Model-Predicted Impact of ECG Monitoring Strategies During Bedaquiline Treatment

Stijn W van Beek; Lénaïg Tanneau; Graeme Meintjes; Sean Wasserman; Neel R Gandhi; Angie Campbell; Charle A Viljoen; Lubbe Wiesner; Rob E Aarnoutse; Gary Maartens; James C M Brust; Elin M Svensson

doi:10.1093/ofid/ofac372

Model-Predicted Impact of ECG Monitoring Strategies During Bedaquiline Treatment

Open Forum Infect Dis. 2022 Jul 27;9(8):ofac372. doi: 10.1093/ofid/ofac372. eCollection 2022 Aug.

Authors

Stijn W van Beek¹, Lénaïg Tanneau², Graeme Meintjes³, Sean Wasserman^{3

4}, Neel R Gandhi^{5

6}, Angie Campbell⁵, Charle A Viljoen^{7

8}, Lubbe Wiesner⁹, Rob E Aarnoutse¹, Gary Maartens^{3

9}, James C M Brust¹⁰, Elin M Svensson^{1

2}

Affiliations

¹ Department of Pharmacy, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, the Netherlands.
² Department of Pharmacy, Uppsala University, Uppsala, Sweden.
³ Department of Medicine, Wellcome Centre for Infectious Diseases Research in Africa, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa.
⁴ Division of Infectious Diseases and HIV Medicine, Department of Medicine, University of Cape Town, Cape Town, South Africa.
⁵ Departments of Epidemiology & Global Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA.
⁶ Division of Infectious Diseases, Department of Medicine, Emory School of Medicine, Emory University, Atlanta, Georgia, USA.
⁷ Division of Cardiology, Department of Medicine, University of Cape Town, Cape Town, South Africa.
⁸ Cape Heart Institute, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa.
⁹ Division of Clinical Pharmacology, Department of Medicine, University of Cape Town, Cape Town, South Africa.
¹⁰ Division of General Internal Medicine, Department of Medicine, Albert Einstein College of Medicine, Bronx, New York, USA.

Abstract

Background: The M2 metabolite of bedaquiline causes QT-interval prolongation, making electrocardiogram (ECG) monitoring of patients receiving bedaquiline for drug-resistant tuberculosis necessary. The objective of this study was to determine the relationship between M2 exposure and Fridericia-corrected QT (QTcF)-interval prolongation and to explore suitable ECG monitoring strategies for 6-month bedaquiline treatment.

Methods: Data from the PROBeX study, a prospective observational cohort study, were used to characterize the relationship between M2 exposure and QTcF. Established nonlinear mixed-effects models were fitted to pharmacokinetic and ECG data. In a virtual patient population, QTcF values were simulated for scenarios with and without concomitant clofazimine. ECG monitoring strategies to identify patients who need to interrupt treatment (QTcF > 500 ms) were explored.

Results: One hundred seventy patients were included, providing 1131 bedaquiline/M2 plasma concentrations and 1702 QTcF measurements; 2.1% of virtual patients receiving concomitant clofazimine had QTcF > 500 ms at any point during treatment (0.7% without concomitant clofazimine). With monthly monitoring, almost all patients with QTcF > 500 ms were identified by week 12; after week 12, patients were predominantly falsely identified as QTcF > 500 ms due to stochastic measurement error. Following a strategy with monitoring before treatment and at weeks 2, 4, 8, and 12 in simulations with concomitant clofazimine, 93.8% of all patients who should interrupt treatment were identified, and 26.4% of all interruptions were unnecessary (92.1% and 32.2%, respectively, without concomitant clofazimine).

Conclusions: Our simulations enable an informed decision for a suitable ECG monitoring strategy by weighing the risk of missing patients with QTcF > 500 ms and that of interrupting bedaquiline treatment unnecessarily. We propose ECG monitoring before treatment and at weeks 2, 4, 8, and 12 after starting bedaquiline treatment.

Keywords: ECG monitoring; QT-interval prolongation; bedaquiline; modeling; tuberculosis.

Abstract

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