ReviewNature reviews. Drug discovery2026
Quantitative systems toxicology: modelling to mechanistically understand and predict drug safety.
Review in Nature reviews. Drug discovery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- From molecular networks to the clinic: a systems biology-nanomedicine roadmap for osteoarthritis diagnosis, regeneration, and safety-by-design.Discover nano · 2026Review
- New approach methodologies (NAMs) for preclinical and translational evaluation of mRNA-lipid nanoparticle (LNP) therapeutics.Journal of controlled release : official journal of the Controlled Release Society · 2026Review
- AI-enabled engineering of hesperidin/ursodeoxycholic acid nanomedicine for synergistic treatment of drug-induced liver injury.Smart molecules : open access · 2026Article
- Predictive Safety in the Era of Artificial Intelligence: Promises, Limits, and the Case for an Enterprise Lifecycle Evidence Ecosystem.Drug safety · 2026Review
- Colonoid-Based Transcriptomics Reveals Conserved and Model-Specific Mechanisms of Doxorubicin-Induced Intestinal Toxicity.International journal of molecular sciences · 2026Article
- Quantitative Systems Toxicology Model Predicts Obeticholic Acid-Associated Liver Injury in Metabolic Dysfunction-Associated Steatotic Liver Disease.Clinical pharmacology and therapeutics · 2026Article
- Animal research in the UK: Regulation, implementation, welfare and development of new approach methodologies.Animal models and experimental medicine · 2026Review
- Intestinal Organoid-Based Mathematical Modeling Predicts Clinical Gastrointestinal Toxicity of Oral Oncology Drugs.CPT: pharmacometrics & systems pharmacology · 2026Article
- Curated and Structure-Based Drug-Target Interactions Improve Underprediction of Drug Side Effects in Network Models.Journal of chemical information and modeling · 2026Article
- Mapping safety in space: the emerging role of spatial transcriptomics in safe drug development.Frontiers in toxicology · 2026Review
- Quantitative systems toxicology approach that integrates PBPK and core hepatic metabolism: a case study with valproic acid.Frontiers in pharmacology · 2026Article
- A digital twin approach for transforming prescription drug labeling.Frontiers in pharmacology · 2026Article
- Insights and perspectives into the etiology of TKI-induced cardiotoxicity.ASPET discovery · 2026Article
Corrections and comments
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Authors and funding
42 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
41145622What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.