Literature DB >> 23098820

Four disruptive strategies for removing drug discovery bottlenecks.

Sean Ekins1, Chris L Waller, Mary P Bradley, Alex M Clark, Antony J Williams.   

Abstract

Drug discovery is shifting focus from industry to outside partners and, in the process, creating new bottlenecks. Technologies like high throughput screening (HTS) have moved to a larger number of academic and institutional laboratories in the USA, with little coordination or consideration of the outputs and creating a translational gap. Although there have been collaborative public-private partnerships in Europe to share pharmaceutical data, the USA has seemingly lagged behind and this may hold it back. Sharing precompetitive data and models may accelerate discovery across the board, while finding the best collaborators, mining social media and mobile approaches to open drug discovery should be evaluated in our efforts to remove drug discovery bottlenecks. We describe four strategies to rectify the current unsustainable situation.
Copyright © 2012 Elsevier Ltd. All rights reserved.

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Year:  2012        PMID: 23098820     DOI: 10.1016/j.drudis.2012.10.007

Source DB:  PubMed          Journal:  Drug Discov Today        ISSN: 1359-6446            Impact factor:   7.851


  8 in total

1.  Improving data and knowledge management to better integrate health care and research.

Authors:  M Cases; L I Furlong; J Albanell; R B Altman; R Bellazzi; S Boyer; A Brand; A J Brookes; S Brunak; T W Clark; J Gea; P Ghazal; N Graf; R Guigó; T E Klein; N López-Bigas; V Maojo; B Mons; M Musen; J L Oliveira; A Rowe; P Ruch; A Shabo; E H Shortliffe; A Valencia; J van der Lei; M A Mayer; F Sanz
Journal:  J Intern Med       Date:  2013-07-15       Impact factor: 8.989

2.  Enabling Anyone to Translate Clinically Relevant Ideas to Therapies.

Authors:  Sean Ekins; Natalie Diaz; Julia Chung; Paul Mathews; Aaron McMurtray
Journal:  Pharm Res       Date:  2016-09-12       Impact factor: 4.200

Review 3.  Collaborative drug discovery for More Medicines for Tuberculosis (MM4TB).

Authors:  Sean Ekins; Anna Coulon Spektor; Alex M Clark; Krishna Dole; Barry A Bunin
Journal:  Drug Discov Today       Date:  2016-11-22       Impact factor: 7.851

4.  Data Mining and Computational Modeling of High-Throughput Screening Datasets.

Authors:  Sean Ekins; Alex M Clark; Krishna Dole; Kellan Gregory; Andrew M Mcnutt; Anna Coulon Spektor; Charlie Weatherall; Nadia K Litterman; Barry A Bunin
Journal:  Methods Mol Biol       Date:  2018

Review 5.  Innovation in academic chemical screening: filling the gaps in chemical biology.

Authors:  Samuel A Hasson; James Inglese
Journal:  Curr Opin Chem Biol       Date:  2013-05-14       Impact factor: 8.822

6.  Bigger data, collaborative tools and the future of predictive drug discovery.

Authors:  Sean Ekins; Alex M Clark; S Joshua Swamidass; Nadia Litterman; Antony J Williams
Journal:  J Comput Aided Mol Des       Date:  2014-06-19       Impact factor: 3.686

Review 7.  A brief review of recent Charcot-Marie-Tooth research and priorities.

Authors:  Sean Ekins; Nadia K Litterman; Renée J G Arnold; Robert W Burgess; Joel S Freundlich; Steven J Gray; Joseph J Higgins; Brett Langley; Dianna E Willis; Lucia Notterpek; David Pleasure; Michael W Sereda; Allison Moore
Journal:  F1000Res       Date:  2015-02-26

Review 8.  Changing R&D models in research-based pharmaceutical companies.

Authors:  Alexander Schuhmacher; Oliver Gassmann; Markus Hinder
Journal:  J Transl Med       Date:  2016-04-27       Impact factor: 5.531

  8 in total

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