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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 (Cmax) were assessed with random-effects models, normalised with current World Health Organization-recommended paediatric doses. Determinants of AUC0–24 and Cmax 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 Cmax 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 individualis

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 (Cmax) were assessed with random-effects models, normalised with current World Health Organization-recommended paediatric doses. Determinants of AUC0–24 and Cmax 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 Cmax 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 individualis

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 (Cmax) were assessed with random-effects models, normalised with current World Health Organization-recommended paediatric doses. Determinants of AUC0–24 and Cmax 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 Cmax 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 individualis

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 (Cmax) were assessed with random-effects models, normalised with current World Health Organization-recommended paediatric doses. Determinants of AUC0–24 and Cmax 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 Cmax 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 individualis

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 (Cmax) were assessed with random-effects models, normalised with current World Health Organization-recommended paediatric doses. Determinants of AUC0–24 and Cmax 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 Cmax 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 individualis

Background In 2010, the World Health Organization (WHO) revised dosing guidelines for treatment of childhood tuberculosis. Our aim was to investigate first-line antituberculosis drug exposures under these guidelines, explore dose optimization using the current dispersible fixed-dose combination (FDC) tablet of rifampicin/isoniazid/pyrazinamide; 75/50/150 mg, and suggest a new FDC with revised weight bands. Methods Children with drug-susceptible tuberculosis in Malawi and South Africa underwent pharmacokinetic sampling while receiving first-line tuberculosis drugs as single formulations according the 2010 WHO recommended doses. Nonlinear mixed-effects modeling and simulation was used to design the optimal FDC and weight-band dosing strategy for achieving the pharmacokinetic targets based on literature-derived adult AUC(0-24h) for rifampicin (38.7-72.9), isoniazid (11.6-26.3), and pyrazinamide (233-429 mg center dot h/L). Results In total, 180 children (42% female; 13.9% living with human immunodeficiency virus [HIV]; median [range] age 1.9 [0.22-12] years; weight 10.7 [3.20-28.8] kg) were administered 1, 2, 3, or 4 FDC tablets (rifampicin/isoniazid/pyrazinamide 75/50/150 mg) daily for 4-8, 8-12, 12-16, and 16-25 kg weight bands, respectively. Rifampicin exposure (for weight and age) was up to 50% lower than in adults. Increasing the tablet number resulted in adequate rifampicin but relatively high isoniazid and pyrazinamide exposures. Administering 1, 2, 3, or 4 optimized FDC tablets (rifampicin/isoniazid/pyrazinamide 120/35/130 mg) to children < 6, 6-13, 13-20. and 20-25 kg, and 0.5 tablet in < 3-month-olds with immature metabolism, improved exposures to all 3 drugs. Conclusions Current pediatric FDC doses resulted in low rifampicin exposures. Optimal dosing of all drugs cannot be achieved with the current FDCs. We propose a new FDC formulation and revised weight bands. Current pediatric dosing guidelines lead to infant rifampicin exposures much lower than in

Aims Simulations are an essential tool for investigating scenarios in pharmacokinetics-pharmacodynamics. The models used during simulation often include the effect of highly correlated covariates such as weight, height and sex, and for children also age, which complicates the construction of an in silico population. For this reason, a suitable and representative patient population is crucial for the simulations to produce meaningful results. For simulation in paediatric patients, international growth charts from the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC) provide a reference, but these may not always be representative for specific populations, such as malnourished children with HIV or acutely unwell children. Methods We present a workflow to construct a virtual paediatric patient population using WHO and CDC growth charts, suggest piecewise linear functions to adjust the median of the growth charts by sex and age, and suggest visual diagnostics to compare with the target population. We applied this workflow in a population of 1206 HIV-positive African children, consisting of 19 742 observations with weight ranging from 3.8 to 79.7 kg, height from 55.5 to 180 cm, and an age between 0.40 and 18 years. Results Before adjustment, the WHO and CDC charts produced weights and heights higher compared to the observed data. After applying our methodology, we could simulate weight, height, sex and age combinations in good agreement with the observed data. Conclusion The methodology presented here is flexible and may be applied to other scenarios where WHO and CDC growth standards might not be appropriate. In addition we provide R scripts and a large ready-to-use paediatric population.

