Chemoinformatics and Machine Learning to Understand Drug Transporter Selectivity | Dr. Sanjay Nigam

Drug transporters interact with a wide range of pharmaceuticals, toxins, metabolites, signal molecules and gut microbiome-derived small molecules. This raises concerns not only about drug-drug interactions but also drug-metabolite and drug-nutrient interactions, among others. Chemoinformatics combined with machine learning methods are useful for identifying molecular properties of small molecules favored by a particular SLC or ABC transporter. This is one several computational strategies that can lead to a clearer understanding of potential interactions between drugs, toxins, metabolites, nutrients, and gut microbe products. It can also lead to a better understanding of transporter-mediated interorgan and interorganismal small molecule communication in normal and pathophysiological states, as proposed in the Remote Sensing and Signaling Theory. This theory focuses on the underlying biological pathways that make drug ADME possible and can serve as a basis for more efficient targeting of drugs to tissues in health and disease (Nigam SK. What Do Drug Transporters Really Do? Nature Rev. Drug Disc. 2015).

Sanjay Nigam

Chemoinformatics and Machine Learning to Understand Drug Transporter Selectivity

Sanjay K. Nigam, MD, is the Nancy Kaehr Chair in Research and a Distinguished Professor of Pediatrics and Medicine at the University of California San Diego. His group discovered the transporter now called OAT1 (originally NKT) as well many other SLC22 transporters. By performing omics analyses of knockout mice, his lab has identified a large number of endogenous ligands (signaling molecules and metabolites) for OAT1, OAT3 and other transporters. The lab has used a variety of systems biology and machine learning methods to understand the biological role of drug transporters. This has led to the formulation of the Remote Sensing and Signaling Theory--which proposes a central role for drug transporters in inter-organ (gut-liver-kidney) and inter-organismal (gut microbes-host) communication by endogenous small molecules. The theory appears particularly applicable to understanding drug-metabolite interactions, chronic kidney disease, and uric acid disorders.