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Release of 50 new, drug-like compounds and their computational target predictions for open source anti-tubercular drug discovery.

ABSTRACT: As a follow up to the antimycobacterial screening exercise and the release of GSK´s first Tres Cantos Antimycobacterial Set (TCAMS-TB), this paper presents the results of a second antitubercular screening effort of two hundred and fifty thousand compounds recently added to the GSK collection. The compounds were further prioritized based on not only antitubercular potency but also on physicochemical characteristics. The 50 most attractive compounds were then progressed for evaluation in three different predictive computational biology algorithms based on structural similarity or GSK historical biological assay data in order to determine their possible mechanisms of action. This effort has resulted in the identification of novel compounds and their hypothesized targets that will hopefully fuel future TB drug discovery and target validation programs alike.

SUBMITTER: Rebollo-Lopez MJ

PROVIDER: S-EPMC4671658 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Release of 50 new, drug-like compounds and their computational target predictions for open source anti-tubercular drug discovery.

Rebollo-Lopez María Jose MJ Lelièvre Joël J Alvarez-Gomez Daniel D Castro-Pichel Julia J Martínez-Jiménez Francisco F Papadatos George G Kumar Vinod V Colmenarejo Gonzalo G Mugumbate Grace G Hurle Mark M Barroso Vanessa V Young Rob J RJ Martinez-Hoyos María M González del Río Rubén R Bates Robert H RH Lopez-Roman Eva Maria EM Mendoza-Losana Alfonso A Brown James R JR Alvarez-Ruiz Emilio E Marti-Renom Marc A MA Overington John P JP Cammack Nicholas N Ballell Lluís L Barros-Aguire David D

PloS one 20151207 12

As a follow up to the antimycobacterial screening exercise and the release of GSK´s first Tres Cantos Antimycobacterial Set (TCAMS-TB), this paper presents the results of a second antitubercular screening effort of two hundred and fifty thousand compounds recently added to the GSK collection. The compounds were further prioritized based on not only antitubercular potency but also on physicochemical characteristics. The 50 most attractive compounds were then progressed for evaluation in three dif ...[more]

PMID: 26642067

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Project description:BackgroundOpen source drug discovery offers potential for developing new and inexpensive drugs to combat diseases that disproportionally affect the poor. The concept borrows two principle aspects from open source computing (i.e., collaboration and open access) and applies them to pharmaceutical innovation. By opening a project to external contributors, its research capacity may increase significantly. To date there are only a handful of open source R&D projects focusing on neglected diseases. We wanted to learn from these first movers, their successes and failures, in order to generate a better understanding of how a much-discussed theoretical concept works in practice and may be implemented.Methodology/principal findingsA descriptive case study was performed, evaluating two specific R&D projects focused on neglected diseases. CSIR Team India Consortium's Open Source Drug Discovery project (CSIR OSDD) and The Synaptic Leap's Schistosomiasis project (TSLS). Data were gathered from four sources: interviews of participating members (n = 14), a survey of potential members (n = 61), an analysis of the websites and a literature review. Both cases have made significant achievements; however, they have done so in very different ways. CSIR OSDD encourages international collaboration, but its process facilitates contributions from mostly Indian researchers and students. Its processes are formal with each task being reviewed by a mentor (almost always offline) before a result is made public. TSLS, on the other hand, has attracted contributors internationally, albeit significantly fewer than CSIR OSDD. Both have obtained funding used to pay for access to facilities, physical resources and, at times, labor costs. TSLS releases its results into the public domain, whereas CSIR OSDD asserts ownership over its results.Conclusions/significanceTechnically TSLS is an open source project, whereas CSIR OSDD is a crowdsourced project. However, both have enabled high quality research at low cost. The critical success factors appear to be clearly defined entry points, transparency and funding to cover core material costs.

Dataset Information

Release of 50 new, drug-like compounds and their computational target predictions for open source anti-tubercular drug discovery.

Publications

Release of 50 new, drug-like compounds and their computational target predictions for open source anti-tubercular drug discovery.

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