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Pharmacometric Models to Improve Treatment of Tuberculosis
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala Universitet. (Farmakometri)
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.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 214
Keyword [en]
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
Identifiers
URN: urn:nbn:se:uu:diva-282139ISBN: 978-91-554-9539-8 (print)OAI: oai:DiVA.org:uu-282139DiVA: diva2:916511
Public defence
2016-05-20, B21, BMC, Husargatan 3, Uppsala, 09:15 (English)
Opponent
Supervisors
Funder
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
List of papers
1. Population Pharmacokinetics of Bedaquiline and Metabolite M2 in Patients With Drug-Resistant Tuberculosis: The Effect of Time-Varying Weight and Albumin
Open this publication in new window or tab >>Population Pharmacokinetics of Bedaquiline and Metabolite M2 in Patients With Drug-Resistant Tuberculosis: The Effect of Time-Varying Weight and Albumin
2016 (English)In: CPT: pharmacometrics and systems pharmacology, ISSN 2163-8306, Vol. 5, no 12, 682-691 p.Article in journal (Refereed) Published
Abstract [en]

Albumin concentration and body weight are altered in patients with multidrug-resistant tuberculosis (MDR-TB) and change during the long treatment period, potentially affecting drug disposition. We here describe the pharmacokinetics (PKs) of the novel anti-TB drug bedaquiline and its metabolite M2 in 335 patients with MDR-TB receiving 24 weeks of bedaquiline on top of a longer individualized background regimen. Semiphysiological models were developed to characterize the changes in weight and albumin over time. Bedaquiline and M2 disposition were well described by three and one-compartment models, respectively. Weight and albumin were correlated, typically increasing after the start of treatment, and significantly affected bedaquiline and M2 plasma disposition. Additionally, age and race were significant covariates, whereas concomitant human immunodeficiency virus (HIV) infection, sex, or having extensively drug-resistant TB was not. This is the first population model simultaneously characterizing bedaquiline and M2 PKs in its intended use population. The developed model will be used for efficacy and safety exposure-response analyses.

National Category
Medical and Health Sciences
Research subject
Pharmaceutical Pharmacology
Identifiers
urn:nbn:se:uu:diva-281724 (URN)10.1002/psp4.12147 (DOI)000390923300005 ()
Funder
Swedish Research Council, 521-2011-3442EU, FP7, Seventh Framework Programme, FP7/2007-2013
Note

Title in Thesis list of papers: Population pharmacokinetics of bedaquiline and metabolite M2 in drug-resistant tuberculosis patients – the effect of time-varying weight and albumin

Available from: 2016-03-30 Created: 2016-03-30 Last updated: 2017-02-08Bibliographically approved
2. Modeling of mycobacterial load reveals bedaquiline’s exposure-response relationship in patients with drug-resistant tuberculosis
Open this publication in new window or tab >>Modeling of mycobacterial load reveals bedaquiline’s exposure-response relationship in patients with drug-resistant tuberculosis
(English)Manuscript (preprint) (Other academic)
National Category
Medical and Health Sciences
Research subject
Pharmaceutical Pharmacology
Identifiers
urn:nbn:se:uu:diva-281733 (URN)
Available from: 2016-03-30 Created: 2016-03-30 Last updated: 2016-05-12
3. Model-Based Estimates of the Effects of Efavirenz on Bedaquiline Pharmacokinetics and Suggested Dose Adjustments for Patients Coinfected with HIV and Tuberculosis
Open this publication in new window or tab >>Model-Based Estimates of the Effects of Efavirenz on Bedaquiline Pharmacokinetics and Suggested Dose Adjustments for Patients Coinfected with HIV and Tuberculosis
Show others...
2013 (English)In: Antimicrobial Agents and Chemotherapy, ISSN 0066-4804, E-ISSN 1098-6596, Vol. 57, no 6, 2780-2787 p.Article in journal (Refereed) Published
Abstract [en]

