A model qualification method for mechanistic physiological QSP models to support model‐informed drug development
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Mechanistic physiological modeling is a scientific method that combines available data with scientific knowledge and engineering approaches to facilitate better understanding of biological systems, improve decision‐making, reduce risk, and increase efficiency in drug discovery and development. It is a type of quantitative systems pharmacology (QSP) approach that places drug‐specific properties in the context of disease biology. This tutorial provides a broadly applicable model qualification method (MQM) to ensure that mechanistic physiological models are fit for their intended purposes.
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A number of publications have recently proposed guidelines or recommendations on modeling practices; some focus on the design process of QSP models, whereas others suggest methods for assessing the quality and/or adequacy of QSP models in particular or models that support the MID3 paradigm in general.
“A number of publications have recently proposed guidelines or recommendations on modeling practices. Some of these focus on the design process of QSP models,26, 27, 28 whereas others suggest methods for assessing the quality and/or adequacy of QSP models in particular27, 28 or models that support the MID3 paradigm in general.29”
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“Model qualification has also been described as an approach to determine whether a model is fit for purpose.(29) On the other hand, other authors(30,32,43,44) define verification as the process of model testing with a data set that has not been used for initial model building, often part of a verification/modification…”
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Gulati, A., & Brady, J. A. (2024) · Journal of Pharmacokinetics and Pharmacodynamics · doi:10.1007/s10928-024-09945-4
Tabish, M., & Naquvi, K. J. (2025) · Journal of Neonatal Surgery · doi:10.52783/jns.v14.2251
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Sengupta, D., & Panda, S. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.17.745376
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- Authors
- Friedrich, C.
- Affiliations
- Rosa & Co, LLC San Francisco California USA
- Venue
- CPT: Pharmacometrics & Systems Pharmacology
- Publisher
- Wiley
- Published
- 2016-01-26
- Volume
- 5
- Issue
- 2
- Pages
- 43-53
- ISSN
- 2163-8306 (ISSN-L)
- PMC
- PMC4761232
- OpenAlex
- W2255465777
- Cited by
- 87
- References
- 72
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 14 · 2023: 8 · 2022: 8 · 2021: 7 · 2020: 6 · 2019: 15 · 2018: 7 · 2017: 5 · 2016: 6
- FWCI
- 11.12
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- CPT Pharmacometrics & Systems Pharmacology (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- University of California, San Francisco
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Pharmaceutical studies and practices
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- quantitative systems pharmacology, model-informed drug development, model validation, drug development, drug discovery
- MeSH terms
- Humans, Models, Biological, Systems Biology, Drug Discovery
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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