In Silico and In Vitro Assessment of OATP1B1 Inhibition in Drug

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In Silico and In Vitro Assessment of OATP1B1 Inhibition in Drug Discovery Matthew L. Danielson, Geri A. Sawada, Thomas J. Raub, and Prashant V. Desai Mol. Pharmaceutics, Just Accepted Manuscript • DOI: 10.1021/acs.molpharmaceut.8b00168 • Publication Date (Web): 21 Jun 2018 Downloaded from http://pubs.acs.org on June 22, 2018

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Molecular Pharmaceutics

TITLE PAGE In Silico and In Vitro Assessment of OATP1B1 Inhibition in Drug Discovery

Matthew L. Danielson†, Geri A. Sawada‡, Thomas J. Raub§, and Prashant V. Desai†,* †

Computational ADME, ‡Drug Disposition, §retired Drug Disposition, Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana, 46285, United States

ACS Paragon Plus Environment

Molecular Pharmaceutics 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ABSTRACT The organic anion-transporting polypeptide 1B1 transporter belongs to the solute carrier superfamily and is highly expressed at the basolateral membrane of hepatocytes. Several clinical studies showed drug-drug interactions involving OATP1B1 thereby prompting the International Transporter Consortium to label OATP1B1 as a critical transporter that can influence a compound’s disposition. To examine OATP1B1 inhibition early in the drug discovery process, we established a medium-throughput concentration-dependent OATP1B1 assay. In order to create an in silico OATP1B1 inhibition model, deliberate in vitro assay enrichment was performed with publically known OATP1B1 inhibitors, noninhibitors, and compounds from our own internal chemistry. To date, approximately 1,200 compounds have been tested in the assay with 60:40 distribution between non-inhibitors and inhibitors. Bagging, random forest, and support vector machine fingerprint (SVM-FP) quantitative structure–activity relationship classification models were created and each method showed positive and negative predictive values > 90%, sensitivity > 80%, specificity > 95% and Matthews correlation coefficient > 0.8 on a prospective test set indicating the ability to distinguish inhibitors from non-inhibitors. A SVMF-FP regression model was also created that showed an R2 of 0.39, Spearman’s rho equal to 0.76, and was capable of predicting 69% of the prospective test set within the experimental variability of the assay (3fold). In addition to the in silico QSAR models, physicochemical trends were examined to provide structure activity relationship guidance to early discovery teams. A JMP partition tree analysis showed that among the compounds with calculated logP > 3.5 and >= 1 negatively-charged atom, 94% were identified as OATP1B1 inhibitors. The combination of the physicochemical trends along with an in silico QSAR model provides discovery project teams a valuable tool to identify and address drug-drug interaction liability due to OATP1B1 inhibition. KEYWORDS OATP1B1, transporters, in silico, QSAR, physicochemical properties INTRODUCTION Transporters are known to modulate the absorption, distribution, metabolism, and excretion (ADME) properties of chemical compounds. In general, transporters are categorized into two superfamilies; the ATP-binding cassette (ABC) transporters that exploit ATP-hydrolysis to export compounds out of the cell and the solute carrier (SLC) superfamily that does not require ATP-hydrolysis to uptake compounds into the cell.1 The organic anion-transporting polypeptide (OATP) 1B1 (SLCO1B1 gene) transporter is part of the SLC superfamily and is highly expressed and exclusively located at the basolateral membrane of hepatocytes.2 Several studies have shown drug-drug interactions (DDI) involving OATP1B1 that led to elevated systemic exposure of OATP1B1 substrates.3-10 For example, inhibition of OATP1B1 by cyclosporine was shown to contribute to the significant increase of statin concentrations (5 to 10 fold) in the blood after administration.8 Due to clinically relevant DDIs, OATP1B1 was identified by the International Transporter Consortium (ITC) as an important transporter that can influence a compounds disposition.11


