Opportunities and challenges in phenotypic drug discovery: An industry perspective
Nature Reviews Drug Discovery, ISSN: 1474-1784, Vol: 16, Issue: 8, Page: 531-543
2017
- 675Citations
- 1,312Captures
- 6Mentions
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Metrics Details
- Citations675
- Citation Indexes671
- CrossRef671
- 635
- Patent Family Citations2
- Patent Families2
- Policy Citations2
- Policy Citation2
- Captures1,312
- Readers1,312
- 1,309
- Mentions6
- References3
- Wikipedia3
- News Mentions2
- News2
- Blog Mentions1
- Blog1
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Nature Reviews Drug Discovery contents August 2017 Volume 16 Number 8 pp 513-586
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Review Description
Phenotypic drug discovery (PDD) approaches do not rely on knowledge of the identity of a specific drug target or a hypothesis about its role in disease, in contrast to the target-based strategies that have been widely used in the pharmaceutical industry in the past three decades. However, in recent years, there has been a resurgence in interest in PDD approaches based on their potential to address the incompletely understood complexity of diseases and their promise of delivering first-in-class drugs, as well as major advances in the tools for cell-based phenotypic screening. Nevertheless, PDD approaches also have considerable challenges, such as hit validation and target deconvolution. This article focuses on the lessons learned by researchers engaged in PDD in the pharmaceutical industry and considers the impact of 'omics' knowledge in defining a cellular disease phenotype in the era of precision medicine, introducing the concept of a chain of translatability. We particularly aim to identify features and areas in which PDD can best deliver value to drug discovery portfolios and can contribute to the identification and the development of novel medicines, and to illustrate the challenges and uncertainties that are associated with PDD in order to help set realistic expectations with regard to its benefits and costs.
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Springer Science and Business Media LLC
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