TY - JOUR
KW - drug safety
KW - Preclinical research
AU - Christopher E. Goldring
AU - Giusy Russomanno
AU - Carmen Pin
AU - Panuwat Trairatphisan
AU - Kylie A. Beattie
AU - Ciarán P. Fisher
AU - Janet Piñero
AU - Richard J. Brennan
AU - Diana Clausznitzer
AU - Ian M. Copple
AU - Theo M. de Kok
AU - Carrie A. Duckworth
AU - Laura I. Furlong
AU - Barbara Füzi
AU - Attila Gabor
AU - Louis Gall
AU - Jan Hengstler
AU - Henning Hermjakob
AU - Fiona Hunter
AU - Danyel Jennen
AU - Mikko Koskinen
AU - Steven J. Kunnen
AU - Lieve Lammens
AU - Sebastian Lobentanzer
AU - Marcel Mohr
AU - Elisa Passini
AU - D. Mark Pritchard
AU - Rahuman S. Malik-Sheriff
AU - Blanca Rodriguez
AU - Eric I. Rossman
AU - Julio Saez-Rodriguez
AU - Friedemann Schmidt
AU - Rowena Sison-Young
AU - Inari Soininen
AU - Sean Turner
AU - Bob van de Water
AU - Johan G. C. van Hasselt
AU - Filippo Venezia
AU - Jeffrey A. Willy
AU - Derek J. Leishman
AU - James L. Stevens
AU - Loic Laplanche
AB - Reliable prediction and prevention of adverse drug reactions (ADRs) remains a key challenge in the development of new medicines. Advanced mathematical and computational modelling approaches, which incorporate cutting-edge mechanistic understanding of ADRs in concert with systematically collected data addressing knowledge gaps, are integral components of model-informed drug discovery and development (MID3). These approaches provide a precise, quantitative framework for predicting and mitigating safety risks in the earliest phases of drug development. Here, we highlight recent developments in the burgeoning field of quantitative systems toxicology (QST), including insights into the current state-of-the-art, as well as outcomes from the Innovative Medicines Initiative (IMI) 2 TransQST project. QST models that describe the disruption of cardiovascular, gastrointestinal, hepatic and renal physiological functions following drug exposure are presented, along with recommendations for their application in drug discovery and development.
BT - Nature Reviews Drug Discovery
DA - 2025-10-27
DO - 10.1038/s41573-025-01308-z
LA - en
N2 - Reliable prediction and prevention of adverse drug reactions (ADRs) remains a key challenge in the development of new medicines. Advanced mathematical and computational modelling approaches, which incorporate cutting-edge mechanistic understanding of ADRs in concert with systematically collected data addressing knowledge gaps, are integral components of model-informed drug discovery and development (MID3). These approaches provide a precise, quantitative framework for predicting and mitigating safety risks in the earliest phases of drug development. Here, we highlight recent developments in the burgeoning field of quantitative systems toxicology (QST), including insights into the current state-of-the-art, as well as outcomes from the Innovative Medicines Initiative (IMI) 2 TransQST project. QST models that describe the disruption of cardiovascular, gastrointestinal, hepatic and renal physiological functions following drug exposure are presented, along with recommendations for their application in drug discovery and development.
PY - 2025
SP - 1
EP - 16
ST - Quantitative systems toxicology
T2 - Nature Reviews Drug Discovery
TI - Quantitative systems toxicology: modelling to mechanistically understand and predict drug safety
UR - https://www.nature.com/articles/s41573-025-01308-z
Y2 - 2025-11-06
SN - 1474-1784
ER -