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Meta-Analysis
. 2023 Mar 9;61(3):2201596.
doi: 10.1183/13993003.01596-2022. Print 2023 Mar.

Global estimates and determinants of antituberculosis drug pharmacokinetics in children and adolescents: a systematic review and individual patient data meta-analysis

Fajri Gafar  1 , Roeland E Wasmann  2 , Helen M McIlleron  2   3 , Rob E Aarnoutse  4 , H Simon Schaaf  5 , Ben J Marais  6   7 , Dipti Agarwal  8 , Sampson Antwi  9   10 , Nguyen D Bang  11 , Adrie Bekker  5 , David J Bell  12 , Chishala Chabala  2   13   14 , Louise Choo  15 , Geraint R Davies  16   17 , Jeremy N Day  18   19 , Rajeshwar Dayal  20 , Paolo Denti  2 , Peter R Donald  5 , Ephrem Engidawork  21 , Anthony J Garcia-Prats  5   22 , Diana Gibb  15 , Stephen M Graham  23   24 , Anneke C Hesseling  5 , Scott K Heysell  25 , Misgana I Idris  26 , Sushil K Kabra  27 , Aarti Kinikar  28 , Agibothu K Hemanth Kumar  29 , Awewura Kwara  30 , Rakesh Lodha  27 , Cecile Magis-Escurra  31 , Nilza Martinez  32 , Binu S Mathew  33 , Vidya Mave  28   34 , Estomih Mduma  35 , Rachel Mlotha-Mitole  36 , Stellah G Mpagama  37 , Aparna Mukherjee  27 , Heda M Nataprawira  38 , Charles A Peloquin  39 , Thomas Pouplin  40 , Geetha Ramachandran  29 , Jaya Ranjalkar  33 , Vandana Roy  41 , Rovina Ruslami  42 , Ira Shah  43 , Yatish Singh  20 , Marieke G G Sturkenboom  44 , Elin M Svensson  4   45 , Soumya Swaminathan  29   46 , Urmila Thatte  47 , Stephanie Thee  48 , Tania A Thomas  25 , Tjokosela Tikiso  2 , Daan J Touw  44 , Anna Turkova  15 , Thirumurthy Velpandian  49 , Lilly M Verhagen  50   51   52 , Jana L Winckler  5 , Hongmei Yang  53 , Vycke Yunivita  42 , Katja Taxis  54 , Jasper Stevens  44   55 , Jan-Willem C Alffenaar  7   56   57   55 ; Global Collaborative Group for Meta-Analysis of Paediatric Individual Patient Data in Pharmacokinetics of Anti-TB Drugs
Affiliations
Meta-Analysis

Global estimates and determinants of antituberculosis drug pharmacokinetics in children and adolescents: a systematic review and individual patient data meta-analysis

Fajri Gafar et al. Eur Respir J. .

Abstract

Background: Suboptimal exposure to antituberculosis (anti-TB) drugs has been associated with unfavourable treatment outcomes. We aimed to investigate estimates and determinants of first-line anti-TB drug pharmacokinetics in children and adolescents at a global level.

Methods: We systematically searched MEDLINE, Embase and Web of Science (1990-2021) for pharmacokinetic studies of first-line anti-TB drugs in children and adolescents. Individual patient data were obtained from authors of eligible studies. Summary estimates of total/extrapolated area under the plasma concentration-time curve from 0 to 24 h post-dose (AUC0-24) and peak plasma concentration (C max) were assessed with random-effects models, normalised with current World Health Organization-recommended paediatric doses. Determinants of AUC0-24 and C max were assessed with linear mixed-effects models.

Results: Of 55 eligible studies, individual patient data were available for 39 (71%), including 1628 participants from 12 countries. Geometric means of steady-state AUC0-24 were summarised for isoniazid (18.7 (95% CI 15.5-22.6) h·mg·L-1), rifampicin (34.4 (95% CI 29.4-40.3) h·mg·L-1), pyrazinamide (375.0 (95% CI 339.9-413.7) h·mg·L-1) and ethambutol (8.0 (95% CI 6.4-10.0) h·mg·L-1). Our multivariate models indicated that younger age (especially <2 years) and HIV-positive status were associated with lower AUC0-24 for all first-line anti-TB drugs, while severe malnutrition was associated with lower AUC0-24 for isoniazid and pyrazinamide. N-acetyltransferase 2 rapid acetylators had lower isoniazid AUC0-24 and slow acetylators had higher isoniazid AUC0-24 than intermediate acetylators. Determinants of C max were generally similar to those for AUC0-24.

Conclusions: This study provides the most comprehensive estimates of plasma exposures to first-line anti-TB drugs in children and adolescents. Key determinants of drug exposures were identified. These may be relevant for population-specific dose adjustment or individualised therapeutic drug monitoring.

