02860nas a2200661   4500000000100000000000100001008004100002260001500043653001600058653002500074100002800099700002100127700001500148700002600163700002100189700002200210700001800232700002300250700002300273700001800296700001900314700002400333700002100357700001800378700001700396700001500413700001800428700002200446700001700468700001800485700001900503700002100522700001800543700002600561700001600587700001800603700002200621700002900643700002100672700002000693700002500713700002300738700002300761700001900784700001600803700002100819700002800840700002000868700002100888700002200909700002100931700001900952245010100971856005501072300000901127520104801136022001402184       2025                            d  c2025-10-2710adrug safety10aPreclinical research1 aChristopher E. Goldring1 aGiusy Russomanno1 aCarmen Pin1 aPanuwat Trairatphisan1 aKylie A. Beattie1 aCiarán P. Fisher1 aJanet Piñero1 aRichard J. Brennan1 aDiana Clausznitzer1 aIan M. Copple1 aTheo M. de Kok1 aCarrie A. Duckworth1 aLaura I. Furlong1 aBarbara Füzi1 aAttila Gabor1 aLouis Gall1 aJan Hengstler1 aHenning Hermjakob1 aFiona Hunter1 aDanyel Jennen1 aMikko Koskinen1 aSteven J. Kunnen1 aLieve Lammens1 aSebastian Lobentanzer1 aMarcel Mohr1 aElisa Passini1 aD. Mark Pritchard1 aRahuman S. Malik-Sheriff1 aBlanca Rodriguez1 aEric I. Rossman1 aJulio Saez-Rodriguez1 aFriedemann Schmidt1 aRowena Sison-Young1 aInari Soininen1 aSean Turner1 aBob van de Water1 aJohan G. C. van Hasselt1 aFilippo Venezia1 aJeffrey A. Willy1 aDerek J. Leishman1 aJames L. Stevens1 aLoic Laplanche00aQuantitative systems toxicology: modelling to mechanistically understand and predict drug safety  uhttps://www.nature.com/articles/s41573-025-01308-z  a1-163 aReliable 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.  a1474-1784