Improving the translation of disease biology though phenomics
Posted: 20 July 2018 | Paul Andrews (National Screening Centre - Scientific Lead - Dundee Laboratory) | No comments yet
How times change. Up to 15 years ago, you would be hard-pressed to find a drug discovery conference with a track dedicated to phenotypic approaches. The underpinning science was called high-content imaging or analysis, being mainly confined to academic drug discovery labs and a handful of pioneers in industry. but overall this was given little attention and often dismissed by the chemistry-dominated leadership in the industry…
Today any self-respecting pharma-focused conference agenda isn’t complete without at least one session on the use of more physiological phenotypic assays for drug discovery. Now it’s all about 3D models and multi-parametric data, iPSC models and patient-centric approaches.
The driver for this shift was a dawning of realisation, triggered by the much-cited 2011 review by David Swinney and John Anthony entitled “How were new medicines made”.1 The title was subtly innocent but the conclusions were full of impact. Retrospective analysis of FDA-approvals showed that more empirical, phenotypic, approaches where very good at finding first-in-class drugs, especially given the level of investment in this area. However, this new era of enlightenment we are now experiencing was most-likely precipitated by the stark impact of economic failure, rather anything else. There was (and probably still is) a profound and persistent inefficiency in the drug discovery and development process – there is a greater than 90% attrition rate, mainly due to lack of efficacy and safety, leading to extraordinarily high costs and an ever-decreasing return on investment, compounded the extended timelines between initial concept and drug approval. A major contributing factor in this failure is that in the rush to drug huge numbers of biochemical targets – borne from the availability of recombinant DNA technology and eventually whole genome sequences – the need for accurate target validation and predictive in vitro cellular models, was overlooked.
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