Safe, effective concomitant treatment regimens for tuberculosis (TB) and HIV infection are urgently needed. Bedaquiline (BDQ) is a promising new anti-TB drug, and efavirenz (EFV) is a commonly used antiretroviral. Due to EFV's induction of cytochrome P450 3A4, the metabolic enzyme responsible for BDQ biotransformation, the drugs are expected to interact. Based on data from a phase I, single-dose pharmacokinetic study, a nonlinear mixed-effects model characterizing BDQ pharmacokinetics and interaction with multiple-dose EFV was developed. BDQ pharmacokinetics were best described by a 3-compartment disposition model with absorption through a dynamic transit compartment model. Metabolites M2 and M3 were described by 2-compartment models with clearance of BDQ and M2, respectively, as input. Impact of induction was described as an instantaneous change in clearance 1 week after initialization of EFV treatment and estimated for all compounds. The model predicts average steady-state concentrations of BDQ and M2 to be reduced by 52% (relative standard error [RSE], 3.7%) with chronic coadministration. A range of models with alternative structural assumptions regarding onset of induction effect and fraction metabolized resulted in similar estimates of the typical reduction and did not offer a markedly better fit to data. Simulations to investigate alternative regimens mitigating the estimated interaction effect were performed. The results suggest that simple adjustments of the standard regimen during EFV coadministration can prevent reduced exposure to BDQ without increasing exposures to M2. However, exposure to M3 would increase. Evaluation in clinical trials of adjusted regimens is necessary to ensure appropriate dosing for HIV-infected TB patients on an EFV-based regimen.

National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:uu:diva-203265 (URN)10.1128/AAC.00191-13 (DOI)000319272100043 ()
Available from: 2013-07-09 Created: 2013-07-08 Last updated: 2017-12-06Bibliographically approved
4. Impact of Lopinavir-Ritonavir or Nevirapine on Bedaquiline Exposures and Potential Implications for Patients with Tuberculosis-HIV Coinfection
Open this publication in new window or tab >>Impact of Lopinavir-Ritonavir or Nevirapine on Bedaquiline Exposures and Potential Implications for Patients with Tuberculosis-HIV Coinfection
2014 (English)In: Antimicrobial Agents and Chemotherapy, ISSN 0066-4804, E-ISSN 1098-6596, Vol. 58, no 11, 6406-6412 p.Article in journal (Refereed) Published
Abstract [en]

Concomitant treatment of tuberculosis (TB) and HIV is recommended and improves outcomes. Bedaquiline is a novel drug for the treatment of multidrug-resistant (MDR) TB; combined use with antiretroviral drugs, nevirapine, or ritonavir-boosted lopinavir (LPV/r) is anticipated, but no clinical data from coinfected patients are available. Plasma concentrations of bedaquiline and its M2 metabolite after single doses were obtained from interaction studies with nevirapine or LPV/r in healthy volunteers. The antiretrovirals' effects on bedaquiline and M2 pharmacokinetics were assessed by nonlinear mixed-effects modeling. Potential dose adjustments were evaluated with simulations. No significant effects of nevirapine on bedaquiline pharmacokinetics were identified. LPV/r decreased bedaquiline and M2 clearances to 35% (relative standard error [RSE], 9.2%) and 58% (RSE, 8.4%), respectively, of those without comedication. As almost 3-fold (bedaquiline) and 2-fold (M2) increases in exposures during chronic treatment with LPV/r are expected, dose adjustments are suggested for evaluation. Efficacious, safe bedaquiline dosing for MDR-TB patients receiving antiretrovirals is important. Modeling results suggest that bedaquiline can be coadministered with nevirapine without dose adjustments. The predicted elevation of bedaquiline and M2 levels during LPV/r coadministration may be a safety concern, and careful monitoring is recommended. Further data are being collected in coinfected patients to determine whether dose adjustments are needed.

National Category
Microbiology in the medical area
Identifiers
urn:nbn:se:uu:diva-238405 (URN)10.1128/AAC.03246-14 (DOI)000344158600008 ()25114140 (PubMedID)
Available from: 2014-12-18 Created: 2014-12-12 Last updated: 2017-12-05Bibliographically approved
5. Rifampicin and rifapentine significantly reduce concentrations of bedaquiline, a new anti-TB drug
Open this publication in new window or tab >>Rifampicin and rifapentine significantly reduce concentrations of bedaquiline, a new anti-TB drug
2015 (English)In: Journal of Antimicrobial Chemotherapy, ISSN 0305-7453, E-ISSN 1460-2091, Vol. 70, no 4, 1106-1114 p.Article in journal (Refereed) Published
Abstract [en]

