Wang Y, Cornett A, King FJ, Mao Y, Nigsch F, Paris CG, McAllister G, Jenkins JL. Evidence-Based and Quantitative Prioritization of Tool Compounds in Phenotypic Drug Discovery.
Cell Chem Biol 2016;
23:862-874. [PMID:
27427232 DOI:
10.1016/j.chembiol.2016.05.016]
[Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2015] [Revised: 04/29/2016] [Accepted: 05/13/2016] [Indexed: 01/07/2023]
Abstract
The use of potent and selective chemical tools with well-defined targets can help elucidate biological processes driving phenotypes in phenotypic screens. However, identification of selective compounds en masse to create targeted screening sets is non-trivial. A systematic approach is needed to prioritize probes, which prevents the repeated use of published but unselective compounds. Here we performed a meta-analysis of integrated large-scale, heterogeneous bioactivity data to create an evidence-based, quantitative metric to systematically rank tool compounds for targets. Our tool score (TS) was then tested on hundreds of compounds by assessing their activity profiles in a panel of 41 cell-based pathway assays. We demonstrate that high-TS tools show more reliably selective phenotypic profiles than lower-TS compounds. Additionally we highlight frequently tested compounds that are non-selective tools and distinguish target family polypharmacology from cross-family promiscuity. TS can therefore be used to prioritize compounds from heterogeneous databases for phenotypic screening.
Collapse