Lack of Influence of Substrate on Ligand Interaction with the Human Multidrug and Toxin Extruder, MATE1

Abstract

Multidrug and toxin extruder (MATE) 1 plays a central role in mediating renal secretion of organic cations, a structurally diverse collection of compounds that includes ∼40% of prescribed drugs. Because inhibition of transport activity of other multidrug transporters, including the organic cation transporter (OCT) 2, is influenced by the structure of the transported substrate, the present study screened over 400 drugs as inhibitors of the MATE1-mediated transport of four structurally distinct organic cation substrates: the commonly used drugs: 1) metformin and 2) cimetidine; and two prototypic cationic substrates, 3) 1-methyl-4-phenylpyridinium (MPP), and 4) the novel fluorescent probe, N,N,N-trimethyl-2-[methyl(7-Transport was measured in Chinese hamster ovary cells that stably expressed the human ortholog of MATE1. Comparison of the resulting inhibition profiles revealed no systematic influence of substrate structure on inhibitory efficacy. Similarly, IC 50 values for 26 structurally diverse compounds revealed no significant influence of substrate structure on the kinetic interaction of inhibitor with MATE1. The IC 50 data were used to generate three-dimensional quantitative pharmacophores that identified hydrophobic regions, H-bond acceptor sites, and an ionizable (cationic) feature as key determinants for ligand binding to MATE1. In summary, in contrast to the behavior observed with some other multidrug transporters, including OCT2, the results suggest that substrate identity exerts comparatively little influence on ligand interaction with MATE1.

Impact of Direction of Transport on the Evaluation of Inhibition Potencies of Multidrug and Toxin Extrusion Protein 1 Inhibitors

“…Regarding substrate-dependency, all 12 compounds showed almost identical IC 50 values (<2.3-fold) for both substrates in uptake assays. Our results are therefore consistent with previously reported substrate-independent IC 50 values for MPP + and metformin (Lechner et al, 2016;Martinez-Guerrero et al, 2016). On the other hand, IC 50 values for MPP + showed a tendency to be greater than those for metformin in efflux assays.…”

“…MPP + and metformin were selected as probe substrates in this study because they are the most studied prototypical and/or clinical relevant organic cations for MATE assays. In addition, IC 50 values towards MATE1 using these two substrates were comparable in uptake assays in previous studies (Lechner et al, 2016;Martinez-Guerrero et al, 2016).…”

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

“…These data were used to calculate predicted IC 50 values that reflected average inhibitory interactions with OCT2, ignoring those compounds that inhibited less than 10% of transport activity (i.e., predicted IC 50 values of .180 mM). Employing methods that have been described previously (Martínez-Guerrero et al, 2016), these data were then used in an attempt to generate a pharmacophore highlighting the common structural features correlated with ligand interaction with OCT2. These efforts, however, failed to converge on a unique pharmacophore using Discovery Studio.…”

“…A total of 480 compounds were used for MPP, TEA, NBD-MTMA, and metformin; 400 compounds were used for cimetidine and ASP. Each compound was diluted to a concentration of 20 mM, pH 7.4, to a final concentration of 2% DMSO using a VIAFLO Multichannel Electronic Pipette (Integra Biosciences Corp., Hudson, NH) (Martínez-Guerrero et al, 2016).…”

Multiple Computational Approaches for Predicting Drug Interactions with Human Equilibrative Nucleoside Transporter 1

“…There have been numerous efforts to use computational approaches to predict drug interactions with transporters such as pharmacophores, quantitative structure activity relationships (QSAR), machine learning models and docking in crystal structures or homology models (e.g., (Ekins et al, 2012;Ekins et al, 2015). For example, we have published several recent examples using in vitro data to generate Bayesian machine learning models that can in turn be used to score libraries of compounds and predict additional compounds (Martinez-Guerrero et al, 2016;Sandoval et al, 2018;Miller et al, 2021). These Bayesian models have been useful in identifying important molecular features in the training sets and such models can be applied to the drug discovery and development process to identify valuable information on favorable and unfavorable drug-transporter interactions before additional in vitro and in vivo studies are conducted.…”