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The original ITC white paper proposed that if the IC50 of a compound is less than or equal to 10 times the unbound Cmax at the clinically relevant dose of the new molecular entity (NME) (Cmax/IC50 >= 0.1), and the systemic exposure (AUC) of a prototypical OATP1B1 substrate (e.g., a statin) is predicted to increase more than two fold in the presence of the NME, then a clinical DDI study with a sensitive OATP1B1 substrate should be considered.11 Subsequently, all major regulatory agencies have drafted guidance for DDIs due to OATP1B1 inhibition. 12-14 (https://www.fda.gov/downloads/Drugs/Guidances/ucm292362.pdf) In an attempt to predict a compounds risk to inhibit OATP1B a priori, several publications detail in silico OATP1B1 models. Prior OATP1B1 models were constructed using computational techniques such as simple physicochemical empirical rules, pharmacophore-based techniques, and quantitative structure– activity relationship (QSAR) models due to the lack of structural information pertaining to OATP1B1.15-25 To date, the majority of in silico models were constructed on single-point inhibition data rather than IC50 values. In this publication we describe our implementation of a medium throughput assay that produces an OATP1B1 IC50 value. In addition to the establishment of the in vitro assay, we took specific steps to enrich the assay with publicly known OATP1B1 inhibitors along with our internal chemistry. This systematic enrichment was performed with the goal of creating a global in silico OATP1B1 inhibition model applicable to a variety of discovery projects. The global OATP1B1 inhibition model is designed to influence SAR decisions and trigger more comprehensive transporter studies for drug discovery projects. Our prospective testing of the models demonstrate successful prediction if a compound has the potential to inhibit OATP1B1. EXPERIMENTAL SECTION In Vitro OATP1B1 Inhibition Assay: Stably-transfected OATP1B1a-expressing HEK293-PEAK cells were maintained in Dulbecco’s Modified Eagle Medium supplemented with 10% FBS, gentamycin, and puromycin in a humidified incubator containing 5% CO2 at 37 degrees Celsius. Cells were split every three to four days and seeded onto 48well poly-D-lysine-coated plates at a density of 150,000 cells/mL. Cells were treated with 10 µM sodium butyrate in culture medium on day three, and used for assay on day four. Fluoresceinated methotrexate (FMTX) uptake linearity was determined by incubating active and inhibited HEK-PEAK OATP1B1a cells with 5 µM FMTX for 15 minutes at 37 degrees Celsius. Complete inhibition of OATP1B1 was accomplished by co-incubating the cells with 20 µM bromosulfophthalein (BSP) and FMTX. To determine an OATP1B1 IC50, test compound was diluted to 100 µM in 5 µM FMTX and serially diluted 1:3 in FMTX (concentrations tested: 100, 33, 11, 3.7, 1.2, 0.41 and 0.14 µM). Positive and negative controls were 5 µM FMTX or 20 µM bromosulfophthalein in 5 µM FMTX, respectively. Cells were rinsed with Dulbecco’s Phosphate Buffered Saline (PBS) containing 10 mM HEPES at pH 7.4. Wash buffer was replaced with 200 µL of test compound ranging from 0.14 to 100 µM in FMTX and incubated at 37



degrees Celsius. After 15 minutes, the cells were washed twice in cold PBS and the contents extracted with 100 µL 100% methanol. 80 µL of the methanol extract was transferred to a black-walled 96-well plate and fluorescence was read on a Spectramax fluorescent plate reader at λ ex = 497 nm, and λ em = 517 nm. IC50 values were calculated using GraphPad Prism version 6. Data Cleaning and Preparation: A systematic protocol was used to curate and clean the in vitro OATP1B1 inhibition data prior to data analysis and the building of QSAR models. Qualified data at the top end of the inhibition assay were set to the unqualified value, meaning > 100 µM was set to 100 µM. To determine the in vitro assay variability, IC50 values were log transformed and the root mean square error (RMSE) was calculated for compounds tested two or more times using the least square fit option in the JMPstatistical discovery software.26 The RMSE was then converted to minimum significant ratio (MSR) using equation

