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Global Sensitivity Analysis of the Rodgers and Rowland Model for Prediction of Tissue: Plasma Partitioning Coefficients: Assessment of the Key Physiological and Physicochemical Factors That Determine Small-Molecule Tissue Distribution
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2020 (English)In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 22, no 41, p. 1-13Article in journal (Refereed) Epub ahead of print
Abstract [en]

In physiologically based pharmacokinetic (PBPK) modelling, the large number of input parameters, limited amount of available data and the structural model complexity generally hinder simultaneous estimation of uncertain and/or unknown parameters. These parameters are generally subject to estimation. However, the approaches taken for parameter estimation vary widely. Global sensitivity analyses are proposed as a method to systematically determine the most influential parameters that can be subject to estimation. Herein, a global sensitivity analysis was conducted to identify the key drug and physiological parameters influencing drug disposition in PBPK models and to potentially reduce the PBPK model dimensionality. The impact of these parameters was evaluated on the tissue-to-unbound plasma partition coefficients (Kpus) predicted by the Rodgers and Rowland model using Latin hypercube sampling combined to partial rank correlation coefficients (PRCC). For most drug classes, PRCC showed that LogP and fraction unbound in plasma (fup) were generally the most influential parameters for Kpu predictions. For strong bases, blood:plasma partitioning was one of the most influential parameter. Uncertainty in tissue composition parameters had a large impact on Kpu and Vss predictions for all classes. Among tissue composition parameters, changes in Kpu outputs were especially attributed to changes in tissue acidic phospholipid concentrations and extracellular protein tissue:plasma ratio values. In conclusion, this work demonstrates that for parameter estimation involving PBPK models and dimensionality reduction purposes, less influential parameters might be assigned fixed values depending on the parameter space, while influential parameters could be subject to parameters estimation.

Place, publisher, year, edition, pages
2020. Vol. 22, no 41, p. 1-13
Keywords [en]
drug distribution, global sensitivity analysis, partition coefficients, PBPK, uncertainty
National Category
Pharmaceutical Sciences Pharmacology and Toxicology Physiology Computational Mathematics Mathematical Analysis
Research subject
Applied and Computational Mathematics; Technology and Health; Applied and Computational Mathematics, Mathematical Statistics; Applied Medical Technology; Chemistry
Identifiers
URN: urn:nbn:se:kth:diva-267223DOI: 10.1208/s12248-020-0418-7ISI: 000514385900001PubMedID: 32016678Scopus ID: 2-s2.0-85078910511OAI: oai:DiVA.org:kth-267223DiVA, id: diva2:1391464
Note

QC 20200205

Available from: 2020-02-04 Created: 2020-02-04 Last updated: 2020-05-11Bibliographically approved

Open Access in DiVA

The full text will be freely available from 2021-02-08 00:00
Available from 2021-02-08 00:00

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Publisher's full textPubMedScopushttps://link.springer.com/article/10.1208/s12248-020-0418-7

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Darwich, Adam S.

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