Discovery of Potent, Selective Multidrug and Toxin Extrusion Transporter 1 (MATE1, SLC47A1) Inhibitors Through Prescription Drug Profiling and Computational Modeling

Abstract

The human multidrug and toxin extrusion (MATE) transporter 1 contributes to the tissue distribution and excretion of many drugs. Inhibition of MATE1 may result in potential drug-drug interactions (DDIs) and alterations in drug exposure and accumulation in various tissues. The primary goals of this project were to identify MATE1 inhibitors with clinical importance or in vitro utility and to elucidate the physicochemical properties that differ between MATE1 and OCT2 inhibitors. Using a fluorescence assay of ASP+ uptake in cells stably expressing MATE1, over 900 prescription drugs were screened and 84 potential MATE1 inhibitors were found. We identified several MATE1 selective inhibitors including four FDA-approved medications that may be clinically relevant MATE1 inhibitors and could cause a clinical DDI. In parallel, a QSAR model identified distinct molecular properties of MATE1 versus OCT2 inhibitors and was used to screen the DrugBank in silico library for new hits in a larger chemical space.

Making Transporter Models for Drug–Drug Interaction Prediction Mobile

“…We have described the use of seven data sets representing six transporters of interest (MATE1, MATE2K, OCT2, OCTN2, ASBT, and NTCP) due to their potential involvement in drug-drug interactions (Diao et al, 2009Zheng et al, 2009;Kido et al, 2011;Astorga et al, 2012;Ekins et al, 2012b;Greupink et al, 2012;Dong et al, 2013Dong et al, , 2014Sedykh et al, 2013;Wittwer et al, 2013;Xu et al, 2013). We focused on Bayesian models with ECFP6 and FCFP6 descriptors because these were previously used to develop models that were successfully validated using prospective testing.…”

“…We previously published several transporter models and described Bayesian models generated using Discovery Studio (Biovia, San Diego, CA) for MATE1, MATE2K, OCTN2, ASBT, and NTCP (Diao et al, 2009Zheng et al, 2010;Astorga et al, 2012;Dong et al, 2013Dong et al, , 2014. We have now analyzed several larger published data sets from other groups for MATE1 (Wittwer et al, 2013) and OCT2 (Kido et al, 2011), which we have also used to generate Bayesian models with Discovery Studio to compare the different fingerprints. To illustrate the utility of transporter models built with open ECFP6 descriptors and the Bayesian algorithm, 5-fold cross-validation and leave-one-out validation were used.…”

“…Receiver operator curve (ROC) values were produced, in which a value of 1 is ideal and a value greater than 0.7 is considered good. Cutoffs for actives and inactives were as previously described (Diao et al, 2009Zheng et al, 2010;Kido et al, 2011;Astorga et al, 2012;Dong et al, 2013Dong et al, , 2014Wittwer et al, 2013). Open transporter models were developed using open-source software (Clark et al, 2014 and loaded into the MMDS app (http://molmatinf.com/) (Fig.…”

“…We are increasingly seeing medium-or high-throughput screens used to develop ligand-based models for individual transporters (Diao et al, 2009Zheng et al, 2009;Kido et al, 2011;Astorga et al, 2012;Ekins et al, 2012b;Greupink et al, 2012;Dong et al, 2013Dong et al, , 2014Sedykh et al, 2013;Wittwer et al, 2013;Xu et al, 2013). One of the significant limitations of this is that the models developed are rarely accessible outside of the research group developing them, likely because of the commercial software required.…”

In silicoADME/T modelling for rational drug design

“…Additionally, it has been suggested that different pharmacophore hypotheses in the CP model may correspond to different inhibitory mechanisms, which can explain the structural diversity of OCT2 inhibitors. Subsequently, Giacomini's team published their research on another important emerging renal transporter, MATE1, and found 84 inhibitors among 900 prescription drugs (Wittwer et al 2013). These authors developed an RF model with an average AUC value of 0·78.…”