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Conflict of interest statement

Conflict of interest: H.S. Schaaf reports grants from the NIH/IMPAACT; and honoraria from Ann Lake publications (sponsored by Johnson & Johnson) for an educational publication on the management of MDR-TB in children. A. Bekker reports grants from IMPAACT, UNITAID; lecture honoraria from Sandoz; support for attending PENTA PIM meeting; and received generic LPV/r, 3TC and ABC for the PETITE study. D.J. Bell reports support for attending a meeting from ViiV pharmaceuticals; and attendance fees for an advisory board meeting from ViiV pharmaceuticals. L. Choo reports grants from the UKRI MRC DFID Wellcome NIHR Joint Global Health Trials, TB Alliance Support for trial drug purchase and UKRI COVID-19 Grant Extension Allocation Award. P. Denti reports a grant for WHO expert review for TB drugs in children. S.M. Graham reports participation on a data safety monitoring board for the TB CHAMP trial; and leadership roles as a co-chair for the Guidelines Development Committee of the WHO updated recommendations and consolidated guidelines on child and adolescent TB, and as a core member for the WHO Child and Adolescent TB Working Group. S.K. Heysell reports grants from the NIH, DANIDA and EDTCP; royalties or licences from UpToDate; and honoraria for lectures from Henry Stewart Talks. A. Kwara reports a grant from the NIH/NICHD. V. Mave reports grants from the NIH and CDC. C.A. Peloquin reports a grant from the NIH. V. Roy reports a grant from the Delhi State TB Association; and leadership roles as a member of the Delhi State TB Association and the MAMC TB Committee. E.M. Svensson reports grants from the NWO personal Veni, IMI UNITE4TB consortium, TB Alliance, UNITAID BenefitKids consortium, WHO expert review, NIH support for IMPAACT studies, Blueprint, Probex, ACTG study Clo-FAST, Janssen Pharmaceuticals, EDCTP support PanTB-HM and Legochem; and leadership or fiduciary roles in the ISOP DI&E committee and BenNeLux PMX organising committee. U. Thatte reports participation on a data safety monitoring board for an ICMR TB trial. T.A. Thomas reports grants from the NIH and the University of Virginia. D.J. Touw reports a grant from Chiesi; consulting fees from Pure IMS and Sanguin; and participation on a data safety monitoring board for the FORMAT trial. A. Turkova reports grants from the UKRI MRC DFID Wellcome NIHR Joint Global Health Trials and MRC Grants for core funding of the Medical Research Council Clinical Trials Unit at the UCL; and TB Alliance Support for SHINE trial drug purchase. All of this work was declared by the authors to be outside the submitted work. All other authors declare no competing interests.

Figures

FIGURE 1
FIGURE 1
Study selection. #: these included unpublished studies or submitted manuscripts identified through contact with investigators (further details are provided in supplementary table E2); : repeated pharmacokinetic measurements in a patient on different days (different sampling occasions). AUC0–24: area under the plasma concentration–time curve from 0 to 24 h post-dose; Cmax: peak plasma concentration; IPD: individual patient data; PK: pharmacokinetic; TB: tuberculosis.
FIGURE 2
FIGURE 2
Forest plots for summary estimates (geometric mean (95% CI)) of dose-normalised area under the plasma concentration–time curve from 0 to 24 h post-dose (AUC0–24) for a) isoniazid, b) rifampicin, c) pyrazinamide and d) ethambutol in children and adolescents with tuberculosis, by sampling schedules (steady-state and non-steady-state). AUC0–24 values were dose-normalised for isoniazid at 10 mg·kg−1, rifampicin at 15 mg·kg−1, pyrazinamide at 35 mg·kg−1 and ethambutol at 20 mg·kg−1. The number in round brackets after the author's name indicates the different sampling occasions within a study. I2: percentage of variation across studies that is due to heterogeneity; QM: omnibus test of all model coefficients.
FIGURE 2
FIGURE 2
Forest plots for summary estimates (geometric mean (95% CI)) of dose-normalised area under the plasma concentration–time curve from 0 to 24 h post-dose (AUC0–24) for a) isoniazid, b) rifampicin, c) pyrazinamide and d) ethambutol in children and adolescents with tuberculosis, by sampling schedules (steady-state and non-steady-state). AUC0–24 values were dose-normalised for isoniazid at 10 mg·kg−1, rifampicin at 15 mg·kg−1, pyrazinamide at 35 mg·kg−1 and ethambutol at 20 mg·kg−1. The number in round brackets after the author's name indicates the different sampling occasions within a study. I2: percentage of variation across studies that is due to heterogeneity; QM: omnibus test of all model coefficients.
FIGURE 3
FIGURE 3
Forest plots for summary estimates (geometric mean (95% CI)) of dose-normalised peak plasma concentration (Cmax) for a) isoniazid, b) rifampicin, c) pyrazinamide and d) ethambutol in children and adolescents with tuberculosis, by sampling schedules (steady-state and non-steady-state). Cmax values were dose-normalised for isoniazid at 10 mg·kg−1, rifampicin at 15 mg·kg−1, pyrazinamide at 35 mg·kg−1 and ethambutol at 20 mg·kg−1. The number in round brackets after the author's name indicates the different sampling occasions within a study. I2: percentage of variation across studies that is due to heterogeneity; QM: omnibus test of all model coefficients.
FIGURE 3
FIGURE 3
Forest plots for summary estimates (geometric mean (95% CI)) of dose-normalised peak plasma concentration (Cmax) for a) isoniazid, b) rifampicin, c) pyrazinamide and d) ethambutol in children and adolescents with tuberculosis, by sampling schedules (steady-state and non-steady-state). Cmax values were dose-normalised for isoniazid at 10 mg·kg−1, rifampicin at 15 mg·kg−1, pyrazinamide at 35 mg·kg−1 and ethambutol at 20 mg·kg−1. The number in round brackets after the author's name indicates the different sampling occasions within a study. I2: percentage of variation across studies that is due to heterogeneity; QM: omnibus test of all model coefficients.
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