Computational Approaches for Predicting Drug Interactions with Human Organic Anion Transporter 4 (OAT4).

Abstract

Human Organic Anion Transporter 4 (OAT4) is predominantly expressed in the kidneys, particularly in the apical membrane of the proximal tubule cells. This transporter is involved in the renal handling of endogenous and exogenous organic anions (OAs), making it an important transporter for drug-drug interactions (DDIs). To better understand OAT4-compound interactions, we generated single concentration (25 μM) in vitro inhibition data for over 1400 small molecules against the uptake of the fluorescent OA 6-carboxyfluorescein (6-CF) in Chinese hamster ovary (CHO) cells. Several drugs exhibiting higher than 50% inhibition in this initial screen were selected to determine IC₅₀ values against three structurally distinct OAT4 substrates: estrone sulfate (ES), ochratoxin A (OTA), and 6-CF. These IC₅₀ values were then compared to the drug plasma concentration as per the 2020 FDA drug-drug interaction (DDI) guidance. Several screened compounds, including some not previously reported, emerged as novel inhibitors of OAT4. These data were also used to build machine learning classification models to predict the activity of potential OAT4 inhibitors. We compared multiple machine learning algorithms and data cleaning techniques to model these screening data and investigated the utility of conformal predictors to predict OAT4 inhibition of a leave-out set. These experimental and computational approaches allowed us to model diverse and unbalanced data to enable predictions for DDIs mediated by this transporter.