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Pharmacometric Models to Improve Treatment of Tuberculosis Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy 214 Pharmacometric Models to Improve Treatment of Tuberculosis ELIN M SVENSSON ACTA UNIVERSITATIS UPSALIENSIS ISSN 1651-6192 ISBN 978-91-554-9539-8 UPPSALA urn:nbn:se:uu:diva-282139 2016 Dissertation presented at Uppsala University to be publicly examined in B21, BMC, Husargatan 3, Uppsala, Friday, 20 May 2016 at 09:15 for the degree of Doctor of Philosophy (Faculty of Pharmacy). The examination will be conducted in English. Faculty examiner: Senior Program Officer, Quantitative Sciences N. L’Ntshotsholé (Shasha) Jumbe (Deptartment of Global Health, Bill & Melinda Gates Foundation). Abstract Svensson, E. M. 2016. Pharmacometric Models to Improve Treatment of Tuberculosis. Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy 214. 79 pp. Uppsala: Acta Universitatis Upsaliensis. ISBN 978-91-554-9539-8. 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. Keywords: pharmacokinetics, pharmacodynamics, population approach, nonlinear mixed- effects models, multidrug-resistant tuberculosis, bedaquiline, antiretroviral, drug-drug interactions, time-to-event, albumin Elin M Svensson, Department of Pharmaceutical Biosciences, Box 591, Uppsala University, SE-75124 Uppsala, Sweden. © Elin M Svensson 2016 ISSN 1651-6192 ISBN 978-91-554-9539-8 urn:nbn:se:uu:diva-282139 (http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-282139) Om du inte står för något faller du för allt [If you don’t stand for something you fall for all] Emil Jensen To Thomas, my poet Cover artwork by Mira Sjögren http://cargocollective.com/mirasjogren Typical daily pill burden in treatment of multidrug-resistant tuberculosis List of Papers This thesis is based on the following papers, which are referred to in the text by their Roman numerals. I Svensson EM, Dosne AG, Karlsson MO. Population pharma- cokinetics of bedaquiline and metabolite M2 in drug-resistant tuberculosis patients – the effect of time-varying weight and al- bumin. Submitted. II Svensson EM, Rossenu S, Karlsson, MO. Modeling of myco- bacterial load reveals bedaquiline’s exposure-response relation- ship in patients with drug-resistant tuberculosis. In manuscript. III Svensson EM, Aweeka F, Park JG, Marzan F, Dooley KE, Karlsson MO. (2013) Model-based estimates of the effects of efavirenz on bedaquiline pharmacokinetics and suggested dose adjustments for patients coinfected with HIV and tuberculosis. Antimicrob Agents Chemother, 57(6):2780-7 IV Svensson EM, Dooley KE, Karlsson MO. (2014) Impact of lopinavir/ritonavir or nevirapine on bedaquiline exposures and potential implications for patients with Tuberculosis-HIV coin- fection. Antimicrob Agents Chemother, 58(11):6406-12 V Svensson EM, Murray S, Karlsson MO, Dooley KE. (2015) Rifampicin and rifapentine significantly reduce concentrations of bedaquiline, a new anti-TB drug. J Antimicrob Chemother, 70(4):1106-14 VI Brill MJE, Svensson EM, Pandie M, Maartens G, Karlsson MO. Confirming model-predicted pharmacokinetic interactions between bedaquiline and lopinavir/ritonavir or nevirapine in pa- tients co-infected with HIV and drug-resistant tuberculosis. Submitted. VII Svensson EM, Acharya C, Clauson B, Dooley KE, Karlsson MO. (2016) Pharmacokinetic interactions for drugs with a long half-life – evidence for the need of model-based analysis. AAPS J, 18(1):171-9 VIII Svensson EM, Karlsson MO. (2014) Use of a linearization ap- proximation facilitating stochastic model building. J Pharma- cokinet Pharmacodyn, 41(2):153-8 IX Svensson E, van der Walt JS, Barnes KI, Cohen K, Kredo T, Huitema A, Nachega JB, Karlsson MO, Denti, P. (2012) Inte- gration of data from multiple sources for simultaneous model- ling analysis: experience from nevirapine population pharmaco- kinetics. Br J Clin Pharmacol, 74(3):465-76 X Hennig S, Svensson EM, Niebecker R, Fourie B, Weiner M, Bonora S, Peloquin C, Gallicano K, Flexner C, Pym A, Vis P, Olliaro P, McIlleron H, Karlsson MO. (2016) Population phar- macokinetic drug-drug interaction pooled analysis of existing data for rifabutin and HIV PIs. J Antimicrob Chemother, E-pub ahead of print. Reprints were made with permission from the respective publishers. Contents Introduction ................................................................................................... 11 Tuberculosis epidemiology ...................................................................... 11 HIV and tuberculosis coinfection ........................................................ 12 Drug-resistant tuberculosis .................................................................. 13 Treatment of tuberculosis ......................................................................... 13 First-line treatment............................................................................... 13 Second-line treatment .......................................................................... 14 Bedaquiline .......................................................................................... 15 Pharmacokinetics and pharmacodynamics ............................................... 16 Drug-drug interactions ......................................................................... 16 Biomarkers for tuberculosis ................................................................. 17 Pharmacometrics ...................................................................................... 18 Nonlinear mixed-effects models .......................................................... 18 Maximum likelihood estimation methods ........................................... 19 Aims .............................................................................................................. 21 Methods ........................................................................................................ 23 Study designs and data ............................................................................. 23 Bedaquiline dose-exposure-response (Paper I-II) ................................ 23 Bedaquiline drug-drug interactions (Paper III-VI) .............................. 25 Methodology for pooled population PK analysis (Paper VIII-X) ....... 26 Previously developed models ................................................................... 27 Software ................................................................................................... 27 Model development procedures ............................................................... 27 Results and discussion .................................................................................. 29 Bedaquiline dose-exposure-response ....................................................... 29 Population PK and time-varying covariates (Paper I) ......................... 29 PK-PD model of time to positivity (Paper II) ...................................... 35 Bedaquiline drug-drug interactions .......................................................... 40 Model-based analyses
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