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Integrated in Silico-in Vitro Strategy for Addressing Cytochrome P450 3A4 Time-Dependent Inhibition Michael Zientek,† Chad Stoner,†,[ Robyn Ayscue,‡ Jacquelyn Klug-McLeod,‡ Ying Jiang,† Michael West,§ Claire Collins,| and Sean Ekins*,⊥,#,∇ Dynamics & Drug Metabolism, Pharmacokinetics, Pfizer Global Research & DeVelopment, San Diego California, Groton, Connecticut, and Sandwich, United Kingdom, Computational Center of Emphasis, Pfizer, Groton, Connecticut, Arnold Consultancy and Technology LLC, 5 Penn Plaza, 19th Floor, New York, New York 10119, Department of Pharmaceutical Sciences, UniVersity of Maryland, 20 Penn Street, Baltimore, Maryland 21201, and Robert Wood Johnson Medical School, UniVersity of Medicine and Dentistry of New Jersey, 675 Hoes Lane, Piscataway, New Jersey 08854 ReceiVed NoVember 20, 2009

Throughout the past decade, the expectations from the regulatory agencies for safety, drug-drug interactions (DDIs), pharmacokinetic, and disposition characterization of new chemical entities (NCEs) by pharmaceutical companies seeking registration have increased. DDIs are frequently assessed using in silico, in vitro, and in vivo methodologies. However, a key gap in this screening paradigm is a full structural understanding of time-dependent inhibition (TDI) on the cytochrome P450 systems, particularly P450 3A4. To address this, a number of high-throughput in vitro assays have been developed. This work describes an automated assay for TDI using two concentrations at two time points (2 + 2 assay). Data generated with this assay for over 2000 compounds from multiple therapeutic programs were used to generate in silico Bayesian classification models of P450 3A4-mediated TDI. These in silico models were validated using several external test sets and multiple random group testing (receiver operator curve value >0.847). We identified a number of substructures that were likely to elicit TDI, the majority containing indazole rings. These in vitro and in silico approaches have been implemented as a part of the Pfizer screening paradigm. The Bayesian models are available on the intranet to guide synthetic strategy, predict whether a NCE is likely to cause a TDI via P450 3A4, filter for in vitro testing, and identify substructures important for TDI as well as those that do not cause TDI. This represents an integrated in silico-in vitro strategy for addressing P450 3A4 TDI and improving the efficiency of screening. Introduction Developing safe and effective small molecule pharmaceuticals is a complex and costly task. The enormous cost of late stage failures and withdrawals has highlighted the need to more thoroughly characterize new chemical entities (NCEs)1 in drug discovery (1). While the number of preclinical assays to study safety and drug-drug interactions (DDIs) has dramatically increased over this time, so has the number of compounds progressing through drug discovery pipelines. However, there are still areas in drug discovery that are less well understood, which require mechanistic insight and models to remove potential compound liabilities that could result in late stage clinical failure. * To whom correspondence should be addressed. E-mail: ekinssean@ yahoo.com. † Pfizer Global Research & Development, San Diego, CA. ‡ Computational Center of Emphasis, Pfizer, Groton, CT. § Pfizer Global Research & Development, Groton, CT. | Pfizer Global Research & Development, Sandwich, United Kingdom. ⊥ Arnold Consultancy and Technology LLC. # University of Maryland. ∇ University of Medicine and Dentistry of New Jersey. [ Deceased. 1 Abbreviations: ADME, absorption, distribution, metabolism, and distribution; AUC, area under the curve; XV, cross-validated; DDI, drug-drug interactions; FCFP_6, functional class fingerprint 6; MBI, mechanism-based enzyme inhibition; MIC, metabolic intermediate complex; NCE, new chemical entities; PCA, principal component analysis; ROC, receiver operator curve; SAR, structure-activity relationship; QSAR, quantitative structure-activity relationship.

The cytochrome P450 (P450) enzymes (EC 1.14.14.1) are currently among the most widely studied proteins in drug discovery and design. These membrane-bound monoxygenases are responsible for many catalytic reactions that generally involve substrates with more hydrophobic character. These enzymes are known to display regio- and stereoselectivity, and while many compounds are selectively metabolized by one enzyme or another, overlapping substrate specificity among different P450 enzymes is fairly common. Molecules may interact with the binding site of these enzymes and result in reversible inhibition (2, 3). Perhaps a more important source of DDI is mechanism-based inhibition, which may result in either metabolic products that form heme or protein adducts or a metabolic inhibitory complex (MIC), thus rendering the enzyme catalytically inactive (4). Mechanism-based enzyme inhibition (MBI) can be considered a subset of time-dependent inhibitors (TDI) and should not be considered synonymous, due to the additional confounding incorporation of a tight-binding parent compound or its metabolites, causing a reversible binding event (5). Since mechanism-based types of inhibition have been the focus of several recent reviews, we point to these examples to help illustrate some structure-activity relationships (SARs) causing TDI. For example, Fontana et al. reviewed all available data tabulating structures, 59 of which were specifically metabolized by P450 3A4 alone to form an intermediate that was able to irreversibly bind to the enzyme (6). Several classes of compounds were identified including (1) acetylenes, which produce an oxirene that rearranges to a reactive ketene; (2)

