Complementary Dual Approach for In Silico Target Identification of Potential Pharmaceutical Compounds in Cystic Fibrosis

Cystic fibrosis is a genetic disease caused by mutation of the CFTR gene, which encodes a chloride and bicarbonate transporter in epithelial cells. Due to the vast range of geno- and phenotypes, it is difficult to find causative treatments; however, small-molecule therapeutics have been clinically approved in the last decade. Still, the search for novel therapeutics is ongoing, and thousands of compounds are being tested in different assays, often leaving their mechanism of action unknown. Here, we bring together a CFTR-specific compound database (CandActCFTR) and systems biology model (CFTR Lifecycle Map) to identify the targets of the most promising compounds. We use a dual inverse screening approach, where we employ target- and ligand-based methods to suggest targets of 309 active compounds in the database amongst 90 protein targets from the systems biology model. Overall, we identified 1038 potential target-compound pairings and were able to suggest targets for all 309 active compounds in the database.

Automated summary

This study used a compound database and a systems biology model to identify targets for the treatment of cystic fibrosis. Through inverse screening methods, 1038 potential target-compound pairings were found.