= 10∙√∙ to determine the variability of the experimental assay.27, 28 The MSR of the assay was determined to be three (Supplementary Figure 1). A MSR of three was then used to assign binary inhibitor or noninhibitor classes around a < 10 µM cutoff. IC50 values = 30 µM were assigned as non-inhibitors. Compounds with duplicate measurements that did not have a consistent classification were excluded from the model building process. In addition, any compound falling into the experimental uncertainty zone was not used in any classification QSAR model or the in silico analysis in an attempt to have a clean and consistent dataset. The regression QSAR model was also built using an MSR of three to clean the dataset. Compounds with repeat measurements and a ratio between the highest and lowest IC50 value greater than three were excluded from the model building process. Compounds with duplicate IC50 values within the MSR of the assay were averaged to assign a single value. Physicochemical Properties and Molecular Descriptors: Physicochemical properties used in the analysis of OATP1B1 inhibition trends were calculated as follows. clogP, clogD, and cpKa were computed using computational tools developed by Biobyte (http://www.biobyte.com) and ChemAxon (http://www.chemaxon.com). Topological polar surface area (TPSA) was calculated based on a previously published method.29 The number of hydrogen bond donors (HBD) was calculated as the sum of NH and OH atoms and the number of hydrogen bond acceptors (HBA) as the sum of N and O atoms. In addition to above mentioned physicochemical properties, over 1,800 2-D molecular descriptors were calculated for use in building the OATP1B1 inhibition QSAR models. The descriptors included common physicochemical properties, molecular connectivity indices, pharmacophoric features, hydrophobicity, among others.30 Our internal experience indicates that tree-based QSAR models effectively discern useful descriptors without the need for extensive variable selection. Although many QSAR publications report extensive use of variable selection techniques, we observe that additional variable selection methods do not improve model performance as measured by cross-validation and chronological prospective test sets across several ADME endpoints.31, 32 However, we discarded descriptors that were


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constant or near constant (> 99% identical descriptor value across all rows of compounds), or highly correlated (> 0.97 correlation between descriptor columns for all rows of compounds). Building QSAR Models The recursive partitioning (tree-based) algorithms Bagging33 and Random Forest (RF)34, along with a support vector machine algorithm based on fingerprints (SVM-FP)35, were evaluated during the model building process. Bagging and RF models were constructed using 100 and 500 trees. A variety of fingerprints were evaluated for SVM-FP classification models. 30 Training and validation sets were randomly created using an 80:20 proportion of the compounds. The 80:20 split was repeated 50 times to assess the average performance of each modeling method built using the 80% training set and evaluated against the corresponding 20% validation set. This ‘internal’ assessment constituted the outof-bag (OOB) sample that was used to estimate the internal prediction performance of the models. The fingerprint for the SVM-FP classification method was chosen based on the cross validation. For the regression based QSAR model, the same optimal fingerprint used to create the classification SVM-FP model was used. IC50 values were log transformed before model creation as variability increased with the mean (data not shown). All final QSAR models were compared using the prospective test sets described in “OATP1B1 Data Cleaning: QSAR Training and Prospective Test Set Creation”. QSAR Metrics to Evaluate Model Performance Many measures of model performance are available for a classification model. Internally, we routinely use five measures: positive predictive value (PPV), negative predictive value (NPV), sensitivity, specificity, and Matthews correlation coefficient defined in the equations below. = =

+

+

=

+

= =

+

∙ − ∙

!( + ) ∙ ( + ) ∙ ( + ) ∙ ( + )