10.1021/tx900417f  2010 American Chemical Society Published on Web 02/12/2010

Modeling CYP3A4 Time-Dependent Inhibition

furans, thiophenes, conjugated structures, and terminal alkenes, which form an epoxide intermediate, and other examples, which did not include the “detoxication step” (furans may be hydroxylated or form a bicarbonylic structure); (3) dichloro- and trichloro-ethylenes, which produce epoxide intermediates; (4) methylenedioxyphenol compounds, which are suggested to form a carbine; (5) secondary and tertiary amines, which are frequently N-dealkylated and can be followed by hydroxylamine formation and then subsequently formation of nitroso-derivatives; and (6) thiono compounds, which are less well understood (6). It was noted in this review that the efficiency and specificity of many of these MBI depended on the structure of the whole molecule. Ghanbari et al. also tabulated the literature, incorporating experimental conditions for many MBI studies, and calculated some physicochemical properties such as pKa, LogP, and LogD, but they did not correlate these with the KI (7). Hollenberg et al. reviewed MBI-forming compounds that include acetylenes, thiol-containing compounds, arylamines, quinones, furanocoumarins, and tertiary amines, while discussing the clinical implications (8). A recent study has also used in vivo-in vitro extrapolation methods including SimCYP to predict DDI for MBI as well as to suggest a TDI-reactive metabolitescreening paradigm (5). As the P450 3A family of enzymes is widely recognized as the most important for human metabolism of a large proportion of commercially available drugs (9), it is frequently assessed in early pharmaceutical drug discovery screens to identify TDI. A recent study used 54 molecules to assess their propensity to form a MIC, which irreversibly binds to the heme (10). The study used recombinant P450 3A4 (+b5), and inhibition data were analyzed using several computational approaches that included the generation of simple molecular descriptors to assist in understanding the relationship between MIC and molecular structure. A preliminary comparison between MIC- and nonMIC-forming compounds based on the mean molecular weight showed a significant difference between the two groups of compounds (p < 0.05), indicating that larger molecules (likely to be P450 3A4 substrates) are more likely to form MIC in vitro. Various computational methods including recursive partitioning, tree-based methods, and logistic regression were used and tested with an external set of molecules (10). This study reiterated that requirements for P450 3A4 (+b5)-mediated MIC formation requires other molecular properties besides the well-known primary, secondary, and tertiary amines or methylenedioxyphenyl features and appeared to be dependent on the number of hydrophobic features and hydrogen bond acceptors. These results also indicated that the descriptors representing the parent molecule could capture the important features of the metabolites and predict MIC formation (10). We are now seeing the increasing use of computational in silico approaches alongside in vitro methods (11-14), even though there is a rich history for using computational and mathematical approaches to predict metabolism dating back to the 1960s (15, 16). Quantitative structure-activity relationships (QSARs) and machine-learning models only started to be generated for P450 inhibition in the last decades based on large in vitro data sets. For example, the molecular modeling method Comparative Molecular Fields Analysis (CoMFA) was used to describe key molecular features of ligands for human P450 1A2 (17) and P450 2C9 (18). However, few of the early QSAR methods used large data sets relevant to the pharmaceutical industry and, in addition, rarely used test sets of molecules to evaluate predictive capability. Computational pharmacophore models have been widely applied for predicting interactions with

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P450s (19), providing insight into the important features for interaction of ligands and proteins (17, 20-30). As the P450 3A enzymes represent the most important and complex human drug-metabolizing enzymes (9), with a very broad substrate specificity and the ability to metabolize a large proportion of marketed drugs, they also can be inhibited by a wide array of molecules. Computational pharmacophores for P450 3A4 have been developed for substrates (31) and inhibitors (28, 31, 32) using an array of kinetic constants [Km, Ki(apparent) and IC50] (19). More recently, with the very large screening sets that have been generated for the P450s, there has been a shift toward machinelearning methods, recursive partitioning, K-nearest neighbors (33-35), and support vector machines for classification type computational models (36, 37). In the current study, we have taken a combined in silico-in vitro approach to predict those compounds likely to cause TDI of P450 3A4 in human liver microsomes. In contrast to earlier studies, we have used a Bayesian classification approach (38, 39) with simple, interpretable molecular descriptors as well as molecular function class fingerprints of maximum diameter 6 (FCFP_6) (10) to classify P450 3A4 TDI and describe new chemical substructures that form a TDI. This computational approach has been integrated into discovery at multiple research stages, thus influencing the screening paradigm for TDI.

