Integrating pharmacogenomics and cheminformatics with diverse disease phenotypes for cell type-guided drug discovery

Abstract

Large-scale pharmacogenomic resources, such as the Connectivity Map (CMap), have greatly assisted computational drug discovery. However, despite their widespread use, CMap-based methods have thus far been agnostic to the biological activity of drugs, as well as to the genomic effects of drugs in multiple disease contexts. Here, we present a network-based statistical approach, Pathopticon, that uses CMap to build cell type-specific gene-drug perturbation networks and integrates these networks with cheminformatic data and diverse disease phenotypes for a nested prioritization of cell lines and drugs. Pathopticon demonstrates a better prediction performance than solely cheminformatic measures and state-of-the-art methods that rely exclusively on pharmacogenomic data. Top predictions made by Pathopticon have high chemical structural diversity, suggesting their potential for building compound libraries. In a proof-of-concept application on vein graft disease, we validate the predicted drugs in vitro and demonstrate that Pathopticon helps pinpoint the shared intermediate phenotypes targeted by each prediction. Overall, our analytical framework integrating pharmacogenomics and cheminformatics provides a feasible blueprint for a cell typespecific drug discovery and repositioning platform with broad implications for the efficiency and success of drug development.