Objectives: Bedaquiline is the first drug of a new class approved for the treatment of TB in decades. Bedaquiline is metabolized by cytochrome P450 (CYP) 3A4 to a less-active M2 metabolite. Its terminal half-life is extremely long (5-6 months), complicating evaluations of drug-drug interactions. Rifampicin and rifapentine, two anti-TB drugs now being optimized to shorten TB treatment duration, are potent inducers of CYP3A4. This analysis aimed to predict the effect of repeated doses of rifampicin or rifapentine on the steady-state pharmacokinetics of bedaquiline and its M2 metabolite from single-dose data using a model-based approach. Methods: Pharmacokinetic data for bedaquiline and M2 were obtained from a Phase I study involving 32 individuals each receiving two doses of bedaquiline, alone or together with multiple-dose rifampicin or rifapentine. Sampling was performed over 14 days following each bedaquiline dose. Pharmacokinetic analyses were performed using non-linear mixed-effects modelling. Models were used to simulate potential dose adjustments. Results: Rifamycin co-administration increased bedaquiline clearance substantially: 4.78-fold [ relative standard error (RSE) 9.10%] with rifampicin and 3.96-fold (RSE 5.00%) with rifapentine. Induction of M2 clearance was equally strong. Average steady-state concentrations of bedaquiline and M2 are predicted to decrease by 79% and 75% when given with rifampicin or rifapentine, respectively. Simulations indicated that increasing the bedaquiline dosage to mitigate the interaction would yield elevated M2 concentrations during the first treatment weeks. Conclusions: Rifamycin antibiotics reduce bedaquiline concentrations substantially. In line with current treatment guidelines for drug-susceptible TB, concomitant use is not recommended, even with dose adjustment.

Keyword
drug-drug interactions, population pharmacokinetics, tuberculosis
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-256860 (URN)10.1093/jac/dku504 (DOI)000354708600021 ()25535219 (PubMedID)
Funder
Swedish Research Council, 521-2011-3442
Available from: 2015-06-26 Created: 2015-06-26 Last updated: 2017-12-04Bibliographically approved
6. Confirming model-predicted pharmacokinetic interactions between bedaquiline and lopinavir/ritonavir or nevirapine in patients with HIV and drug resistant tuberculosis
Open this publication in new window or tab >>Confirming model-predicted pharmacokinetic interactions between bedaquiline and lopinavir/ritonavir or nevirapine in patients with HIV and drug resistant tuberculosis
Show others...
(English)Article in journal (Refereed) Submitted
National Category
Medical and Health Sciences
Research subject
Pharmaceutical Pharmacology
Identifiers
urn:nbn:se:uu:diva-281726 (URN)
Available from: 2016-03-30 Created: 2016-03-30 Last updated: 2016-05-12
7. Pharmacokinetic Interactions for Drugs with a Long Half-Life-Evidence for the Need of Model-Based Analysis
Open this publication in new window or tab >>Pharmacokinetic Interactions for Drugs with a Long Half-Life-Evidence for the Need of Model-Based Analysis
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2016 (English)In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 18, no 1, 171-179 p.Article in journal (Refereed) Published
Abstract [en]

Pharmacokinetic drug-drug interactions (DDIs) can lead to undesired drug exposure, resulting in insufficient efficacy or aggravated toxicity. Accurate quantification of DDIs is therefore crucial but may be difficult when full concentration-time profiles are problematic to obtain. We have compared non-compartmental analysis (NCA) and model-based predictions of DDIs for long half-life drugs by conducting simulation studies and reviewing published trials, using antituberculosis drug bedaquiline (BDQ) as a model compound. Furthermore, different DDI study designs were evaluated. A sequential design mimicking conducted trials and a population pharmacokinetic (PK) model of BDQ and the M2 metabolite were utilized in the simulations where five interaction scenarios from strong inhibition (clearance fivefold decreased) to strong induction (clearance fivefold increased) were evaluated. In trial simulations, NCA systematically under-predicted the DDIs' impact. The bias in average exposure was 29-96% for BDQ and 20-677% for M2. The model-based analysis generated unbiased predictions, and simultaneous fitting of metabolite data increased precision in DDI predictions. The discrepancy between the methods was also apparent for conducted trials, e.g., lopinavir/ritonavir was predicted to increased BDQ exposure 22% by NCA and 188% by model-based methods. In the design evaluation, studies with parallel designs were considered and shown to generally be inferior to sequential/cross-over designs. However, in the case of low inter-individual variability and no informative metabolite data, a prolonged parallel design could be favored. Model-based analysis for DDI assessments is preferable over NCA for victim drugs with a long half-life and should always be used when incomplete concentration-time profiles are part of the analysis.