Materials and Methods Reagents. Acetonitrile (MeCN), glucose-6-phosphate, glucose6-phosphate dehydrogenase from baker’s yeast, 0.1 M magnesium chloride solution, midazolam, β-nicotinamide adenine dinucleotide phosphate sodium salt hydrate (NADP+), 1 M potassium phosphate dibasic solution, and 1 M potassium phosphate monobasic solution were purchased from Sigma Aldrich (St. Louis, MO). Human liver microsomes, pooled from 60 male and female donors, were obtained as a special order from BD Gentest (Bedford, MA). All other materials were of the highest quality attainable. Inactivation Kinetics. The time-dependent inactivation experimental approach was based on a truncated inactivation constant (KI) and the maximum rate of inactivation (kinact) experiment. This type of approach was derived from well-established practices (40-42). This assay determined enzyme inactivation using a preincubation of the human liver microsomes (at 37 °C), NADPH, and compounds of interest tested at two concentrations and two preincubation time points (2 + 2), followed by dilution to a secondary mix containing a probe substrate. All biological reaction dilutions were in 100 mM potassium phosphate buffer with 1 mM MgCl2. The primary reaction in 200 µL total volume proceeded with a 5 min preincubation of 160 µL of 1.25 mg/mL human liver microsomes (1 mg/mL final) and 20 µL of 10 mM NADPH regeneration system (final concentration: 5 mM glucose-6phosphate, 1 mM NADP+, and 1 U/mL glucose-6-phosphate dehydrogenase). The reagents were mixed and initiated with 20 µL of test compound (10 or 60 µM). A zero time point (15 µL) aliquot from the primary reaction was immediately taken and added to 285 µL of 10 µM midazolam and 1 mM NADPH regeneration system and proceeded for 6.5 min. The final concentration of microsomes transferred to the secondary reaction was 0.05 mg/ mL, and the residual test compound concentrations were 0.5 and 3 µM, due to the two concentrations tested in the experiment. This secondary reaction was then quenched with 300 µL of MeCN. At 30 min, a second 15 µL aliquot of the primary reaction was taken and incubated with the midazolam/NADPH mixture for 6.5 min in a similar fashion as the zero time point. Samples were analyzed in the MRM mode using a Sciex API 4000 mass spectrometer (Applied Biosystems, Foster City, CA) with a Shimadzu LC-10AD pump (Shimadzu Inc., Japan) and a LEAP CTC PAL autosampler (LEAP Technologies, Carrboro, NC). A Phenomenex Onyx Monolithic C18, 4.6 mm × 50 mm column (Phenomenex, Torrance, CA) was used for separation with a mobile

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phase composition of 0.1% formic acid in water (A) and 0.1% formic acid in MeCN (B). The following gradient was used at a flow rate of 3000 µL/min: from 0.01 to 0.4 min (15% B), 1.35 to 1.49 min (90% B), and 1.5 min (15% B). The sample injection volume was 20 µL, and the flow was split postcolumn with 1 mL/ min going to the mass spectrometer. The API 4000 mass spectrometer settings used an ESI, positive mode Turbo-ionspray, with the following settings: For 1′-hydroxymidazolam, the metabolite was monitored with transition (m/z) 342.2 > 203.1 and a retention time of 0.98 min, while for buspirone (internal standard), a transition (m/z) 385.7 > 122.2 and retention time of 0.95 min were used. Data Analysis. All measurements were based on the percent activity remaining of the microsomes after preincubation treatment with test compound in the presence of NADPH regeneration system. This was achieved via measurement of 1′-hydroxymidazolam formation in relation to the vehicle control at each preincubation time point (eq 1). percent activity remaining ) 100 × (compound reaction)/(average vehicle control reaction) (1) The percent change over time was derived from the percent activity remaining after the preincubation time at 30 min subtracted from the percent remaining activity after no preincubation time (eq 2).

percent change over time ) percent activity remaining t0 percent activity remaining t30

(2)

Data Interpretation. A positive TDI designation was noted if one or both of the concentrations (10 or 60 µM) resulted in a percent change over time of greater or equal to 25%. The 25% change rule had been put into place to identify relatively inefficient TDIs, such as erythromycin and clarithromycin, with kinact/KI values equal to 1.5-4 and 1 mL/min/µmol, respectively (41, 43). When TDI was evaluated in the presence of residual reversible inhibition, another rule was applied. If reversible inhibition at the zero minute preincubation was less than or equal to 70% activity remaining at either concentration and a 15% change or greater following the 30 min preincubation was observed, the result was noted as a positive TDI. Descriptor Generation and Analysis. The data analysis was performed with Discovery Studio 2.1 (Accelrys, San Diego, CA) using a structure-delimited file. Molecular descriptors were calculated with the “calculate molecular properties” protocol within the software. The following simple molecular descriptors were selected because of their interpretability and their ability to be generated with various commercial and open software. The descriptors were lipophilicity (ALogP), molecular weight, sum of atomic polarizabilities (apol), radius of gyration, number of rotatable bonds, number of rings, number of aromatic rings, number of hydrogen bond acceptors, number of hydrogen bond donors, molecular surface area, molecular polar surface area, dipole magnitude, and principal moments of inertia magnitude descriptors, which were selected as used in an earlier study (10). These descriptors were statistically analyzed for all molecules used in this study using JMP (SAS Institute Cary, NC), and summary statistics for those compounds forming TDI and those that do not form TDI were prepared using Excel (Microsoft). We also investigated analysis of the long-range functional class fingerprint description 6 keys (referred to as FCFP_6) with or without the descriptors described above as these have been used by others in recent studies with promising results for a range of applications (44). FCFPs use the “role” of an atom in the initial atom code, which is based on the quick estimate of the functional role that the atom plays. This role indicates that the atom must be a combination of the following: hydrogen bond acceptor, hydrogen bond donor, positively ionized or positively ionizable, negatively ionized or negatively ionizable, aromatic, or halogen. FCFP_6 represents that the maximum diameter explored

