Literature DB >> 27620174

Enabling Anyone to Translate Clinically Relevant Ideas to Therapies.

Sean Ekins1,2, Natalie Diaz3,4,5, Julia Chung6,7,8, Paul Mathews3,4, Aaron McMurtray3,4,5.   

Abstract

How do we inspire new ideas that could lead to potential treatments for rare or neglected diseases, and allow for serendipity that could help to catalyze them? How many potentially good ideas are lost because they are never tested? What if those ideas could have lead to new therapeutic approaches and major healthcare advances? If a clinician or anyone for that matter, has a new idea they want to test to develop a molecule or therapeutic that they could translate to the clinic, how would they do it without a laboratory or funding? These are not idle theoretical questions but addressing them could have potentially huge economic implications for nations. If we fail to capture the diversity of ideas and test them we may also lose out on the next blockbuster treatments. Many of those involved in the process of ideation may be discouraged and simply not know where to go. We try to address these questions and describe how there are options to raising funding, how even small scale investments can foster preclinical or clinical translation, and how there are several approaches to outsourcing the experiments, whether to collaborators or commercial enterprises. While these are not new or far from complete solutions, they are first steps that can be taken by virtually anyone while we work on other solutions to build a more concrete structure for the "idea-hypothesis testing-proof of concept-translation-breakthrough pathway".

Keywords:  drug discovery; ideas; proof of concept; serendipity

Mesh:

Year:  2016        PMID: 27620174     DOI: 10.1007/s11095-016-2039-5

Source DB:  PubMed          Journal:  Pharm Res        ISSN: 0724-8741            Impact factor:   4.200


  29 in total

Review 1.  How to improve R&D productivity: the pharmaceutical industry's grand challenge.

Authors:  Steven M Paul; Daniel S Mytelka; Christopher T Dunwiddie; Charles C Persinger; Bernard H Munos; Stacy R Lindborg; Aaron L Schacht
Journal:  Nat Rev Drug Discov       Date:  2010-02-19       Impact factor: 84.694

2.  Epidemiologic Modeling of HIV/AIDS: Use of Computational Models to Study the Population Dynamics of the Disease to Assess Effective Intervention Strategies for Decision-making.

Authors:  T Habtemariam; B Tameru; D Nganwa; G Beyene; L Ayanwale; V Robnett
Journal:  Adv Syst Sci Appl       Date:  2008-03

3.  Four disruptive strategies for removing drug discovery bottlenecks.

Authors:  Sean Ekins; Chris L Waller; Mary P Bradley; Alex M Clark; Antony J Williams
Journal:  Drug Discov Today       Date:  2012-10-23       Impact factor: 7.851

Review 4.  An end to the myth: there is no drug development pipeline.

Authors:  Kristin Baxter; Elizabeth Horn; Neely Gal-Edd; Kristi Zonno; James O'Leary; Patrick F Terry; Sharon F Terry
Journal:  Sci Transl Med       Date:  2013-02-06       Impact factor: 17.956

Review 5.  Mathematical and computational models of the retina in health, development and disease.

Authors:  Paul A Roberts; Eamonn A Gaffney; Philip J Luthert; Alexander J E Foss; Helen M Byrne
Journal:  Prog Retin Eye Res       Date:  2016-04-07       Impact factor: 21.198

6.  High-Content Analysis of Breast Cancer Using Single-Cell Deep Transfer Learning.

Authors:  Chetak Kandaswamy; Luís M Silva; Luís A Alexandre; Jorge M Santos
Journal:  J Biomol Screen       Date:  2016-01-08

7.  Computational models of heart disease.

Authors:  Nic Smith; Natalia Trayanova
Journal:  Drug Discov Today Dis Models       Date:  2014

8.  Computational Models Describing Possible Mechanisms for Generation of Excessive Beta Oscillations in Parkinson's Disease.

Authors:  Alex Pavlides; S John Hogan; Rafal Bogacz
Journal:  PLoS Comput Biol       Date:  2015-12-18       Impact factor: 4.475

9.  Tools for Citizen-Science Recruitment and Student Engagement in Your Research and in Your Classroom.

Authors:  Sarah E Council; Julie E Horvath
Journal:  J Microbiol Biol Educ       Date:  2016-03-01

10.  A Guide to Scientific Crowdfunding.

Authors:  Julien Vachelard; Thaise Gambarra-Soares; Gabriela Augustini; Pablo Riul; Vinicius Maracaja-Coutinho
Journal:  PLoS Biol       Date:  2016-02-17       Impact factor: 8.029

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  1 in total

1.  The Natural Product Eugenol Is an Inhibitor of the Ebola Virus In Vitro.

Authors:  Thomas Lane; Manu Anantpadma; Joel S Freundlich; Robert A Davey; Peter B Madrid; Sean Ekins
Journal:  Pharm Res       Date:  2019-05-17       Impact factor: 4.200

  1 in total

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