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

• Published in: Molecular pharmaceutics 2018-08, Vol.15 (8), p.3060-3068
• Main Authors: Danielson, Matthew L, Sawada, Geri A, Raub, Thomas J, Desai, Prashant V
• Format: Article
• Language: English
• Subjects: Biological Assay - methods; Chemistry, Pharmaceutical; Computer Simulation; Drug Discovery - methods; Drug Interactions; HEK293 Cells; Humans; Liver-Specific Organic Anion Transporter 1 - antagonists & inhibitors; Liver-Specific Organic Anion Transporter 1 - chemistry; Liver-Specific Organic Anion Transporter 1 - metabolism; Models, Chemical; Small Molecule Libraries - chemistry; Small Molecule Libraries - pharmacology; Structure-Activity Relationship
• ISSN: 1543-8384; 1543-8392
• DOI: 10.1021/acs.molpharmaceut.8b00168

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 show 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. 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 1200 compounds have been tested in the assay with 60:40 distribution between noninhibitors 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 noninhibitors. A SVMF-FP regression model was also created that showed an R 2 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 (3-fold). In addition to the in silico quantitative structure–activity relationship (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.