TY - JOUR
KW - Drug development
KW - Experimental models of disease
KW - High-throughput screening
KW - Preclinical research
KW - Target validation
AU - Jack W. Scannell
AU - James Bosley
AU - John A. Hickman
AU - Gerard R. Dawson
AU - Hubert Truebel
AU - Guilherme S. Ferreira
AU - Duncan Richards
AU - J. Mark Treherne
AB - 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.
BT - Nature Reviews Drug Discovery
DA - 2022-12
DO - 10.1038/s41573-022-00552-x
IS - 12
LA - en
N2 - 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.
PY - 2022
SP - 915
EP - 931
ST - Predictive validity in drug discovery
T2 - Nature Reviews Drug Discovery
TI - Predictive validity in drug discovery: what it is, why it matters and how to improve it
UR - https://www.nature.com/articles/s41573-022-00552-x
VL - 21
Y2 - 2024-04-17
SN - 1474-1784
ER -