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Development of visual predictive checks accounting for multimodal parameter distributions in mixture models
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Univ Cologne, Fac Med, Gleueler Str 24, D-50931 Cologne, Germany;Univ Cologne, Univ Hosp Cologne, Ctr Pharmacol, Dept Pharmacol 1, Gleueler Str 24, D-50931 Cologne, Germany.ORCID iD: 0000-0002-7003-5300
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.ORCID iD: 0000-0003-1258-8297
2019 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 46, no 3, p. 241-250Article in journal (Refereed) Published
Abstract [en]

The assumption of interindividual variability being unimodally distributed in nonlinear mixed effects models does not hold when the population under study displays multimodal parameter distributions. Mixture models allow the identification of parameters characteristic to a subpopulation by describing these multimodalities. Visual predictive check (VPC) is a standard simulation based diagnostic tool, but not yet adapted to account for multimodal parameter distributions. Mixture model analysis provides the probability for an individual to belong to a subpopulation (IPmix) and the most likely subpopulation for an individual to belong to (MIXEST). Using simulated data examples, two implementation strategies were followed to split the data into subpopulations for the development of mixture model specific VPCs. The first strategy splits the observed and simulated data according to the MIXEST assignment. A shortcoming of the MIXEST-based allocation strategy was a biased allocation towards the dominating subpopulation. This shortcoming was avoided by splitting observed and simulated data according to the IPmix assignment. For illustration purpose, the approaches were also applied to an irinotecan mixture model demonstrating 36% lower clearance of irinotecan metabolite (SN-38) in individuals with UGT1A1 homo/heterozygote versus wild-type genotype. VPCs with segregated subpopulations were helpful in identifying model misspecifications which were not evident with standard VPCs. The new tool provides an enhanced power of evaluation of mixture models.

Place, publisher, year, edition, pages
SPRINGER/PLENUM PUBLISHERS , 2019. Vol. 46, no 3, p. 241-250
Keywords [en]
Visual predictive checks, Mixture models, Multimodal parameter distributions, Pharmacokinetics, Pharmacodynamics
National Category
Pharmaceutical Sciences
Identifiers
URN: urn:nbn:se:uu:diva-385960DOI: 10.1007/s10928-019-09632-9ISI: 000468597100003PubMedID: 30968312OAI: oai:DiVA.org:uu-385960DiVA, id: diva2:1327478
Available from: 2019-06-19 Created: 2019-06-19 Last updated: 2019-06-19Bibliographically approved

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Arshad, UsmanChasseloup, EstelleNordgren, RikardKarlsson, Mats O.

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