Assessment of Substrate-Dependent Ligand Interactions at the Organic Cation Transporter OCT2 Using Six Model Substrates

Abstract

Organic cation transporter (OCT) 2 mediates the entry step for organic cation secretion by renal proximal tubule cells and is a site of unwanted drug-drug interactions (DDIs). But reliance on decision tree-based predictions of DDIs at OCT2 that depend on IC values can be suspect because they can be influenced by choice of transported substrate; for example, IC values for the inhibition of metformin versus MPP transport can vary by 5- to 10-fold. However, it is not clear whether the substrate dependence of a ligand interaction is common among OCT2 substrates. To address this question, we screened the inhibitory effectiveness of 20 M concentrations of several hundred compounds against OCT2-mediated uptake of six structurally distinct substrates: MPP, metformin,,,-trimethyl-2-[methyl(7-nitrobenzo[c][1,2,5]oxadiazol-4-yl)amino]ethanaminium (NBD-MTMA), TEA, cimetidine, and 4-4-dimethylaminostyryl--methylpyridinium (ASP). Of these, MPP transport was least sensitive to inhibition. IC values for 20 structurally diverse compounds confirmed this profile, with IC values for MPP averaging 6-fold larger than those for the other substrates. Bayesian machine-learning models of ligand-induced inhibition displayed generally good statistics after cross-validation and external testing. Applying our ASP model to a previously published large-scale screening study for inhibition of OCT2-mediated ASP transport resulted in comparable statistics, with approximately 75% of "active" inhibitors predicted correctly. The differential sensitivity of MPP transport to inhibition suggests that multiple ligands can interact simultaneously with OCT2 and supports the recommendation that MPP not be used as a test substrate for OCT2 screening. Instead, metformin appears to be a comparatively representative OCT2 substrate for both in vitro and in vivo (clinical) use.

Repurposing Pyramax® for the Treatment of Ebola Virus Disease: Additivity of the Lysosomotropic Pyronaridine and Non-Lysosomotropic Artesunate

“…The Assay Central software has been previously described (23)(24)(25)(26)(27)(28)(29)(30)(31)(32) which uses the source code management system Git to gather and store structure-activity datasets collated in Molecular Notebook (Molecular Materials Informatics, Inc. in Montreal, Canada). The output is a high-quality dataset and a Bayesian model using extendedconnectivity fingerprints of maximum diameter 6 (ECFP6) descriptors.…”

Rat Organic Cation Transporter 1 Contains Three Binding Sites for Substrate 1-Methyl-4-phenylpyridinium per Monomer

“…Currently, novel molecular entities (NMEs) are tested for interaction with hOCT1 and hOCT2 expressed in epithelial cells to determine whether they inhibit uptake of a cationic model substrate that is applied at a micromolar concentration (Ahlin et al, 2008(Ahlin et al, , 2011Chen et al, 2017). This procedure has turned out to be insufficient because it was observed that the efficacy of inhibitors was dependent on the molecular structure of the employed substrate and was different when substrate concentrations far below their respective Michaelis-Menten constant (K m ) values were used for uptake measurements (Belzer et al, 2013;Thévenod et al, 2013;Yin et al, 2016;Minuesa et al, 2017;Gorboulev et al, 2018;Sandoval et al, 2018). For example, high-affinity inhibition of uptake of 1-methyl-4-phenylpyridinium 1 (MPP 1 ) by hOCT1, hOCT2, and human OCT3 (hOCT3) by the nucleoside reverse transcriptase inhibitor tenofovir disoproxil fumarate was observed when uptake was measured with 12.5 nM MPP 1 but did not show up when uptake was performed with 1 mM MPP 1 (Minuesa et al, 2009(Minuesa et al, , 2017.…”

Synergistic drug combinations and machine learning for drug repurposing in chordoma

“…Machine learning models for chordoma drug discovery. Several recently published studies of compounds screened against chordoma cell lines 20,21 were used to generate Bayesian machine learning models with our Assay Central software 10,12,[22][23][24][25][26][27][28][29] . In one published chordoma study 1097 compounds were screened against 3 chordoma cell lines (U-CH1, U-CH2, MUG-Chor1) and 27 had chordoma selective cytotoxicity 20 and many of these were EGFR inhibitors.…”

“…These datasets underwent curation to remove problematic molecules before model building as described elsewhere 10,12,[22][23][24][25][26][27][28][29] . We utilized Assay Central which has been previously described in detail 10,12,[22][23][24][25][26][27][28][29] to prepare and merge datasets collated in Molecular Notebook 60 , as well as generate Bayesian models using ECFP6 descriptors 61,62 . Briefly, the Assay Central project includes automated workflows for curating well-defined structure-activity datasets that employ a set of rules for the detection of problematic data (i.e.…”