around each atom is six near atoms. It is important to note that FCFP_6 descriptors do not recognize differences between stereoisomers. The understanding of stereochemistry as it relates to TDI prediction is still a work in progress. Bayesian Model Generation and Validation. The descriptors described earlier in the Materials and Methods were used with a Laplacian-corrected Bayesian classification (45-49) to distinguish between compounds that are TDI positive and those that are not able to form TDI (negative) in our assay. Bayesian classification is a simple probabilistic classification model. It is based on Bayes’ theorem (eq 3):

p(h|d) )

P(d|h)P(h) P(d)

(3)

where h is the hypothesis or model, d is the observed data, p(h) is the prior belief (probability of hypothesis h before observing any data), p(d) is the data evidence (marginal probability of the data), p(d|h) is the likelihood (probability of data d if hypothesis h is true), and p(h|d) is the posterior probability (probability of hypothesis h being true given the observed data d). Bayesian statistics takes into consideration the complexity of the model as well as the likelihood of a model, such that it automatically picks the simplest model that can explain the observed data and prevents overfitting. In the Bayesian modeling software within Discovery Studio 2.1 (Accelrys), the learned models are created with a learnby-example paradigm: The user marks the sample data that are of interest (good or active), and then, the system learns to distinguish them from background data (i.e., those that are inactive). The learning process generates a large set of Boolean features (e.g., and, not, or etc.) from the input descriptors and then collects the frequency of occurrence of each feature in the good subset and in all data samples. To apply the model, the features of the sample are generated, and a weight is calculated for each feature using a Laplacian-adjusted probability estimate to account for the different sampling frequencies of different features. The weights are summed to provide a probability estimate, which is a relative predictor of the likelihood of that sample being from the good subset (e.g., a more positive value). The first Bayesian model was built utilizing only the molecular descriptors described in the Descriptor Generation and Analysis section of the Materials and Methods. This model was built using 1853 molecules (553 positive TDI) from across all Pfizer laboratories and validated using leave-one-out cross-validation. Each sample was left out one at a time, and a model was built using the remainder of the samples. That model, in turn, was used to predict the left-out sample. Once all of the samples had predictions, a crossvalidation receiver operator curve (ROC) plot was generated, and the cross-validated (XV) ROC area under the curve (AUC) was calculated (Table 1). The ROC curve was created by plotting (onespecificity) on the x-axis against the sensitivity on y. A random test that cannot distinguish between the groups will give a straight line from (0,0) to (1,1). As the accuracy of the test improves, the curve moves further toward the ideal situation where both sensitivity and specificity are 1 (0,1). The accuracy of the test was assessed by measuring the AUC. Typically, the results are presented with the AUC ranging from 0.5 (a random model) to 1.0 (a perfect model). A second Bayesian model was developed using the same molecular descriptors in addition to FCFP_6 descriptors. This model also used the same training set and was validated as described above using the XV ROC AUC approach. For both models, once all of the samples had predictions, an ROC enrichment plot was generated, and the percentage of true category members was captured at a particular percentage cutoff. For example, a column labeled “1%” would be the percentage of true category members (e.g., actives) that were found in the top 1% of the list, when sorted by the model score (Supporting Information, Table 1). This table shows the output name, the percentage of samples that are in that particular category, the number of category members, and the percentage of true members



Table 1. XV Results for Bayesian Model Buildinga no. of molecules XV (LOO) ROC XV (LO 30% × 100) XV (LO 50% × 100) no. of TDI LOO TP/FN in training set AUC ROC AUC ROC AUC positive best split LOO FP/TN

model model 1: simple descriptors model 2: simple descriptors + FCFP_6 model 3: all molecules simple descriptors + FCFP_6

1853

0.75

0.83

0.83

553

–0.084

1853

0.85

0.83

0.83

553

–5.550

2071

0.85

0.84

0.84

592

–6.497

402/151 413/887 438/115 269/1031 472/120 306/1173

a FN, false negative; FP, false positive; LOO, leave one out; LO, leave out %; TDI, time-dependent inhibition; TN, true negative; TP, true positive; and best split, Bayesian score cutoff between active and inactive.

