Pharmacokinetics of standard versus high-dose rifampin for tuberculosis preventive treatment: A sub-study of the 2R2 randomized controlled trial
Bibliographic record
Abstract
Pharmacokinetic data of rifampin, when used for tuberculosis preventive treatment (TPT) are not available. We aimed to describe the pharmacokinetics of rifampin used for TPT, at standard and higher doses, and to assess predictors of rifampin exposure. A pharmacokinetic sub-study was performed in Bandung, Indonesia among participants in the 2R2 randomized trial, which compared TPT regimens of two months of high-dose rifampin at 20 mg/kg/day (2R20) and 30 mg/kg/day (2R30), with four months of standard-dose rifampin at 10 mg/kg/day (4R10) in adolescents and adults. Intensive pharmacokinetic sampling was performed after 2–8 weeks of treatment. Pharmacokinetic parameters were assessed non-compartmentally. Total exposure (AUC0-24) and peak concentration (Cmax) between arms were compared using one-way ANOVA and Tukey's post-hoc tests. Multivariable linear regression analyses were used to assess predictors of AUC0-24 and Cmax. We enrolled 51 participants in this study. In the 4R10, 2R20, and 2R30 arms, the geometric mean AUC0-24 was 68.0, 186.8, and 289.9 h⋅mg/L, and Cmax was 18.4, 36.7, and 54.4 mg/L, respectively; high inter-individual variabilities were observed. Compared with the 4R10 arm, AUC0-24 and Cmax were significantly higher in the 2R20 and 2R30 arms (p<0.001). Drug doses, body weight, and female sex were predictors of higher rifampin AUC0-24 and Cmax (p<0.05). AUC0-24 and Cmax values were much higher than those reported in persons with TB disease. Doubling and tripling the rifampin dose led to three- and four-fold higher exposure compared to standard dose. Pharmacokinetic/pharmacodynamic modeling and simulations are warranted to support shortening of TPT regimens with high-dose rifampin.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".