Keyword
drug-drug interactions, long half-life, model-based analysis, non-compartmental analysis, pharmacokinetics
National Category
Pharmacology and Toxicology
Identifiers
urn:nbn:se:uu:diva-275555 (URN)10.1208/s12248-015-9829-2 (DOI)000367529900014 ()
Funder
Swedish Research Council, 521-2011-3442EU, FP7, Seventh Framework Programme, FP7/2007-2013
Available from: 2016-02-04 Created: 2016-02-04 Last updated: 2017-11-30Bibliographically approved
8. Use of a linearization approximation facilitating stochastic model building
Open this publication in new window or tab >>Use of a linearization approximation facilitating stochastic model building
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.

Keyword
Linearization, Random effects, Nonlinear mixed effects models, Pharmacometrics, Diagnostics
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:uu:diva-224577 (URN)10.1007/s10928-014-9353-5 (DOI)000334075300005 ()
Available from: 2014-05-19 Created: 2014-05-14 Last updated: 2017-12-05Bibliographically approved
9. Integration of data from multiple sources for simultaneous modelling analysis: experience from nevirapine population pharmacokinetics
Open this publication in new window or tab >>Integration of data from multiple sources for simultaneous modelling analysis: experience from nevirapine population pharmacokinetics
Show others...
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.

Keyword
missing covariates, nevirapine, NONMEM, population pharmacokinetics, prediction and variability corrected visual predictive check, simultaneous modelling analysis
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:uu:diva-181835 (URN)10.1111/j.1365-2125.2012.04205.x (DOI)000307218100008 ()
Available from: 2012-10-02 Created: 2012-10-01 Last updated: 2017-12-07Bibliographically approved
10. Population pharmacokinetic drug-drug interaction pooled analysis of existing data for rifabutin and HIV PIs
Open this publication in new window or tab >>Population pharmacokinetic drug-drug interaction pooled analysis of existing data for rifabutin and HIV PIs
Show others...
2016 (English)In: Journal of Antimicrobial Chemotherapy, ISSN 0305-7453, E-ISSN 1460-2091, Vol. 71, no 5, 1330-1340 p.Article in journal (Refereed) Published
Abstract [en]

OBJECTIVES: Extensive but fragmented data from existing studies were used to describe the drug-drug interaction between rifabutin and HIV PIs and predict doses achieving recommended therapeutic exposure for rifabutin in patients with HIV-associated TB, with concurrently administered PIs.

METHODS: Individual-level data from 13 published studies were pooled and a population analysis approach was used to develop a pharmacokinetic model for rifabutin, its main active metabolite 25-O-desacetyl rifabutin (des-rifabutin) and drug-drug interaction with PIs in healthy volunteers and patients who had HIV and TB (TB/HIV).

RESULTS: Key parameters of rifabutin affected by drug-drug interaction in TB/HIV were clearance to routes other than des-rifabutin (reduced by 76%-100%), formation of the metabolite (increased by 224% in patients), volume of distribution (increased by 606%) and distribution to the peripheral compartment (reduced by 47%). For des-rifabutin, clearance was reduced by 35%-76% and volume of distribution increased by 67%-240% in TB/HIV. These changes resulted in overall increased exposure to rifabutin in TB/HIV patients by 210% because of the effects of PIs and 280% with ritonavir-boosted PIs.

CONCLUSIONS: Given together with non-boosted or ritonavir-boosted PIs, rifabutin at 150 mg once daily results in similar or higher exposure compared with rifabutin at 300 mg once daily without concomitant PIs and may achieve peak concentrations within an acceptable therapeutic range. Although 300 mg of rifabutin every 3 days with boosted PI achieves an average equivalent exposure, intermittent doses of rifamycins are not supported by current guidelines.

National Category
Infectious Medicine
Identifiers
urn:nbn:se:uu:diva-281723 (URN)10.1093/jac/dkv470 (DOI)000376291300027 ()26832753 (PubMedID)
Funder
Swedish Research Council, 521-2011-3442
Available from: 2016-03-30 Created: 2016-03-30 Last updated: 2017-11-30Bibliographically approved

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