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Harnessing Omics Sciences, Population Databases, and Open Innovation Models for Theranostics-Guided Drug Discovery and Development

Authors

  • Edward S. Dove,

    1. Centre of Genomics and Policy, Department of Human Genetics, Faculty of Medicine, McGill University, Montreal, Canada
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  • Vural Özdemir,

    1. Centre of Genomics and Policy, Department of Human Genetics, Faculty of Medicine, McGill University, Montreal, Canada
    2. Group on Complex Collaboration, Desautels Faculty of Management, McGill University, Montreal, Canada
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  • Yann Joly

    Corresponding author
    • Centre of Genomics and Policy, Department of Human Genetics, Faculty of Medicine, McGill University, Montreal, Canada
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  • Funding Support: Canadian Institutes of Health Research; Fonds de recherche du Québec—Santé; Ministère du Développement économique, de l'Innovation et de l'Exportation (Quebec).

Correspondence to: Yann Joly, Centre of Genomics and Policy, Department of Human Genetics, Faculty of Medicine, McGill University, 740 Dr. Penfield Avenue, Montreal, QC H3A 0G4, Canada.

E-mail: yann.joly@mail.mcgill.ca

Abstract

Preclinical Research

Omics 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.

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