Aims Simulations are an essential tool for investigating scenarios in pharmacokinetics-pharmacodynamics. The models used during simulation often include the effect of highly correlated covariates such as weight, height and sex, and for children also age, which complicates the construction of an in silico population. For this reason, a suitable and representative patient population is crucial for the simulations to produce meaningful results. For simulation in paediatric patients, international growth charts from the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC) provide a reference, but these may not always be representative for specific populations, such as malnourished children with HIV or acutely unwell children. Methods We present a workflow to construct a virtual paediatric patient population using WHO and CDC growth charts, suggest piecewise linear functions to adjust the median of the growth charts by sex and age, and suggest visual diagnostics to compare with the target population. We applied this workflow in a population of 1206 HIV-positive African children, consisting of 19 742 observations with weight ranging from 3.8 to 79.7 kg, height from 55.5 to 180 cm, and an age between 0.40 and 18 years. Results Before adjustment, the WHO and CDC charts produced weights and heights higher compared to the observed data. After applying our methodology, we could simulate weight, height, sex and age combinations in good agreement with the observed data. Conclusion The methodology presented here is flexible and may be applied to other scenarios where WHO and CDC growth standards might not be appropriate. In addition we provide R scripts and a large ready-to-use paediatric population.

Aims Simulations are an essential tool for investigating scenarios in pharmacokinetics-pharmacodynamics. The models used during simulation often include the effect of highly correlated covariates such as weight, height and sex, and for children also age, which complicates the construction of an in silico population. For this reason, a suitable and representative patient population is crucial for the simulations to produce meaningful results. For simulation in paediatric patients, international growth charts from the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC) provide a reference, but these may not always be representative for specific populations, such as malnourished children with HIV or acutely unwell children. Methods We present a workflow to construct a virtual paediatric patient population using WHO and CDC growth charts, suggest piecewise linear functions to adjust the median of the growth charts by sex and age, and suggest visual diagnostics to compare with the target population. We applied this workflow in a population of 1206 HIV-positive African children, consisting of 19 742 observations with weight ranging from 3.8 to 79.7 kg, height from 55.5 to 180 cm, and an age between 0.40 and 18 years. Results Before adjustment, the WHO and CDC charts produced weights and heights higher compared to the observed data. After applying our methodology, we could simulate weight, height, sex and age combinations in good agreement with the observed data. Conclusion The methodology presented here is flexible and may be applied to other scenarios where WHO and CDC growth standards might not be appropriate. In addition we provide R scripts and a large ready-to-use paediatric population.

Aims Simulations are an essential tool for investigating scenarios in pharmacokinetics-pharmacodynamics. The models used during simulation often include the effect of highly correlated covariates such as weight, height and sex, and for children also age, which complicates the construction of an in silico population. For this reason, a suitable and representative patient population is crucial for the simulations to produce meaningful results. For simulation in paediatric patients, international growth charts from the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC) provide a reference, but these may not always be representative for specific populations, such as malnourished children with HIV or acutely unwell children. Methods We present a workflow to construct a virtual paediatric patient population using WHO and CDC growth charts, suggest piecewise linear functions to adjust the median of the growth charts by sex and age, and suggest visual diagnostics to compare with the target population. We applied this workflow in a population of 1206 HIV-positive African children, consisting of 19 742 observations with weight ranging from 3.8 to 79.7 kg, height from 55.5 to 180 cm, and an age between 0.40 and 18 years. Results Before adjustment, the WHO and CDC charts produced weights and heights higher compared to the observed data. After applying our methodology, we could simulate weight, height, sex and age combinations in good agreement with the observed data. Conclusion The methodology presented here is flexible and may be applied to other scenarios where WHO and CDC growth standards might not be appropriate. In addition we provide R scripts and a large ready-to-use paediatric population.

Aims Simulations are an essential tool for investigating scenarios in pharmacokinetics-pharmacodynamics. The models used during simulation often include the effect of highly correlated covariates such as weight, height and sex, and for children also age, which complicates the construction of an in silico population. For this reason, a suitable and representative patient population is crucial for the simulations to produce meaningful results. For simulation in paediatric patients, international growth charts from the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC) provide a reference, but these may not always be representative for specific populations, such as malnourished children with HIV or acutely unwell children. Methods We present a workflow to construct a virtual paediatric patient population using WHO and CDC growth charts, suggest piecewise linear functions to adjust the median of the growth charts by sex and age, and suggest visual diagnostics to compare with the target population. We applied this workflow in a population of 1206 HIV-positive African children, consisting of 19 742 observations with weight ranging from 3.8 to 79.7 kg, height from 55.5 to 180 cm, and an age between 0.40 and 18 years. Results Before adjustment, the WHO and CDC charts produced weights and heights higher compared to the observed data. After applying our methodology, we could simulate weight, height, sex and age combinations in good agreement with the observed data. Conclusion The methodology presented here is flexible and may be applied to other scenarios where WHO and CDC growth standards might not be appropriate. In addition we provide R scripts and a large ready-to-use paediatric population.