found. Percentages that are less than 100% are in bold. The percentile results table shows, for each model, the cutoff needed to capture a particular percentage of the TDI positive samples (Supporting Information, Table 2). For each cutoff, it shows the estimated percentages of false positives and true negatives, in that order, for the TDI negative samples. This table is designed to assist in selecting a cutoff value that best balances the desire to capture as many good predictions as possible, while keeping the number of false positives at a minimum. The rates shown in this table are estimates derived from the XV data. Cutoffs that lead to 10% or greater false positives are displayed in bold for ease of identification. The category statistics table shows a comparison across the three models (Supporting Information, Table 3). For each group, the number of members/nonmembers (N), the mean prediction for each subset (Mean), and the estimate standard deviation of the predictions for each subset (StdDev) are given. Both models 1 and 2 were evaluated with two separate test sets of TDI data that were generated over time. Principal component analysis (PCA) available in Discovery Studio was used to compare the molecular descriptor space for the test and training sets (using descriptors ALogP, molecular weight, number hydrogen bond donors, number of hydrogen bond acceptors, number of rotatable bonds, number of rings, number of aromatic rings, and molecular fractional polar surface area). In addition, a literature data set was used as a third test set. The 27 molecules from the Ghanbari paper (7) were not used in the original training data set. These molecules were combined with the training set compounds (1853) to generate the PCA analysis (1880 total). The two Bayesian models were then used to make predictions for the test set. A final third model was built including all of the training molecules plus both of these test sets from our laboratory (2071 compounds) and was again validated using leave-one-out cross-validation. All models generated were additionally evaluated by leaving out either 30 or 50% of the data and rebuilding the model 100 times to generate the ROC AUC, using protocols made available in Discovery Studio. Additionally, the 27 molecules from the Ghanbari paper (7) were evaluated for their similarity to molecules in Bayesian model 1 using MDL keys and the Tanimoto similarity. MDL keys are a set of 960, mostly substructural features, developed for rapid substructural searching, The presence or absence of these features can be denoted with bits (e.g., 1 or 0, respectively would represent such bits). The Tanimoto similarity can be described in eq 4:

SA/(SA + SB + SC)

(4)

where SA represents the number of bits present in both the target and the reference molecules, SB is the number of bits in the target but not the reference molecule, and SC is the number of bits in the reference but not the target molecule. The Tanimoto similarity was calculated using a standard protocol available in Discovery Studio.

Results Bayesian Models. Although there is considerable discussion about how to evaluate computational models in the environmental sciences research field (50, 51), there is as yet no clear standard methods approved (to our knowledge) for evaluating model robustness for the pharmaceutical industry. There are five principles (51) suggested to be associated with QSAR models:

(1) a defined end point; (2) use of an unambiguous algorithm; (3) defined domain of applicability for the model; (4) use appropriate measures of goodness-of-fit, robustness, and predictivity; and (5), if possible, provide mechanistic interpretation if they are to achieve regulatory acceptance in the environmental sciences field. In this study, we provide a defined end point as described above in the data interpretation section. We used an unambiguous Bayesian algorithm that is commercially available and has been widely used by both industry and academics alike. As will be described below, we have ensured which compounds were within the domain of applicability of the model. In terms of the models goodness of fit, robustness, and predictivity, we have taken multiple approaches. We initially evaluated the models with multiple cross-validation approaches, which are generally optimistic, and then, we evaluated the models with multiple external test sets as this is more representative of the needs of changing projects and chemical space coverage in the industry. Finally, we have provided some mechanistic interpretation of the models derived. The XV ROC AUC for model 1 with 1853 molecules built with simple molecular descriptors alone was 0.752, and this improved to 0.847 for model 2 with the addition of FCFP_6 descriptors. By using the FCFP_6 descriptors, we can identify those substructure descriptors that contribute to the activity for TDI (Figure 1A) and those that are not present in active compounds (Figure 1B). It is obviously apparent that the indazole substructure is dominant in the features needed for TDI activity alongside other nitrogen-containing aromatic ring systems (pyrazole and the methoxy-aminopyridine) (Figure 1A). Several of the features not commonly present in TDI actives included nonaromatic nitrogen rings (Figure 1B). The enrichment for these two Bayesian models is similar (Supporting Information, Table 1) with model 2 being slightly better in terms of the percentile results (Supporting Information, Table 2), and the category statistics indicate a larger separation between TDI actives and TDI inactives for model 2 (Supporting Information, Table 3). When model 1 is analyzed using PCA with test set 1 (three components total variance explained is 0.812), it suggests that in this descriptor space the test set molecules are close to the training set molecules with some TDI positive compounds a little further away from the training set and possibly indicating that they are outside the domain of applicability (Figure 2). PCA analysis of test set 2 (three components total variance explained is 0.826) again indicated that most compounds were close to the training set, although again some of the TDI positive compounds were further away (Figure 3). Predicting test set 1 with the Bayesian model with the FCFP_6 descriptors appears to yield an initially faster hit rate, for example, the actives are prioritized by their Bayesian score and thus generally identified before inactives. However, the model with only the simple molecular descriptors finds all of the TDI positive compounds faster, denoted by the steeper initial slope. Both models would appear substantially better than the random hit rate (Figure 4).

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Figure 1. Model 2. Simple molecular descriptors with FCFP_6 descriptors: features important for TDI positive (A) and features not important for TDI positive (B). Each panel shows the naming convention for each fragment, the numbers of molecules in which it is present that are active, and the Bayesian score for the fragment.

Figure 2. PCA test set 1. Yellow spheres, test set; and blue, training set.

