like molecules. This might be different in laboratories where some project areas may be inadvertently synthesizing potent inhibitors for P450s as a consequence of combinatorial chemistry or parallel synthesis. As more empirical data become available, its incorporation into the training set should enhance the future predictive power of our models. Continuous improvements in throughput for in vitro drug-drug interaction studies could be viewed as a means to obviate the need for computational predictions. However, in a number of companies, large virtual libraries of molecules must be evaluated and refined prior to synthesis or purchase, which argues in favor of rapid computational filters for drug-drug interactions and other important properties. Chemtree and algorithm technologies developed to handle nonlinear data clearly will play a valuable role in this regard to aid in filtering and compound selection.
Sean Ekins
Jennifer Berbaum
Richard K. Harrison
Concurrent Pharmaceuticals Inc., Fort Washington, Pennsylvania
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Footnotes |
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Address correspondence to: Dr. Sean Ekins, Concurrent Pharmaceuticals Inc., 502 West Office Center Drive, Fort Washington, PA 19034. E-mail: sekins{at}concurrentpharma.com
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References |
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TopAbstractMaterials and MethodsResults and DiscussionReferences |
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