@article{6406,
  keywords = {drug safety, Preclinical research},
  author = {Christopher E. Goldring and Giusy Russomanno and Carmen Pin and Panuwat Trairatphisan and Kylie A. Beattie and Ciarán P. Fisher and Janet Piñero and Richard J. Brennan and Diana Clausznitzer and Ian M. Copple and Theo M. de Kok and Carrie A. Duckworth and Laura I. Furlong and Barbara Füzi and Attila Gabor and Louis Gall and Jan Hengstler and Henning Hermjakob and Fiona Hunter and Danyel Jennen and Mikko Koskinen and Steven J. Kunnen and Lieve Lammens and Sebastian Lobentanzer and Marcel Mohr and Elisa Passini and D. Mark Pritchard and Rahuman S. Malik-Sheriff and Blanca Rodriguez and Eric I. Rossman and Julio Saez-Rodriguez and Friedemann Schmidt and Rowena Sison-Young and Inari Soininen and Sean Turner and Bob van de Water and Johan G. C. van Hasselt and Filippo Venezia and Jeffrey A. Willy and Derek J. Leishman and James L. Stevens and Loic Laplanche},
  title = {Quantitative systems toxicology: modelling to mechanistically understand and predict drug safety},
  abstract = {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.},
  year = {2025},
  journal = {Nature Reviews Drug Discovery},
  pages = {1-16},
  month = {2025-10-27},
  issn = {1474-1784},
  url = {https://www.nature.com/articles/s41573-025-01308-z},
  doi = {10.1038/s41573-025-01308-z},
  language = {en},
}