Abstract
Statistical analysis was performed on physicochemical descriptors of ~250 drugs known to interact with one or more SLC22 "drug" transporter (i.e., SLC22A6 or OAT1, SLC22A8 or OAT3, SLC22A1 or OCT1, and SLC22A2 or OCT2), followed by application of machine-learning methods and wet-lab testing of novel predictions. In addition to molecular charge, organic anion transporters (OATs) were found to interact with planar structures, whereas organic cation transporters (OCTs) interact with more three-dimensional structures (i.e., greater SP3 character). Moreover, compared to OAT1 ligands, OAT3 ligands possess more acyclic tetravalent bonds and have a more zwitterionic/cationic character. Multiple pharmacophore models based on the drugs were generated and, consistent with the machine-learning analyses, one unique pharmacophore created from ligands of OAT3 possessed cationic properties similar to OCT ligands; this was confirmed by quantitative atomic property field analysis (APF). Virtual screening with this pharmacophore, followed by transport assays, identified several cationic drugs that selectively interact with OAT3 but not OAT1. Although this analysis may be somewhat limited by the need to rely largely on inhibition data for modeling, wet-lab/in vitro transport studies, as well as analysis of drug/metabolite handling in Oat and Oct knockout animals supports the general validity of the approach--which can also be applied to other SLC and ABC drug transporters. This may make it possible to predict the molecular properties of a drug or metabolite necessary for interaction the transporter(s), thereby enabling better predicting of drug-drug interactions (DDI) and drug-metabolite interactions (DMI).
- The American Society for Pharmacology and Experimental Therapeutics