Generation of Bayesian prediction models for OATP-mediated drug-drug interactions based on inhibition screen of OATP1B1, OATP1B1∗15 and OATP1B3
article
Human organic anion-transporting polypeptide 1B1 (OATP1B1) and OATP1B3 are important hepatic uptake transporters. Early assessment of OATP1B1/1B3-mediated drug-drug interactions (DDIs) is therefore important for successful drug development. A promising approach for early screening and prediction of DDIs is computational modeling. In this study we aimed to generate a rapid, single Bayesian prediction model for OATP1B1, OATP1B1∗15 and OATP1B3 inhibition. Besides our previously generated HEK-OATP1B1 and HEK-OATP1B1∗15 cells, we now generated and characterized HEK-OATP1B3 cells. Using these cell lines we investigated the inhibitory potential of 640 FDA-approved drugs from a commercial library (10 μM) on the uptake of [3H]-estradiol-17β-d-glucuronide (1 μM) by OATP1B1, OATP1B1∗15, and OATP1B3. Using a cut-off of ≥60% inhibition, 8% and 7% of the 640 drugs were potent OATP1B1 and OATP1B1∗15 inhibitors, respectively. Only 1% of the tested drugs significantly inhibited OATP1B3, which was not sufficient for Bayesian modeling. Modeling of OATP1B1 and OATP1B1∗15 inhibition revealed that presence of conjugated systems and (hetero)cycles with acceptor/donor atoms in- or outside the ring enhance the probability of a molecule binding these transporters. The overall performance of the model for OATP1B1 and OATP1B1∗15 was ≥80%, including evaluation with a true external test set. Our Bayesian classification model thus represents a fast, inexpensive and robust means of assessing potential binding of new chemical entities to OATP1B1 and OATP1B1∗15. As such, this model may be used to rank compounds early in the drug development process, helping to avoid adverse effects in a later stage due to inhibition of OATP1B1 and/or OATP1B1∗15. Chemicals/CAS: abamectin, 71751-41-2; acemetacin, 53164-05-9; atazanavir, 198904-31-3; bromocriptine mesilate, 22260-51-1; clarithromycin, 81103-11-9; clobetasol propionate, 25122-46-7; dihydroergocristine methanesulfonate, 24730-10-7; dipyridamole, 58-32-2; docetaxel, 114977-28-5; estradiol, 50-28-2; fluindostatin, 93957-54-1; fosinopril, 88889-14-9, 98048-97-6; losartan, 114798-26-4; mifepristone, 84371-65-3; nicardipine, 54527-84-3, 55985-32-5; olmesartan, 144689-63-4; pranlukast, 103177-37-3; pyrantel embonate, 22204-24-6; rapamycin, 53123-88-9; rifampicin, 13292-46-1; rifamycin, 6998-60-3, 14897-39-3, 15105-92-7; salazosulfapyridine, 599-79-1; suramin, 129-46-4, 145-63-1; telmisartan, 144701-48-4; tibolone, 5630-53-5; troglitazone, 97322-87-7
Topics
Bayesian prediction modelComputational modelingDrugdrug interactionOATPPolymorphismsTransporterAbamectinAcemetacinAtazanavirBromocriptine mesilateClarithromycinClobetasol propionateDihydroergocristine methanesulfonateDipyridamoleDocetaxelEstradiolFluindostatinFosinoprilLosartanMifepristoneNicardipineOlmesartanPranlukastPyrantel embonateRapamycinRifampicinRifamycinSalazosulfapyridineSolute carrier organic anion transporter 1B1Solute carrier organic anion transporter 1B1 15Solute carrier organic anion transporter 1B3SuraminTelmisartanTiboloneTroglitazoneUnclassified drugUnindexed drugBayesian learningControlled studyDrug protein bindingHEK293 cell lineHuman cellNucleotide sequencePredictionPriority journalProtein expressionTandem mass spectrometryUltra performance liquid chromatography
TNO Identifier
523191
ISSN
09280987
Source
European Journal of Pharmaceutical Sciences, 70, pp. 29-36.
Pages
29-36
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