True positive, false positive, true negative and false negative are abbreviated as TP, FP, TN, and FN. Matthews correlation coefficient reflects the agreement between the predicted and the observed binary classes and ranges from -1 to +1. Higher values indicate better model performance irrespective of skewness in the distribution of the two classes. For the regression model, the square of the correlation coefficient (R2) between the observed and predicted value was used as a performance metric. In addition to the square of the correlation



coefficient, we also used the proportion of predicted values within the MSR of the assay to judge model performance. Spearman’s rank correlation coefficient (ρ) was also reported as an indicator of the models ability to rank order the prospective test set molecules. 36 RESULTS AND DISCUSSION FMTX Uptake by HEK-PEAK OATP1B1a Cells Uptake of the fluorescent OATP1B1 substrate FMTX, expressed as relative fluorescence units (RFU), by HEK-PEAK OATP1B1a cells was linear from 10 to 25 minutes (Figure 1). Active Uptake of FMTX was approximately 6-fold greater than that of BSP-inhibited cells at 30 minutes. Michaelis-Menten analysis of dose-dependent FMTX uptake at 15 minutes determined the Km of FMTX to be 10.4 µM (Figure 2).

Time-Dependent Uptake of FMTX by Active and Inhibited HEK-PEAK OATP1B1a Cells 350

Relative Fluorescence Units

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300 250 200

Active

150

Inhibited

100 50 0 5

10

15

20

25

30

Incubation Time (minutes)

Figure 1. Time dependent uptake of FMTX into HEK-PEAK cells expressing OATP1B1. Uptake of 5 µM FMTX was determined for active (closed circles) and inhibited (20 µM BSP) HEK-PEAK OATP1B1a cells (closed triangles), in 48-well plates. Values are means ±SD of quadruplicate determinations in a single experiment.


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Figure 2. Concentration-dependent uptake of FMTX in D-PBS. Uptake of increasing concentrations of FMTX was measured at 37 degree Celsius for 15 minutes on 48-well plates with active or BSP-inhibited HEK-PEAK OATP1B1a expressing cells. OATP1B1-mediated uptake (closed circles) was calculated by subtracting passive uptake by OATP1B1-inhibited cells (open squares) from uptake by active cells (open circles). The values (quadruplicate) were fitted to the Michaelis-Menten equation using non-linear regression analysis. The Km for FMTX uptake was calculated to be 10.4 µM. For all IC50 determinations 5 µM FMTX was used.

For IC50 assessments, 100% inhibited, (5 µM FMTX plus 20 µM bromosulfophthalein, IC50 = 0.6 µM) and 100% active (5 µM FMTX in buffer) controls were used to monitor the uptake window and to calculate percent activity remaining by the following equation: % % &'%( = )

(* +'+,- − %&%( * ./-) 0 ∗ 100 (%&%( * % − %&%( * ./-)

Graphs for several classic OATP1B1 inhibitors are shown in Figure 3. IC50 values were calculated by GraphPad PRISM version 6 and the equation: 2=

(/++' + (+ − /++')) 1 + 10(345(6789 :;)∗>?>@AB)



Figure 3. IC50 determination for publically known OATP1B1 inhibitors using 5 µM FMTX as the substrate. Concentration-dependent inhibitory effect of simvastatin acid, rifampicin, bromosulfophthalein, and atorvastatin on OATP1B1a-mediated transport. IC50 values were determined by the equation 2 = (C@DD@EF(D@A:C@DD@E)) GFGH(IJK(LM89 NO)∗PQRRSRTUV)

in Prism v6 (GraphPad Software, La Jolla, CA, USA).

OATP1B1 in Vitro Assay Enrichment Promptly after the establishment of the in vitro OATP1B1 screening assay, the computational ADME group was involved to determine if a global in silico QSAR model could be created. At that time, approximately 50 publically known OATP1B1 inhibitors or substrates had been tested in the assay along with a handful of internal compounds. It was determined that the chemical space profiled in the assay thus far did not warrant a global model given the limited number of compounds and the high proportion of public compounds. In order to establish a global OATP1B1 inhibition QSAR model, we intentionally selected chemically diverse compounds to be run in the assay thereby increasing the chemical space coverage. For the first round of assay enrichment, we used prior published OATP1B1 physicochemical property trends to select compounds from our internal library for screening: molecular weight > 350, > 5 hydrogen bond