It is possible to combine both of these Bayesian models and use them to rank the TDI positive molecules not previously identified with either model. The use of both models in this way seems to improve the hit rate slightly. The maximum rate of finding actives also indicates how well the models perform (e.g., 90% of the TDI positives are found in the first sampling of 10 compounds and can be correctly classed as TDI positive using the “positive in either model,” as compared with 80% with FCFP_6 descriptors alone). Another way to look at this is that nearly 50% of the TDI positives are found after screening less than 20% of the data set using the combination of both models. When test set 2 is evaluated with models 1 and 2, both perform similarly, with model 1 possessing a slightly increased initial rate of TDI positive identification (Figure 5). Once again, the combination of both Bayesian models performs better than either model alone, finding 80% of the TDI positives in the top 20% of the data set when sorted by Bayesian score. All of these ROCs show better than random ability to identify the TDI positive molecules.

An additional external data set of molecules not in the training or test sets was obtained from the literature (7); PCA analysis of this external data set (three component total variance explained is 0.81) indicated that some of the molecules were quite separate from the training set and outside the domain of applicability (Supporting Information, Figure 1) such as the structurally dissimilar molecules (Table 2), for example, 4-ipomeanol (Supporting Information, Figure 1C). Some degree of correlation (r2 ) 0.47, p value 0.0001) was observed between the Tanimoto similarity and the Bayesian score for model 1 (Supporting Information, Figure 2), indicating that molecules with higher similarity to those in the training set were more likely to be predicted with a higher Bayesian score (TDI). The molecules with negative scores have a much lower mean Tanimoto similarity (0.62, SD 0.1) versus those with positive scores (0.78, SD 0.09), and this difference is statistically significant (two-sided t test p ) 0.0004). The Bayesian model generated using simple molecular descriptors and FCFP_6 descriptors (model 2) scored nearly all of the test set 3 molecules

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the two-tailed t test. It was found that for the 13 descriptors generated, values were higher (Supporting Information, Table 4) for TDI-forming compounds, and all were statistically significant with most possessing p < 0.0001. For example, TDIforming compounds are generally larger and more hydrophobic. This would represent a general indicator for drug design purposes, smaller molecules may be less likely to have TDIforming potential with P450 3A4, and this conforms with our previous findings for MIC formation (10). Molecular Features Important for TDI. Molecular insights into the general properties of TDI-forming compounds were obtained by using the FCFP_6 descriptor results from Discovery Studio to select molecules with a common substructure and analyze those that are TDI forming from those that do not form the TDI. As demonstrated in Figures 1 and 6, the indazole feature seems to be a new feature that is important for TDIforming compounds.

Discussion

Figure 3. PCA test set 2. (A) All molecules and (B) actives. Yellow spheres, test set; and blue, training set.

with negative values. It is important to note that this literature data set consisted of only mechanism-based inactivators (e.g., MBI with varying degrees of KI and kinact), but basically, there were no non-MBI compounds included. The training and test set (1 and 2) molecules were combined and used to create a “final” combined Bayesian model with 2071 molecules that used the simple molecular descriptors and FCFP_6 fingerprints. This model had a slightly improved XV ROC AUC value as compared with model 2 (Table 1) and similar enrichment, percentile, and category statistics (Supporting Information, Tables 1-3). The key contributing FCFP_6 descriptors (Figure 6) were similar to those in model 2, for example, the indazole substructure (as compared with Figure 1). Also, several of the structural features not in TDI active compounds included nonaromatic nitrogen containing rings. All Bayesian models generated were evaluated by leaving out either 30 or 50% of the data and rebuilding the model 100 times to generate the XV ROC AUC. In each case, the leave out 30 or 50% testing AUC value was comparable to the leave-one-out approach, and these values were very favorable, indicating good model robustness (Table 1). Descriptor Analysis. All readily interpretable descriptors calculated for TDI and non-TDI molecules were compared using

Many major pharmaceutical companies are pursuing multiparameter optimization, multiobjective optimization (52), or multidimensional drug discovery (35, 53-56). Multiparameter optimization of safety, pharmacology, and absorption, distribution, metabolism, and distribution (ADME) properties in research has been facilitated by the development of automated assays with high-throughput capabilities. Drug metabolism and enzyme inhibition are necessary components of multiparameter optimization. Methods for predicting whether a compound will inhibit an enzyme include computational models built from in vitro assay data that can play an important role in understanding potential drug interactions very early in discovery. Because of automated assays and advances in LC-MS techniques, laboratories have produced large high-quality data sets that have enabled the development of a number of computational in silico models (32, 57). Computational methodologies are becoming an increasingly integrated part of the drug discovery process (11-14). One of the in vitro assays that has been successfully modeled at Pfizer, using tens of thousands of data points, is the P450 cocktail assay using clinically relevant probes (58-61). While there are many mechanisms that can cause TDI, the most commonly observed are due to metabolite formation or MBI due to direct conjugation to either the heme of P450 or the apoprotein, resulting in loss of enzyme activity (5-7, 10, 41). An automated robotic assay has been established to test the ability of NCEs to cause TDI and, as such, aid in the identification of the above mechanisms. The automation of the 2 + 2 assay described herein has enabled the generation of over 2000 molecules to date from multiple therapeutic programs and chemical libraries and has allowed the development of multiple in silico TDI models. It should be noted that our in silico approach will inherit the same limitations as the in vitro experiment. The limitations of the in vitro assay include the lack of sensitivity to identify compounds with low observed inactivation rate constant, which are unidentifiable at the 30 min preincubation time point. Conversely, because of the low number of preincubation time points assessed, achieving true MichaelisMenten first-order kinetics will not be known; thus, there is a potential to underpredict the true magnitude of TDI. A compromise between sensitivity and first-order kinetics was adopted by choosing a 30 min preincubation time. Also, confounding reversible inhibition seen with parent compound after dilution may obscure a positive result for TDI. Data obtained from low-solubility NCEs or compounds with high microsomal binding, nonspecific binding, and chemical instabil-



