title: Generation of Bayesian prediction models for OATP-mediated drug-drug interactions based on inhibition screen of OATP1B1, OATP1B1∗15 and OATP1B3.
abstract: 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 (10μM) on the uptake of [(3)H]-estradiol-17β-d-glucuronide (1μM) 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.
authors.affiliation: TNO, Zeist, The Netherlands. Electronic address: evita.vandesteeg@tno.nl.
authors.forename: E
authors.initials: E
authors.lastname: van de Steeg
authors.affiliation: TNO, Zeist, The Netherlands.
authors.forename: J
authors.initials: J
authors.lastname: Venhorst
authors.affiliation: TNO, Zeist, The Netherlands.
authors.forename: H T
authors.initials: HT
authors.lastname: Jansen
authors.affiliation: TNO, Zeist, The Netherlands.
authors.forename: I H G
authors.initials: IH
authors.lastname: Nooijen
authors.affiliation: TNO, Zeist, The Netherlands; BioFocus, A Charles River Company, Darwinweg 24, 2333 CR, Leiden, The Netherlands.
authors.forename: J
authors.initials: J
authors.lastname: DeGroot
authors.affiliation: TNO, Zeist, The Netherlands.
authors.forename: H M
authors.initials: HM
authors.lastname: Wortelboer
authors.affiliation: TNO, Zeist, The Netherlands.
authors.forename: M L H
authors.initials: ML
authors.lastname: Vlaming
mesh_terms: D001499:Bayes Theorem; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D004347:Drug Interactions / Q000502:physiology; D005544:Forecasting; D057809:HEK293 Cells; D006801:Humans; D027381:Liver-Specific Organic Anion Transporter 1; D008954:Models, Biological; D027361:Organic Anion Transporters / Q000502:physiology*; D029362:Organic Anion Transporters, Sodium-Independent / Q000502:physiology*; D004364:Pharmaceutical Preparations / Q000378:metabolism*; D000074542:Solute Carrier Organic Anion Transporter Family Member 1B3
chemical_list: D027381:Liver-Specific Organic Anion Transporter 1; D027361:Organic Anion Transporters; D029362:Organic Anion Transporters, Sodium-Independent; D004364:Pharmaceutical Preparations; C503999:SLCO1B1 protein, human; C421209:SLCO1B3 protein, human; D000074542:Solute Carrier Organic Anion Transporter Family Member 1B3
keywords: Bayesian prediction model; Computational modeling; Drug–drug interaction; OATP; Polymorphisms; Transporter