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Quantitative systems toxicology: modelling to mechanistically understand and predict drug safety

Nature Reviews Drug Discovery (2025)Cite this article

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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.

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Fig. 1: Construction and validation of quantitative systems toxicology models.
Fig. 2: Example implementation of a gastrointestinal toxicity model.
Fig. 3: Overview of the virtual assay platform for in silico drug trials to predict drug-induced pro-arrhythmic cardiotoxicity.

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Acknowledgements

This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement number 116030. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA. The authors declare that this work reflects only the views of the authors and that IMI-JU is not responsible for any use that may be made of the information it contains. This paper is dedicated to K. Park, a leader in the field of drug safety, who led the work of the TransQST Consortium from 2018 to 2020. B.R. is funded by a Wellcome Trust Fellowship in Basic Biomedical Sciences (214290/Z/18/Z).

Author information

Author notes

  1. These authors contributed equally: Christopher E. Goldring, Giusy Russomanno, Carmen Pin, Panuwat Trairatphisan, Kylie A. Beattie, Ciarán P. Fisher, Janet Piñero.

  2. These authors jointly supervised this work: Derek J. Leishman, James L. Stevens and Loic Laplanche

Authors and Affiliations

  1. Centre for Drug Safety Science, Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK

    Christopher E. Goldring, Giusy Russomanno, Ian M. Copple & Rowena Sison-Young

  2. Systems Medicine, Clinical Pharmacology and Safety Sciences, R&D Department, AstraZeneca, Cambridge, UK

    Carmen Pin & Louis Gall

  3. AbbVie Deutschland, Ludwigshafen, Germany

    Panuwat Trairatphisan, Diana Clausznitzer, Sean Turner & Loic Laplanche

  4. Institute for Computational Biomedicine, Faculty of Medicine, Heidelberg University and Heidelberg University Hospital, Heidelberg, Germany

    Panuwat Trairatphisan, Attila Gabor, Sebastian Lobentanzer & Julio Saez-Rodríguez

  5. Sanofi Research and Development, Frankfurt am Main, Germany

    Panuwat Trairatphisan, Marcel Mohr & Friedemann Schmidt

  6. GSK Research and Development, Stevenage, UK

    Kylie A. Beattie & Ciarán P. Fisher

  7. Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM) and Dept. of Medicine and Life Sciences (MELIS), Universitat Pompeu Fabra (UPF), Barcelona, Spain

    Janet Piñero & Laura I. Furlong

  8. MedBioinformatics Solutions SL, Barcelona, Spain

    Janet Piñero & Laura I. Furlong

  9. Sanofi Research and Development, Cambridge, MA, USA

    Richard J. Brennan

  10. Department of Translational Genomics, GROW Research Institute for Oncology and Developmental Biology, Universiteit Maastricht, Maastricht, The Netherlands

    Theo M. de Kok & Danyel Jennen

  11. Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK

    Carrie A. Duckworth & D. Mark Pritchard

  12. Universität Wien, Wien, Austria

    Barbara Füzi

  13. Forschungsgesellschaft für Arbeitsphysiologie und Arbeitsschutz e.V, Dortmund, Germany

    Jan Hengstler

  14. European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK

    Henning Hermjakob, Fiona Hunter & Rahuman S. Malik-Sheriff

  15. Orion Pharma, Orion Corporation, Espoo, Finland

    Mikko Koskinen

  16. Universiteit Leiden, Leiden, The Netherlands

    Steven J. Kunnen, Bob van de Water, Johan G. C. van Hasselt & James L. Stevens

  17. Galapagos NV, Mechelen, Belgium

    Lieve Lammens

  18. NC3Rs – National Centre for the Replacement, Refinement and Reduction of Animals in Research, London, UK

    Elisa Passini

  19. Department of Computer Science, British Heart Foundation Centre of Research Excellence, University of Oxford, Oxford, UK

    Elisa Passini & Blanca Rodríguez

  20. GSK, Collegeville, PA, USA

    Eric I. Rossman

  21. Synapse Research Management Partners, Madrid, Spain

    Inari Soininen

  22. Institut de Recherches Internationales Servier, Suresnes, France

    Filippo Venezia

  23. Vertex Pharmaceuticals, Boston, MA, USA

    Jeffrey A. Willy

  24. Eli Lilly and Company, Indianapolis, IN, USA

    Derek J. Leishman

Authors
  1. Christopher E. Goldring
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  2. Giusy Russomanno
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  7. Janet Piñero
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  8. Richard J. Brennan
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  9. Diana Clausznitzer
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  11. Theo M. de Kok
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  31. Julio Saez-Rodríguez
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  38. Filippo Venezia
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  39. Jeffrey A. Willy
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  40. Derek J. Leishman
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  41. James L. Stevens
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  42. Loic Laplanche
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Corresponding authors

Correspondence to Christopher E. Goldring or Loic Laplanche.

Ethics declarations

Competing interests

C.P. and L.G. are employees and shareholders of AstraZeneca. K.A.B., C.P.F. and E.I.R. are employees of GSK. F.S., M.M. and P.T. are employees and shareholders, and R.J.B. is a retired employee and shareholder of Sanofi. D.C., S.T. and L.L. are employees and shareholders of AbbVie. M.K. is an employee of Orion Corporation. L.L. is a shareholder of Johnson & Johnson. J.S.-R. reports in the past 3 years funding from GSK and Pfizer and fees or honoraria from Travere Therapeutics, Stadapharm, Astex, Owkin, Pfizer, Grunenthal, Tempus and Moderna. F.V. is an employee of Servier. J.A.W. is an employee of Vertex Pharmaceuticals. D.J.L. is an employee of Eli Lilly and Company. All other authors declared no competing interests for this work.

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Related links

ChEMBL: https://www.ebi.ac.uk/chembl/

DrugBank data sets: https://www.drugbank.com/datasets

ExpressionAtlas: https://www.ebi.ac.uk/gxa/home

GastroPlus PBPK and PBBM software: https://www.simulations-plus.com/software/gastroplus/

GitHub PKSim: https://github.com/Open-Systems-Pharmacology/PK-Sim

IUPHAR/BPS Guide to PHARMACOLOGY: https://www.guidetopharmacology.org/

PharmGKB (ClinPGx): https://www.clinpgx.org/

Simcyp PBPK: https://www.certara.com/software/simcyp-pbpk/

Simulations Plus: https://www.simulations-plus.com/

Therapeutic Target Database: https://db.idrblab.net/ttd/

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Goldring, C.E., Russomanno, G., Pin, C. et al. Quantitative systems toxicology: modelling to mechanistically understand and predict drug safety. Nat Rev Drug Discov (2025). https://doi.org/10.1038/s41573-025-01308-z

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