Population Pharmacokinetics of Bedaquiline and Metabolite M2 in Patients With Drug-Resistant Tuberculosis: The Effect of Time-Varying Weight and Albumin

E M Svensson, A-G Dosne, M O Karlsson, E M Svensson, A-G Dosne, M O Karlsson

Abstract

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.

© 2016 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

Figures

Figure 1
Figure 1
Visual predictive check showing the 2.5th, 50th, and 97.5th percentiles (lines) of observed albumin concentration and body weights (dots) and the 95% confidence intervals (shaded areas) of the same percentiles from model‐simulated data.
Figure 2
Figure 2
Examples of individual fits from three patients with distinctly different profiles: typical increase (ID1), less common decrease (ID2), or relatively constant (ID3) albumin concentrations.
Figure 3
Figure 3
Prediction and variability corrected visual predictive checks showing the 2.5th, 50th, and 97.5th percentiles (lines) for observed bedaquiline (BDQ) and M2 concentrations and the 95% confidence intervals (shaded areas) of the same percentiles from model‐simulated data over time after the start of treatment (upper panel 0–24 weeks and middle panel 24–96 weeks) and over time after dose (lower panel).
Figure 4
Figure 4
Illustration of covariate relationships included on bedaquiline (BDQ) clearance (CL) and central volume of distribution (V) in the final model. For CL the solid and dotted lines represent the typical values for non‐black and black patients, respectively. For V, the solid line is the typical value. The shaded areas are the 95% confidence interval of the interindividual variability. The lines at the bottom of the graphs represent observed covariate values at the start of treatment.

