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Integration of data from multiple sources for simultaneous modelling analysis: experience from nevirapine population pharmacokinetics
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Farmakometri)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Farmakometri)
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2012 (English)In: British Journal of Clinical Pharmacology, ISSN 0306-5251, E-ISSN 1365-2125, Vol. 74, no 3, 465-476 p.Article in journal (Refereed) Published
Abstract [en]

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT Integrating individual data from multiple sources in one simultaneous population analysis (sometimes called a mega-model) can address novel research questions and add power for covariate detection without requiring new clinical studies. However, the development of this type of model can be challenging and time consuming. Nevirapine is a non-nucleoside reverse transcriptase inhibitor commonly used for treatment of human immunodeficiency virus infection in resource-limited settings.

WHAT THIS STUDY ADDS This study outlines a strategy for integration of data from multiple sources for modelling analysis. It provides suggestions on handling of missing covariates in the context of several data sources and a starting point for development of a multinational nevirapine mega-model. AIMS To propose a modelling strategy to efficiently integrate data from different sources in one simultaneous analysis, using nevirapine population pharmacokinetic data as an example.

METHODS Data from three studies including 115 human immunodeficiency virus-infected South African adults were used. Patients were on antiretroviral therapy regimens including 200 mg nevirapine twice daily and sampled at steady state. A development process was suggested, implemented in NONMEM7 and the final model evaluated with an external data set.

RESULTS A stepwise approach proved efficient. Model development started with the intensively sampled data. Data were added sequentially, using visual predictive checks for inspecting their compatibility with the existing model. Covariate exploration was carried out, and auxiliary regression models were designed for imputation of missing covariates. Nevirapine pharmacokinetics was described by a one-compartment model with absorption through two transit compartments. Body size was accounted for using allometric scaling. The model included a mixture of two subpopulations with different typical values of clearance, namely fast (3.12 l h-1) and slow metabolizers (1.45 l h-1), with 17% probability of belonging to the latter. Absorption displayed large between-occasion variability, and food slowed the absorption mean transit time from 0.6 to 2.5 h. Concomitant antitubercular treatment including rifampicin typically decreased bioavailability by 39%, with significant between-subject variability. Visual predictive checks of external validation data indicated good predictive performance.

CONCLUSIONS The development strategy succeeded in integrating data from different sources to produce a model with robust parameter estimates. This work paves the way for the creation of a nevirapine mega-model, including additional data from numerous diverse sources.

Place, publisher, year, edition, pages
2012. Vol. 74, no 3, 465-476 p.
Keyword [en]
missing covariates, nevirapine, NONMEM, population pharmacokinetics, prediction and variability corrected visual predictive check, simultaneous modelling analysis
National Category
Medical and Health Sciences
URN: urn:nbn:se:uu:diva-181835DOI: 10.1111/j.1365-2125.2012.04205.xISI: 000307218100008OAI: oai:DiVA.org:uu-181835DiVA: diva2:558145
Available from: 2012-10-02 Created: 2012-10-01 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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Svensson, Elinvan der Walt, Jan-StefanKarlsson, Mats O.
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