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

  • Philip J. Sandoval, Kimberley M. Zorn, Alex M. Clark, Sean Ekins, Stephen H. Wright
  • Molecular Pharmacology, June 2018, American Society for Pharmacology & Experimental Therapeutics (ASPET)
  • DOI: 10.1124/mol.117.111443

Organic Cation Transporter 2 machine learning models

What is it about?

We generated inhibition data for six structurally distinct substrates: MPP, metformin, N,N,N-trimethyl-2-[methyl(7-nitrobenzo[c][1,2,5]oxadiazol-4-yl)amino]ethanaminium (NBD-MTMA), TEA, cimetidine, and 4-4-dimethylaminostyryl-N-methylpyridinium (ASP). These were then used to generate Bayesian machine learning models with different tools (Discovery Studio and Assay Central). These models were used to predict a test set. We showed metformin appears the best substrate for use in vivo and in vitro.

Why is it important?

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). It was not clear if substrate dependence of a ligand interaction was common among OCT2 substrates.


Dr Sean Ekins
Collaborations in Chemistry

This is the latest publication in a decade plus collaboration that has modeled different OCT and MATE datasets.

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The following have contributed to this page: Dr Sean Ekins