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acceptors in the molecule, slow predicted passive permeability in MDCK cells, either negative or zwitterionic in charge, and when possible compounds with clogD > 3.5 and PSA > 100.16-18, 21, 24, 37 In addition to screening compounds with specific physicochemical properties, representative compounds from all active discovery projects were screened in the assay. This decision enabled active enrichment of the data set with the chemical space most relevant for current chemistry teams along with introducing additional structural diversity. Subsequently, discovery projects found to contain strong OATP1B1 inhibitors were subjected to a hit expansion exercise where several computational algorithms were used to select similar compounds from the same chemical series along with similar compounds from different scaffolds (scaffold hopping). This provided more in depth understanding of a single chemical series and enriched the data set with project related scaffolds that may have an OATP1B1 inhibition liability. In the final round of assay enrichment, random compounds were selected from our internal library without bias toward identifying OATP1B1 inhibitors. In addition to this diverse selection, several marketed drugs were included in this round of enrichment that contained various physicochemical properties and were not previously reported to inhibit OATP1B1. This step added further structural diversity to the library of OATP1B1 screened compounds in an attempt to create a non-biased OATP1B1 inhibition data set. In total, 1,203 IC50 values comprise the OATP1B1 in vitro data set. OATP1B1 Data Cleaning: QSAR Training and Prospective Test Set Creation A total of 1,203 IC50 values, 906 unique compounds after removing duplicate runs, were measured in the OATP1B1 screening inhibition assay; 111 publically known compounds exist in the dataset creating a 90:10 ratio of internal to public compounds (publically known compounds and average IC50 values listed in Supplementary Table 1). Prior to all analysis or QSAR build, the inhibition data were systematically curated as detailed in the Materials and Methods section. The evaluation of 130 repeated compounds (run independently on different dates) showed the OATP1B1 inhibition assay to have a MSR of three. Threefold variability was not unexpected as the reported IC50 value for several publicly known OATP1B1 inhibitors varies significantly.14 Given threefold variability in our assay, IC50 values in Supplementary Table 1 are in alignment with previously reported literature values.14 A MSR of three was then used to assign “clean” inhibitor or non-inhibitor classes around a < 10 µM cutoff. IC50 values = 30 µM were assigned as non-inhibitors. Subsequently, any compound falling into the experimental uncertainty zone was not used in any classification QSAR build or the in silico physicochemical analysis in an attempt to have a clean, consistent dataset. Following the abovementioned data curation, the OATP1B1 classification model training set consisted of 409 unique compounds; 38% of compounds were classified as inhibitors and 62% were classified as noninhibitors representing a data distribution amenable to a QSAR modeling.38 The identical curation procedure was applied to a chronological prospective test set of compounds that was never used in the training of the classification QSAR models (meaning compounds were tested after the QSAR model was built). In an attempt to mimic the real world scenario where one would not know the activity of the



compounds in the prospective test set without running them in the in vitro assay, we did not discard the compounds in the experimental uncertainty zone for the prospective test set. As expected, the prediction of such compounds is approximately equal between the classes (< 10 µM and > 10 µM) for each modeling method (Supplementary Table 2). In total, 171 compounds reside in the prospective test set with 73 of those falling into the experimental uncertain zone. The remaining 98 compounds in the prospective test set were used to evaluate the classification model performance metrics reported in Figure 5. Out of 98 prospective test set compounds, 34% of compounds were classified as inhibitors and 66% were classified as non-inhibitors providing a similar data distribution as the training set. In addition to the similar data distribution between the training and prospective test sets, Figure 4 shows that the chemical space of the prospective test set was covered by the training set.

