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Deriving mechanism-based pharmacodynamic models by reducing quantitative systems pharmacology models

  • Quantitative systems pharmacology (QSP) models integrate comprehensive qualitative and quantitative knowledge about pharmacologically relevant processes. We previously proposed a first approach to leverage the knowledge in QSP models to derive simpler, mechanism-based pharmacodynamic (PD) models. Their complexity, however, is typically still too large to be used in the population analysis of clinical data. Here, we extend the approach beyond state reduction to also include the simplification of reaction rates, elimination of reactions, and analytic solutions. We additionally ensure that the reduced model maintains a prespecified approximation quality not only for a reference individual but also for a diverse virtual population. We illustrate the extended approach for the warfarin effect on blood coagulation. Using the model-reduction approach, we derive a novel small-scale warfarin/international normalized ratio model and demonstrate its suitability for biomarker identification. Due to the systematic nature of the approach inQuantitative systems pharmacology (QSP) models integrate comprehensive qualitative and quantitative knowledge about pharmacologically relevant processes. We previously proposed a first approach to leverage the knowledge in QSP models to derive simpler, mechanism-based pharmacodynamic (PD) models. Their complexity, however, is typically still too large to be used in the population analysis of clinical data. Here, we extend the approach beyond state reduction to also include the simplification of reaction rates, elimination of reactions, and analytic solutions. We additionally ensure that the reduced model maintains a prespecified approximation quality not only for a reference individual but also for a diverse virtual population. We illustrate the extended approach for the warfarin effect on blood coagulation. Using the model-reduction approach, we derive a novel small-scale warfarin/international normalized ratio model and demonstrate its suitability for biomarker identification. Due to the systematic nature of the approach in comparison with empirical model building, the proposed model-reduction algorithm provides an improved rationale to build PD models also from QSP models in other applications.show moreshow less

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Metadaten
Author details:Undine FalkenhagenORCiD, Jane KnöchelORCiD, Charlotte Kloft, Wilhelm HuisingaORCiDGND
DOI:https://doi.org/10.1002/psp4.12903
ISSN:2163-8306
Pubmed ID:https://pubmed.ncbi.nlm.nih.gov/36866520
Title of parent work (English):CPT: Pharmacometrics & Systems Pharmacology
Subtitle (English):an application to warfarin
Publisher:Wiley
Place of publishing:Hoboken
Publication type:Article
Language:English
Date of first publication:2023/03/03
Publication year:2023
Release date:2024/07/02
Volume:12
Issue:4
Number of pages:12
First page:432
Last Page:443
Funding institution:Graduate Research Training Program PharMetrX: Pharmacometrics &; Computational Disease Modeling, Berlin/Potsdam, Germany
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Mathematik
DDC classification:5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie
Peer review:Referiert
Grantor:Publikationsfonds der Universität Potsdam
Publishing method:Open Access / Gold Open-Access
DOAJ gelistet
License (German):License LogoCC-BY-NC-ND - Namensnennung, nicht kommerziell, keine Bearbeitungen 4.0 International
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