Quantitative systems toxicology: modelling to mechanistically understand and predict drug safety - Goldring, Christopher; Russomanno, Giusy; Pin, Carmen; Trairatphisan, Panuwat; Beattie, Kylie A.; Fisher, Ciarán P.; Piñero, Janet; Brennan, Richard J.; Clausznitzer, Diana; Copple, Ian M.; de Kok, Theo M.; Duckworth, Carrie A.; et al. (Nature reviews. Drug discovery, 2025)
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.
Veröffentlicht: 27. Oktober 2025
Gesehen am 02.01.2026