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Harnessing Omics Sciences, Population Databases, and Open Innovation Models for Theranostics‐Guided Drug Discovery and Development
Author(s) -
Dove Edward S.,
Özdemir Vural,
Joly Yann
Publication year - 2012
Publication title -
drug development research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.582
H-Index - 60
eISSN - 1098-2299
pISSN - 0272-4391
DOI - 10.1002/ddr.21035
Subject(s) - drug discovery , omics , drug , population , medicine , pharmacology , data science , computer science , computational biology , bioinformatics , biology , environmental health
Preclinical ResearchOmics science‐driven population databases and biobanks help in enabling robust, large‐scale, high‐throughput biomarker discovery and validation. As targeted drug therapies will require the development of companion diagnostic tests to identify patients most suitable for a given drug therapy, databases and biobanks represent one of the optimal and rapidly emerging ways to enable personalized medicine with reduced development timelines. Moreover, data‐intensive omics technologies represent a new dual reconfiguration of 21st‐century science whereby communitarian value‐driven “infrastructure science” and individual entrepreneurship‐driven “discovery science” now coexist. In the hope of overcoming the “transfer problem” in omics research that continues to hinder the full realization of concrete applications for human health, biobanks and databases are increasingly harnessing various open innovation models, such as open access, open source, expert sourcing, and patent pools. These models appear at various stages (drug repurposing, upstream, and downstream) of the research and development ( R&D ) process. While laudable, their inclusion will likely spur a variety of ethical, legal, and social issues ( ELSI ), including those revolving around consent, privacy, and property. By collectively anticipating and analyzing these issues, tensions among these innovation models and extant laws and policies regulating biomedical research and therapeutics based on the classical discovery science model can be resolved. This article does not posit which models will work best to achieve drug discovery and development breakthroughs, but rather, advocates for evidence‐based analyses that couple technical and economic data with global ELSI research to foster a more nuanced, contextualized, and thorough understanding of the new dual configuration of postgenomics pharmaceutical R&D .