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Use of a linearization approximation facilitating stochastic model building
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.
2014 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 41, no 2, 153-158 p.Article in journal (Refereed) Published
Abstract [en]

The objective of this work was to facilitate the development of nonlinear mixed effects models by establishing a diagnostic method for evaluation of stochastic model components. The random effects investigated were between subject, between occasion and residual variability. The method was based on a first-order conditional estimates linear approximation and evaluated on three real datasets with previously developed population pharmacokinetic models. The results were assessed based on the agreement in difference in objective function value between a basic model and extended models for the standard nonlinear and linearized approach respectively. The linearization was found to accurately identify significant extensions of the model's stochastic components with notably decreased runtimes as compared to the standard nonlinear analysis. The observed gain in runtimes varied between four to more than 50-fold and the largest gains were seen for models with originally long runtimes. This method may be especially useful as a screening tool to detect correlations between random effects since it substantially quickens the estimation of large variance-covariance blocks. To expedite the application of this diagnostic tool, the linearization procedure has been automated and implemented in the software package PsN.

Place, publisher, year, edition, pages
2014. Vol. 41, no 2, 153-158 p.
Keyword [en]
Linearization, Random effects, Nonlinear mixed effects models, Pharmacometrics, Diagnostics
National Category
Medical and Health Sciences
URN: urn:nbn:se:uu:diva-224577DOI: 10.1007/s10928-014-9353-5ISI: 000334075300005OAI: diva2:717918
Available from: 2014-05-19 Created: 2014-05-14 Last updated: 2016-05-12Bibliographically approved
In thesis
1. Pharmacometric Models to Improve Treatment of Tuberculosis
Open this publication in new window or tab >>Pharmacometric Models to Improve Treatment of Tuberculosis
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Tuberculosis (TB) is the world’s most deadly infectious disease and causes enormous public health problems. The comorbidity with HIV and the rise of multidrug-resistant TB strains impede successful therapy through drug-drug interactions and the lack of efficient second-line treatments. The aim of this thesis was to support the improvement of anti-TB therapy through development of pharmacometric models, specifically focusing on the novel drug bedaquiline, pharmacokinetic interactions and methods for pooled population analyses.

A population pharmacokinetic model of bedaquiline and its metabolite M2, linked to semi-mechanistic models of body weight and albumin concentrations, was developed and used for exposure-response analysis. Treatment response was quantified by measurements of mycobacterial load and early bedaquiline exposure was found to significantly impact the half-life of bacterial clearance. The analysis represents the first successful characterization of a concentration-effect relationship for bedaquiline.

Single-dose Phase I studies investigating potential interactions between bedaquiline and efavirenz, nevirapine, ritonavir-boosted lopinavir, rifampicin and rifapentine were analyzed with a model-based approach. Substantial effects were detected in several cases and dose-adjustments mitigating the impact were suggested after simulations. The interaction effects of nevirapine and ritonavir-boosted lopinavir were also confirmed in patients with multidrug-resistant TB on long-term treatment combining the antiretrovirals and bedaquiline. Furthermore, the outcomes from model-based analysis were compared to results from conventional non-compartmental analysis in a simulation study. Non-compartmental analysis was found to consistently underpredict the interaction effect when most of the concentration-time profile was not observed, as commonly is the case for compounds with very long terminal half-life such as bedaquiline.

To facilitate pooled analyses of individual patient data from multiple sources a structured development procedure was outlined and a fast diagnostic tool for extensions of the stochastic model components was developed. Pooled analyses of nevirapine and rifabutin pharmacokinetics were performed; the latter generating comprehensive dosing recommendations for combined administration of rifabutin and antiretroviral protease inhibitors.

The work presented in this thesis demonstrates the usefulness of pharmacometric techniques to improve treatment of TB and especially contributes evidence to inform optimized dosing regimens of new and old anti-TB drugs in various clinical contexts.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2016. 79 p.
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 214
pharmacokinetics, pharmacodynamics, population approach, nonlinear mixed-effects models, multidrug-resistant tuberculosis, bedaquiline, antiretroviral, drug-drug interactions, time-to-event, albumin
National Category
Medical and Health Sciences
Research subject
Clinical Pharmacology
urn:nbn:se:uu:diva-282139 (URN)978-91-554-9539-8 (ISBN)
Public defence
2016-05-20, B21, BMC, Husargatan 3, Uppsala, 09:15 (English)
Swedish Research Council, 521-2011-3442EU, FP7, Seventh Framework Programme, 115337EU, FP7, Seventh Framework Programme, 115156
Available from: 2016-04-28 Created: 2016-04-03 Last updated: 2016-05-12

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