Figure 4. Chemical space plot generated by StarDrop v6.4 using the 2D visual clustering algorithm based on chemical structure. Path-based fingerprints and Tanimoto similarity define chemical similarity and the algorithm reduces the high-dimensional data into two dimensions (x and y axes). Compounds in close proximity to each other represent similar compounds. As the distance between compounds increases, the compounds become less similar. Training set compounds are colored grey, prospective test set compounds colored red. In general, the chemical space of the prospective test set was covered by the training set.

For the regression QSAR model, the same initial 906 unique compounds were curated as detailed in the Materials and Methods section using a MSR of three. Without the need to assign an experimental uncertainty zone around a cutoff, 676 unique compounds represent the training set. After identical data curation, 159 compounds comprised the prospective test set. The lack of an experimental uncertainty


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zone in the regression model provided larger training and prospective test sets compared to the classification models. QSAR Model Performance Based on the internal OOB assessment where the validation sets were a subset of original training set, Bagging, RF, and SVM-FP classification models exhibited similar performance with PPV and NPV values between 85 – 90% (data not shown). Subsequently, we compared the Bagging, RF, and SVM-FP classification models based on the prediction of 98 compounds in the prospective test set (Figure 5).

Figure 5. Confusion matrices and performance metrics evaluating 98 compounds in the classification model prospective test set. A) Bagging, B) RF, and C) SVM-FP algorithm results are shown.

In the prospective test set analysis, all three classification QSAR algorithms performed well with PPV and NPV values > 90%, sensitivity > 80%, specificity > 95%, and MCC > 0.8. At first glance, one might be tempted to conclude that RF is the optimal algorithm based on slightly higher performance metrics compared to the Bagging and SVM-FP methods. However, while slightly higher, it is premature to make such a conclusion as the apparent performance difference between RF and SVM-FP stems from the misprediction of a single compound. As new additional in vitro data becomes available, the performance metrics for all QSAR models will fluctuate when chronologically updated. Therefore, all QSAR models should be monitored and performance metrics re-evaluated with future model updates.



Figure 6. SVM-FP regression model performance on the 158 compound prospective test set. 109 (69%) compounds were predicted within assay variability (between 3-fold dotted lines, colored green). R2 for the prospective test set equaled 0.39 (red solid line), and Spearman’s rho equaled 0.76. The line of unity is shown by the black solid line. Log transformed IC50 values shown.

Figure 6 summarizes the prospective testing of the regression based SVM-FP model. Out of 158 compounds in the prospective test set, 109 compounds were predicted within three fold, the experimental assay variability. The R2 of the model was 0.39 and Spearman’s rho equaled 0.76 further indicating that the model would be useful in predicting experimental IC50 values when prioritizing the design and synthesis of compounds toward decreasing OATP1b1 inhibition. In our experience, Bagging and RF models generally perform well on ADME datasets consisting of hundreds of compounds. As the dataset grows in size (typically thousands), SVM-FP models equal and often outperform Bagging and RF models in terms of performance metrics and increased speed of prediction. Based on the anticipated use of the OATP1B1 model across multiple discovery projects consisting of large sets of compounds (including virtual libraries), along with the continued expansion of the in vitro dataset, we expect to maintain a single SVM-FP model (classification or regression) in the future assuming subsequent QSAR performance metrics support such a decision.

Physicochemical Trends


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Figure 7. JMP partition analysis tree of 409 compounds in the classification OATP1B1 training set. The combination of clogP > 3.5 and one or more negatively charged atoms provided a 25-fold enrichment of inhibitors compared to original dataset distribution.