Figure 4. Test set 1. Receiver operator curve showing the rate of finding the 19 TDI positives in a total of 62 molecules tested. Legend: solid triangle, model 1; solid square, model 2; solid circle, TDI positive in either model; cross, maximum rate of finding TDI positives; and empty diamond, random rate of finding TDI positives.

Figure 5. Test set 2. Receiver operator curve showing the rate of finding the 20 TDI positive compounds in 156 total compounds. Legend: solid triangle, model 1; solid square, model 2; solid circle, TDI positive in either model; cross, maximum rate of finding TDI positives; and empty diamond, random rate of finding TDI positives.

Table 2. External Test Set Predictions Using Molecules from Ref 7a

molecule ritonavir saquinavir L-754-394 irinotecan gomisin C silybin nelfinavir azithromycin clarithromycin triacetyloleadomycin amiodarone amprenavir norverapamil diltiazem SN38 N-demethyltamoxifen tamoxifen mifepristone hydrastine bergamottin glabridin gestodene 17-R-ethynylestradiol isoniazid fluoxetine diclofenac 4-ipomeanol a

literature MIC (1 ) active, 0 ) inactive)(10) 1 0

1 1 1 1 1 1 1

1

literature Ki range(7) 0.1–0.17 0.17–0.65 7.5 24 0.4 32–132 0.48–0.57 623–690 37–41 0.18 10 0.26–0.37 2.1–5.9 2.2 26 2.6 0.2 4.7 110 1.9–24 7 46 18 28–228 21 1640 20

literature kinact range(7)

closest MDL keys Tanimoto similarity

Bayesian model 1 simple descriptors

Bayesian model 2 FCFP_6

0.08–0.4 0.26–0.31 1.62 0.06 0.092 0.06–0.08 0.22–0.47 0.016–0.025 0.042–0.046 0.15 0.032 0.59–0.73 0.21–1.12 0.17 0.1 0.08 0.04 0.089 0.23 0.36–0.7 0.14 0.4 0.04 0.065–0.08 0.018 0.246 0.15

0.675 0.792 0.847 0.744 0.704 0.778 0.74 0.898 0.981 0.787 0.662 0.708 0.84 0.739 0.679 0.542 0.604 0.554 0.721 0.567 0.623 0.51 0.604 0.633 0.667 0.587 0.413

4.481 4.306 3.94 3 2.926 2.925 2.078 2.033 0.556 0.533 0.191 0.026 –1.248 –1.822 –2.105 –3.016 –3.568 –3.664 –3.925 –4.217 –5.683 –8.25 –9.179 –9.864 –10.228 –10.5 –11.629

–5.56 –15.737 –8.491 –16.846 –0.175 –3.721 –13.992 –26.094 –26.486 –13.915 –10.698 –9.771 2.206 –7.734 –10.782 –9.512 –12.729 –11.716 –8.222 –9.883 –14.386 –16.41 –13.956 –16.074 –11.155 –18.132 –15.482

Best split model 1 ) -0.084, and best split model 2 ) -5.550. Bayesian scores above the best split value represent a predicted time-dependent inhibitor.

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Figure 6. Model 3. Simple descriptors with FCFP_6: features important for TDI positive (A) and features not important for TDI positive (B). Each panel shows the naming convention for each fragment, the numbers of molecules in which it is present that are active, and the Bayesian score for the fragment.

ity can all result in erroneous data (62). However, the advantage of in silico approaches, when highly predictive of experimental results, is their ability to virtually assess compounds prior to their synthesis, thus reducing the potential for the development of nonproductive compounds (11-14). This in silico method also allows for filtering of compounds that absolutely must be screened in vitro due to the potential for TDI and limit the numbers of compounds screened that are low risk. In this study, Bayesian models generated using FCFP_6 descriptors were essential to clearly identify that the indazole ring, the pyrazole, and the methoxy-aminopyridine rings are important for TDI (Figures 1 and 6). Therefore, future efforts will focus on following up on compounds containing these rings by in vitro testing using the 2 + 2 assay, spectral analysis, and molecular modeling including docking in the P450 3A4 active site (data not shown) regardless of whether or not said compounds are predicted by the model to exhibit TDI. In the context of other computational tools and technologies available for TDI determination, we are only aware of the model for MIC (10), which was based on 54 molecules. To date, we have not seen large-scale models such as the one described here. The approximately 2000 compounds employed as the training/ test sets for the computational models widely represent the Pfizer chemistry space and as illustrated in the PCA analysis of simple molecular descriptor space, there is very good overlap between the training and the test sets (Figures 2 and 3). This indicates that the test sets are generally within the domain of applicability of the models. The use of simple interpretable descriptors suggests that larger molecules are more likely to have TDIrelated issues in line with what is seen for MIC (10). Using interpretable descriptors is important so that scientists can readily understand what they need to change in the molecule. We are seeing the development of freely available tools for calculating simple interpretable molecular descriptors [like ChemSpider, www.chemspider.com (63) as well as open source software (64)], which makes observations from studies like this one more broadly accessible to those that may not have access to commercial computational software for descriptor calculation like Discovery Studio.