References

    1. European Medicines Agency CHMP assessment report: SIRTURO . <>.
    1. Kakkar, A.K. & Dahiya, N. Bedaquiline for the treatment of resistant tuberculosis: promises and pitfalls. Tuberculosis (Edinb). 94, 357–362 (2014).
    1. Zignol, M. et al Global incidence of multidrug‐resistant tuberculosis. J. Infect. Dis. 194, 479–485 (2006).
    1. World Health Organization Global Tuberculosis Report 2015 . 20th ed. (World Health Organization, Geneva, Switzerland, 2015).
    1. Wu, S. et al Adverse events associated with the treatment of multidrug‐resistant tuberculosis: a systematic review and meta‐analysis. Am. J. Ther. 23, e521–e530 (2016).
    1. Andries, K. et al A diarylquinoline drug active on the ATP synthase of Mycobacterium tuberculosis. Science 307, 223–227 (2005).
    1. Koul, A. et al Diarylquinolines target subunit c of mycobacterial ATP synthase. Nat. Chem. Biol. 3, 323–324 (2007).
    1. Diacon, A.H. et al Randomized pilot trial of eight weeks of bedaquiline (TMC207) treatment for multidrug‐resistant tuberculosis: long‐term outcome, tolerability, and effect on emergence of drug resistance. Antimicrob. Agents Chemother. 56, 3271–3276 (2012).
    1. Diacon, A.H. et al Multidrug‐resistant tuberculosis and culture conversion with bedaquiline. N. Engl. J. Med. 371, 723–732 (2014).
    1. FDA Office of Antimicrobial Products Briefing package , NDA 204‐384, SirturoTM, (bedaquiline 100 mg tablets), for the treatment of adults (≥18 years) as part of combination therapy of pulmonary multi‐drug resistant tuberculosis (MDRTB). <>. Accessed 13 March 2014.
    1. Diacon, A.H. et al Final 120‐week results of a phase II randomised, double‐blind, placebo‐controlled study of 24‐weeks bedaquiline treatment for MDR‐TB (C208). Int. J. Tuberc. Lung Dis. 17, S234–S235 (abstract OP–176–02) (2013).
    1. Janssen Pharmaceuticals Sirturo , United States product insert. <>. Accessed 13 March 2014.
    1. van Heeswijk, R.P. , Dannemann, B. & Hoetelmans, R.M. Bedaquiline: a review of human pharmacokinetics and drug‐drug interactions. J. Antimicrob. Chemother. 69, 2310–2318 (2014).
    1. Liu, K. et al Bedaquiline metabolism: enzymes and novel metabolites. Drug Metab. Dispos. 42, 863–866 (2014).
    1. Mesens, N. , Verbeeck, J. & Rouan, M. Elucidating the role of M2 in the preclinical safety profile of TMC207 (38th Union World Conference on Lung Health, PS‐71358‐11, p. S167. Cape Town, South Africa, 2007).
    1. van Heeswijk, R. et al The effect of CYP3A4 inhibition on the clinical pharmacokinetics of TMC207 (38th Union World Conference on Lung Health, PS‐71358‐1, Cape Town, South Africa, 2007).
    1. US Food and Drug Administration Center for drug evaluation and research . Application number 204384Orig1s000, Clinical Pharmacology and Biopharmaceutics review(s). <>.
    1. Svensson, E.M. , Aweeka, F. , Park, J.G. , Marzan, F. , Dooley, K.E. & Karlsson, M.O. 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, 2780–2787 (2013).
    1. Svensson, E.M. , Dooley, K.E. & Karlsson, M.O. Impact of lopinavir‐ritonavir or nevirapine on bedaquiline exposures and potential implications for patients with tuberculosis‐HIV coinfection. Antimicrob. Agents Chemother. 58, 6406–6412 (2014).
    1. Svensson, E.M. , Murray, S. , Karlsson, M.O. & Dooley, K.E. Rifampicin and rifapentine significantly reduce concentrations of bedaquiline, a new anti‐TB drug. J. Antimicrob. Chemother. 70, 1106–1114 (2015).
    1. McLeay, S C. , Vis, P. , van Heeswijk, R.P. & Green, B. Population pharmacokinetics of bedaquiline (TMC207), a novel antituberculosis drug. Antimicrob. Agents Chemother. 58, 5315–5324 (2014).
    1. Pym, A.S. et al Bedaquiline in the treatment of multidrug‐ and extensively drug‐resistant tuberculosis. Eur. Respir. J. 47, 564–574 (2016).
    1. Savic, R.M. , Jonker, D.M. , Kerbusch, T. & Karlsson, M.O. Implementation of a transit compartment model for describing drug absorption in pharmacokinetic studies. J. Pharmacokinet. Pharmacodyn. 34, 711–726 (2007).
    1. Bisaso, K.R. et al Characterizing plasma albumin concentration changes in TB/HIV patients on anti retroviral and anti‐tuberculosis therapy. In Silico Pharmacol. 2, 3 (2014).
    1. Anderson, B.J. & Holford, N.H. Mechanism‐based concepts of size and maturity in pharmacokinetics. Annu. Rev. Pharmacol. Toxicol. 48, 303–332 (2008).
    1. Bergstrand, M. , Hooker, A.C. , Wallin, J.E. & Karlsson, M.O. Prediction‐corrected visual predictive checks for diagnosing nonlinear mixed‐effects models. AAPS J. 13, 143–151 (2011).
    1. Dosne, A.‐G. , Bergstrand, M. & Karlsson, M.O. Application of sampling importance resampling to estimate parameter uncertainty distributions. Abstracts of the Annual Meeting of the Population Approach in Europe. (p 22, Abstract 2907, Glasgow, Scotland, 2013).
    1. Dosne, A.‐G. , Bergstrand, M. & Karlsson, M.O. Determination of appropriate settings in the assessment of parameter uncertainty distributions using sampling importance resampling (SIR). Abstracts of the Annual Meeting of the Population Approach in Europe. (p 24, Abstract 3546, Crete, Greece, 2015).
    1. Dosne, A.G. , Bergstrand, M. , Harling, K. & Karlsson, M.O. Improving the estimation of parameter uncertainty distributions in nonlinear mixed effects models using sampling importance resampling. J. Pharmacokinet. Pharmacodyn. (2016); e‐pub ahead of print.
    1. Beal, S. , Sheiner, L.B. , Boeckmann, A. & Bauer, R.J. NONMEM user's guides (1989–2013) (Icon Development Solutions, Ellicott City, MD, 2013).
    1. Lindbom, L. , Pihlgren, P. & Jonsson, E.N. PsN‐Toolkit–a collection of computer intensive statistical methods for non‐linear mixed effect modeling using NONMEM. Comput. Methods Programs Biomed. 79, 241–257 (2005).
    1. Jonsson, E.N. & Karlsson, M.O. Xpose–an S‐PLUS based population pharmacokinetic/pharmacodynamic model building aid for NONMEM. Comput. Methods Programs Biomed. 58, 51–64 (1999).
    1. Keizer, R.J. , Karlsson, M.O. & Hooker, A. Modeling and simulation workbench for NONMEM: tutorial on Pirana, PsN, and Xpose. CPT Pharmacometrics Syst. Pharmacol. 2, e50 (2013).
    1. Bergstrand, M. Application of mixed‐effects to improve mechanistic understanding and predictability of oral absorption. University dissertation from Uppsala: Acta Universitatis Upsaliensis. <> (2011).
    1. Petersson, K.J. , Hanze, E. , Savic, R.M. & Karlsson, M.O. Semiparametric distributions with estimated shape parameters. Pharm. Res. 26, 2174–2185 (2009).
    1. Svensson, E.M. , Acharya, C. , Clauson, B. , Dooley, K.E. & Karlsson, M.O. Pharmacokinetic interactions for drugs with a long half‐life—evidence for the need of model‐based analysis. AAPS J. 18, 171–179 (2016).
    1. Ramakrishnan, K. , Shenbagarathai, R. , Kavitha, K. , Uma, A. , Balasubramaniam, R. & Thirumalaikolundusubramanian, P. Serum zinc and albumin levels in pulmonary tuberculosis patients with and without HIV. Jpn. J. Infect. Dis. 61, 202–204 (2008).
    1. Peresi, E. , Silva, S.M. , Calvi, S.A. & Marcondes‐Machado, J. Cytokines and acute phase serum proteins as markers of inflammatory regression during the treatment of pulmonary tuberculosis. J. Bras. Pneumol. 34, 942–949 (2008).
    1. Benet, L.Z. & Hoener, B.A. Changes in plasma protein binding have little clinical relevance. Clin. Pharmacol. Ther. 71, 115–121 (2002).
    1. Johnson, T.N. , Rostami‐Hodjegan, A. & Tucker, G.T. Prediction of the clearance of eleven drugs and associated variability in neonates, infants and children. Clin. Pharmacokinet. 45, 931–956 (2006).
    1. Lamba, J.K. , Lin, Y.S. , Schuetz, E.G. & Thummel, K.E. Genetic contribution to variable human CYP3A‐mediated metabolism. Adv. Drug Deliv. Rev. 54, 1271–1294 (2002).
    1. Brill, M.J. , Svensson, E.M. , Pandie, M. , Maartens, G. & Karlsson, M.O. Confirming model‐predicted pharmacokinetic interactions between bedaquiline and lopinavir/ritonavir or nevirapine in patients with HIV and drug resistant tuberculosis. Abstracts from the 25th meeting, 7–10 June 2016. (p 25, Abstract 5919, Lisboa, Portugal, 2016).
    1. Svensson, E.M. , Rossenu, S. & Karlsson, M.O. Bedaquiline's exposure‐response relationship revealed through modeling of mycobacterial load. Abstracts from the 25th meeting, 7–10 June 2016. (p 25, Abstract 5937, Lisboa, Portugal, 2016).

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