The OATP1B1 classification models accurately classified compounds as inhibitors or non-inhibitors; however, it can be difficult to distill why the model made any given prediction. In an attempt to provide structural guidance to medicinal chemists in order to mitigate a predicted OATP1B1 inhibition risk, the physicochemical properties of inhibitors and non-inhibitors were explored in several prior publications.15-19, 21-25, 37 LogP is consistently reported to be an important descriptor in separating inhibitors from non-inhibitors in previous publications. Other physicochemical parameters such as hydrogen bond acceptor strength, the presence of a negatively-charged atom, the number of hydrogen bond donors and acceptors, polar surface area (PSA), and molecular weight have been reported to be useful in separating OATP1B1 inhibitors versus non-inhibitors. Taking a similar approach, common physicochemical descriptors were calculated for the entire classification QSAR training set consisting of 409 compounds, 154 classified as inhibitors and 255 classified as non-inhibitors (see Methods: Physicochemical Properties and Molecular Descriptors). Figure 7 displays a recursive partitioning tree created by JMP where all physicochemical descriptors were treated as independent variables to explain the dependent variable (inhibitor or non-inhibitor classification in this case). An advantage of this statistical technique is its ability to consider all physicochemical parameters (instead of querying if each parameter is significant on its own), thereby determining what combination and order of physicochemical parameters leads to the best separation of activities. Similar to the previously published results, clogP was found to be the physicochemical property that provided the best separation of inhibitors vs non-inhibitors. Among the compounds with clogP > 3.5, 75% were observed to be OATP1B1 inhibitors. The 3.5 clogP cutoff provided a 5-fold enrichment of inhibitors compared to the original distribution. Further splitting the inhibitor branch (clogP > 3.5) of the partition tree, the presence of a negatively-charged atom provided the next best separation of



inhibitors versus non-inhibitors. If a compound had a predicted clogP > 3.5 and >= 1 negatively-charged atom, 94% of the time the compound was an OATP1B1 inhibitor. The combination of clogP > 3.5 and >= 1 negatively-charged atom provided a 25-fold enrichment of inhibitors compared to original dataset distribution. Traversing the opposite branch (non-inhibitor side) of the tree, if a compound had a clogP < 3.5, 83% of the time the compound was not an inhibitor of OATP1B1. Additional splitting of this branch found molecular weight as the next most influential descriptor to separate inhibitors from non-inhibitors. A compound with a clogP < 3.5 and a molecular weight < 420 was a non-inhibitor 96% of the time. clogP alone provided a 3-fold enrichment and in combination with molecular weight < 420 provided a 15-fold enrichment of non-inhibitors. When interpreting the JMP partition tree in Figure 7, a couple of things should be noted. First, each split (and the subsequent combination of physicochemical parameter splits) is accompanied by the probability and the number of compounds in the leaf of the tree. For example, consider the inhibitor branch of the tree, clogP >= 3.5. 147 compounds out of 409 fall into this branch, with 110 (75%) being inhibitors (< 10 µM classification) and 37 (25%) being non-inhibitors (> 10 µM classification). While stating “OATP1B1 inhibitors have higher clogP values than non-inhibitors” is correct based on the probabilities, this should not be interpreted as an exclusive rule. A clogP > 3.5 provides a high likelihood of being an OATP1B1 inhibitor, but it is evident from this branch that 37 compounds (25%) are exceptions to this trend. Second, one should not interpret individual leaves without considering the entire branch. If one interprets that “all OATP1B1 inhibitors are likely to have a negatively-charged atom”, this isn’t accurate. The partition tree in Figure 7 suggests that a compound with a clogP > 3.5 and a negatively-charged atom has a high probability of being an OATP1B1 inhibitor. However, when considered in isolation, Figure 8 illustrates that the presence of a negatively-charged atom alone provided no meaningful separation between inhibitors and non-inhibitors (approximately 50:50 ratio between inhibitors and non-inhibitors when a compound contains a negatively charged atom). While the OATP1B1 name itself suggests that this transporter has an affinity toward compounds containing a negatively charged atom, Figure 8 demonstrates that a presence of a negatively charged atom in isolation of other physicochemical descriptors does not discern OATP1B1 inhibitors from non-inhibitors. It should be noted that net negative charge does show a meaningful separation between inhibitors and noninhibitors (Supplementary Figure 2). Despite this trend, net charge was not found to be a discriminating descriptor when included in the JMP partition tree analysis and did not replace the negatively charged atom descriptor in the tree. To further check if our internal OATP1B1 dataset followed individual physicochemical trends published on other datasets, we examined physicochemical parameters individually to determine if there was statistical separation between inhibitors and non-inhibitors (Supplementary Figure 3). Several previously reported physicochemical trends associated with OATP1B1 inhibitors were not evident in our dataset. The number of hydrogen bond donors and acceptors, PSA, and containing a negatively-charged atom showed no statistical separation of inhibitors versus non-inhibitors (Supplementary Figure 3).


