Bayesian models leveraging bioactivity and cytotoxicity information for drug discovery

Academic Article

Abstract

  • Identification of unique leads represents a significant challenge in drug discovery. This hurdle is magnified in neglected diseases such as tuberculosis. We have leveraged public high-throughput screening (HTS) data to experimentally validate a virtual screening approach employing Bayesian models built with bioactivity information (single-event model) as well as bioactivity and cytotoxicity information (dual-event model). We virtually screened a commercial library and experimentally confirmed actives with hit rates exceeding typical HTS results by one to two orders of magnitude. This initial dual-event Bayesian model identified compounds with antitubercular whole-cell activity and low mammalian cell cytotoxicity from a published set of antimalarials. The most potent hit exhibits the in vitro activity and in vitro/in vivo safety profile of a drug lead. These Bayesian models offer significant economies in time and cost to drug discovery. © 2013 Elsevier Ltd.
  • Digital Object Identifier (doi)

    Author List

  • Ekins S; Reynolds RC; Kim H; Koo MS; Ekonomidis M; Talaue M; Paget SD; Woolhiser LK; Lenaerts AJ; Bunin BA
  • Start Page

  • 370
  • End Page

  • 378
  • Volume

  • 20
  • Issue

  • 3