We have also demonstrated that the combination of both Bayesian models with interpretable molecular descriptors (as well as with and without FCFP_6 descriptors) can be used to more rapidly identify TDI positive molecules than either model alone. As these computational models have followed the recently suggested “five principles” for models used in the environmental sciences field (described earlier), we were comfortable with their wider use. The Bayesian models were therefore integrated in the Pfizer “in house” informatics platform with the additional feature of being able to highlight (using readily interpretable color coding) likely important molecular features based on the FCFP_6 descriptors (Figure 7A). This data visualization provides insights for structural modifications, assuming that they are acceptable for the target pharmacophore. We have also developed a strategy that suggests how the in vitro and in silico methods can be used together in a standard operating procedure (Figure 7B). This strategy puts the in silico model prior to in vitro TDI screening and mechanistic studies and is stage gate aligned. The model can be continuously retrained with new in vitro data; therefore, the model will expand with evolving chemistry. The best use of the in silico model to this point has been its ability to filter and cut down on the number of compounds tested in vitro in the screening-designed synthesis stage. If the model flags the compound series as being unlikely to have a TDI issue, far fewer of that compound series are tested in vitro. Second, if a compound is identified as a “likely” TDI, the compounds are tested in vitro using the 2 + 2 assay to benchmark the compounds for the project team, and this also enables continuous improvement of the model. These combined methods enable us to focus resources on those compounds of interest that need more extensive TDI characterization and elucidation of mechanism. On the basis of the authors’ previous experience, integrated in silico-in vitro approaches have reduced early ADME in vitro screening by 30%, resulting in substantial cost savings. In conclusion, with the increasing cost of drug development, the barriers to NCEs entering the clinic have increased. As an industry, we have to use the data that we generate in highthroughput screening for selection of the most appealing

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Figure 7. (A) Screenshot of Bayesian models implemented on the Pfizer intranet. Under predicted activity and atom-normalized color: HM (high moderate)/red, high or moderate risk of TDI; L (low)/blue, low risk of TDI. (B) Proposed TDI strategy in discovery and development for series and compound triage leading to first in human. FIH, first in human; FIP, first in patient; and NDA, new drug application.


molecules for further testing. Selecting those compounds that have the lowest risk of TDI, independent of mechanism, will help minimize TDI-related compound attrition and increase the odds of making it to the clinic. The availability of an in silico model for P450 3A4-mediated TDI addresses an unmet need for this major drug-metabolizing enzyme and aids medicinal chemistry at Pfizer in the design of NCEs that have a reduced risk for producing a TDI. The computational model can also be used by drug metabolism scientists to help prospectively prioritize which compounds to test in the 2 + 2 assay and to identify those molecules that are high risk that may need further more time-consuming (i.e., expensive) mechanistic characterization. As additional molecules are subsequently screened in vitro, they will be included in future training sets of the in silico model for its continuous retraining (65), thereby expanding the diversity of chemistry space and increasing the model’s domain of applicability (66-68). Further mechanistic modeling of the P450 systems in addition to computational modeling should decrease the risk of attrition due to DDIs caused by TDI. Although this study presents new models derived with proprietary data, we would expect that others could also use a similar approach to both experimentally determine TDI and then use this data for computational model generation. The advantage of the Bayesian modeling approach used herein is the costeffective fast model generation and use of interpretable descriptors that aid in molecule modification. The successful development of these TDI models should therefore be of general interest to drug metabolism scientists employed to predict DDI. Computational models are an increasingly integral part of modern drug discovery research, and it is key that we continue to develop such approaches as the in silico-in vitro strategy described so that we can go on to reliably predict which molecules will have the best chance of success in the clinic (69). Acknowledgment. S.E. gratefully acknowledges Dr. Shikha Varma-O’Brien and colleagues (Accelrys Inc.) for providing Discovery Studio 2.1, Dr. Rene´e J. G. Arnold for support, and Professor Alexander Tropsha (University of North Carolina at Chapel Hill) for bringing the model validation principles to my attention. We also thank Drs. Ellen Wu and Caroline Lee for their thoughtful discussions concerning this work. Supporting Information Available: Tables of enrichment, percentile, and category statistics results for Bayesian model building and means and SDs for the molecular descriptors and figures of a PCA plot and molecular similarity versus Bayesian score for the literature test set. This material is available free of charge via the Internet at http://pubs.acs.org.

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