Abstract
Organic cation/carnitine transporter (OCTN2; SLC22A5) is an important transporter for L-carnitine homeostasis, but can be inhibited by drugs, which may cause L-carnitine deficiency and possibly other OCTN2-mediated drug-drug interactions. One objective was to develop a quantitative structure-activity relationship (QSAR) of OCTN2 inhibitors, in order to predict and identify other potential OCTN2 inhibitors and infer potential clinical interactions. A second objective was to assess two high renal clearance drugs that interact with OCTN2 in vitro (cetirizine and cephaloridine) for possible OCTN2-mediated drug-drug interactions. Using previously generated in vitro data of 22 drugs, a 3D quantitative pharmacophore model and a Bayesian machine learning model were developed. The four pharmacophore features include two hydrophobic groups, one hydrogen-bond acceptor, and one positive ionizable center. The Bayesian machine learning model was developed using simple interpretable descriptors and function class fingerprints of maximum diameter 6 (FCFP_6). An external test set of 27 molecules, including 15 newly identified OCTN2 inhibitors, and a literature test set of 22 molecules were used to validate both models. The computational models afforded good capability to identify structurally diverse OCTN2 inhibitors, providing a valuable tool to predict new inhibitors efficiently. Inhibition results confirmed our previously observed association between rhabdomyolysis and C(max)/K(i) ratio. The two high renal clearance drugs cetirizine and cephaloridine were found not to be OCTN2 substrates, and their diminished elimination by other drugs is concluded not to be mediated by OCTN2.