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Predictive validity in drug discovery: what it is, why it matters and how to improve it

Abstract

Successful drug discovery is like finding oases of safety and efficacy in chemical and biological deserts. Screens in disease models, and other decision tools used in drug research and development (R&D), point towards oases when they score therapeutic candidates in a way that correlates with clinical utility in humans. Otherwise, they probably lead in the wrong direction. This line of thought can be quantified by using decision theory, in which ‘predictive validity’ is the correlation coefficient between the output of a decision tool and clinical utility across therapeutic candidates. Analyses based on this approach reveal that the detectability of good candidates is extremely sensitive to predictive validity, because the deserts are big and oases small. Both history and decision theory suggest that predictive validity is under-managed in drug R&D, not least because it is so hard to measure before projects succeed or fail later in the process. This article explains the influence of predictive validity on R&D productivity and discusses methods to evaluate and improve it, with the aim of supporting the application of more effective decision tools and catalysing investment in their creation.

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Fig. 1: Pharmaceutical R&D depends on selections made using a set of decision tools.
Fig. 2: Predictive validity, throughput and research and development decision performance.
Fig. 3: R&D productivity is very sensitive to predictive validity.
Fig. 4: Financial value of decision tool evaluation in pharmaceutical R&D.

Code availability

The Mathematica code used to generate the decision-theoretical analyses shown in Figs. 2 and 3 is available in Supplementary Box 4.

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Acknowledgements

The authors thank A. Morton and A. Colson at the Department of Management Science at Strathclyde University for their help on the decision tool evaluation section; B. Versaevel and E. Billet de Villemeur, at EMLYON Business School and at Université des Sciences et Technologies de Lille, respectively, for their help on the economics of decision tool-related innovation; and L. Ewart, EVP Science at Emulate, for her help with toxicology-related content. They thank M. Todd at the UCL School of Pharmacy for helpful comments on an earlier draft of the paper. Finally, they thank the reviewers, whose thoughtful comments substantially improved the paper.

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Correspondence to Jack W. Scannell.

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J.W.S. is a director and shareholder of JW Scannell Analytics, which sells consulting services to the biopharmaceutical and financial services sectors, including to firms that are commercializing screening and disease models; is CEO of Unify Pharmaceuticals Corp; is Director of Etheros Pharmaceuticals Corp; and holds equity options in Ochre Bio. J.B. is an employee of Novadiscovery and holds equity options in the firm. G.R.D. is a director and major shareholder of P1vital and P1vital Products Ltd. P1vital provides clinical research services to the pharmaceutical industry. P1vital Products provides data management and digital technology to the pharmaceutical industry and health-care providers. H.T. is employed by AiCuris AG, owns shares in Bayer AG, Evotec SE, Cyclerion Therapeutics, Inc. and recently incorporated www.knowledge-house.com. G.S.F. has received fees from Merck KGaA and Curare Consulting; has been employed by the Access to Medicine Foundation, 3D PharmXchange and Janssen Biologics; and is now employed by GlaxoSmithKline. D.R. is a consultant to OMASS Therapeutics Ltd and is on the scientific advisory board of Avicenna Biosciences, Inc. J.M.T. is a director and/or shareholder of Talisman Therapeutics, Gen2 Neuroscience, Avilex Pharma, Domain Therapeutics, Cellesce, Ubiquigent and Monument Therapeutics. J.A.H. declares no competing interests.

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Nature Reviews Drug Discovery thanks Neil Carragher, Matthew Nelson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Scannell, J.W., Bosley, J., Hickman, J.A. et al. Predictive validity in drug discovery: what it is, why it matters and how to improve it. Nat Rev Drug Discov 21, 915–931 (2022). https://doi.org/10.1038/s41573-022-00552-x

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