title: Bayesian models leveraging bioactivity and cytotoxicity information for drug discovery.
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.
authors.affiliation: Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA 94010, USA. ekinssean@yahoo.com
authors.forename: Sean
authors.initials: S
authors.lastname: Ekins
authors.forename: Robert C
authors.initials: RC
authors.lastname: Reynolds
authors.forename: Hiyun
authors.initials: H
authors.lastname: Kim
authors.forename: Mi-Sun
authors.initials: MS
authors.lastname: Koo
authors.forename: Marilyn
authors.initials: M
authors.lastname: Ekonomidis
authors.forename: Meliza
authors.initials: M
authors.lastname: Talaue
authors.forename: Steve D
authors.initials: SD
authors.lastname: Paget
authors.forename: Lisa K
authors.initials: LK
authors.lastname: Woolhiser
authors.forename: Anne J
authors.initials: AJ
authors.lastname: Lenaerts
authors.forename: Barry A
authors.initials: BA
authors.lastname: Bunin
authors.forename: Nancy
authors.initials: N
authors.lastname: Connell
authors.forename: Joel S
authors.initials: JS
authors.lastname: Freundlich
mesh_terms: D000818:Animals; D000995:Antitubercular Agents / Q000494:pharmacology* / Q000633:toxicity*; D001499:Bayes Theorem; D002522:Chlorocebus aethiops; D055808:Drug Discovery; D004353:Drug Evaluation, Preclinical; D005260:Female; D020128:Inhibitory Concentration 50; D008264:Macrophages / Q000187:drug effects; D051379:Mice; D009169:Mycobacterium tuberculosis / Q000187:drug effects; D014709:Vero Cells
chemical_list: D000995:Antitubercular Agents