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Although we evaluated physicochemical parameters individually to compare to prior publications, we suggest using the multi-property physicochemical trends similar to that described in Figure 7. Multiproperty trends considers all physicochemical parameters discerns the optimal combination to separate inhibitors from non-inhibitors. 100 %

21

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90 % 80 %

Color by IC50 (uM) 10

109

70 % 60 % 50 %

146

40 % 30 % 20 % 10 % 0% x≤0

0 0 bin (containing a negatively charged atom) suggesting that this descriptor alone did not separate inhibitors from non-inhibitors.

CONCLUSIONS: The OATP1B1 transporter was identified by the International Transporter Consortium (ITC) as an important transporter that can influence a compounds disposition. To characterize a compounds risk of inhibiting OATP1B1, we established a medium-throughput screening assay. To date, approximately 1,200 public and internal compounds have been profiled in this assay. Through deliberate assay enrichment, our OATP1B1 dataset contains an 80:20 ratio of internal compounds compared to public compounds. Using an IC50 of 10 µM as a cutoff to classify a compound as an inhibitor or non-inhibitor of OATP1B1, the database contains approximately a 60:40 distribution of non-inhibitors versus inhibitors and was amenable to QSAR modeling. Bagging, RF, and SVM-FP QSAR methods were evaluated in order to create an in silico OATP1B1 inhibition model. All three QSAR algorithms performed well on an external perspective test set with PPV, NPV values > 90%, sensitivity > 80%, specificity > 95%, and MCC > 0.8 indicating all methods could distinguish between inhibitors and non-inhibitors. The regression based SVM-FP model also showed promising results with 69% of the prospective test set predicted within experimental variability. The R2 value of the model equaled 0.39 and Spearman’s rho equaled 0.76 indicating that the regression model



is also useful in predicting experimental IC50 values to prioritize design and synthesis of compounds toward decreasing OATP1b1 inhibition. In addition to the in silico QSAR models, physicochemical trends were examined in order to provide SAR guidance to early discovery teams. Some physicochemical trends were found to be similar to those previously reported in the literature, but not all. clogP was the single physicochemical descriptor that provided the best separation of inhibitors vs non-inhibitors, providing 5-fold enrichment. Compounds with a clogP > 3.5 and containing a negatively-charged atom were shown to be OATP1B1 inhibitors 94% of the time in our dataset and this combination of physicochemical parameters provided a 25-fold enrichment of inhibitors. The multi-property physicochemical trends along with an in silico QSAR model will provide project teams a valuable tool to identify and address drug-drug interaction liability due to OATP1B1 inhibition.


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SUPPORTING INFORMATION: The Supporting Information is available free of charge via the internet at http://pubs.acs.org. Supplementary Figure 1: 130 repeated compounds used to determine the MSR for the OATP1B1 inhibition assay. Supplementary Figure 2: Distribution of inhibitors vs non-inhibitors when examining net charge as a descriptor in isolation. Supplementary Figure 3: Individual physicochemical parameters were calculated to determine their ability to distinguish between OATP1B1 inhibitors and non-inhibitors. Supplementary Table 1: Average OATP1B1 IC50 values for publicly disclosed compounds. Supplementary Table 2: Classification model prediction of 73 compounds in the prospective test set that fall into the experimental uncertain zone.

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In Silico and In Vitro Assessment of OATP1B1 Inhibition in Drug

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