Pharmacokinetics and pharmacodynamics of high-dose isoniazid for the treatment of rifampicin- or multidrug-resistant tuberculosis in Indonesia
Bibliographic record
Abstract
BACKGROUND: Pharmacokinetic data on high-dose isoniazid for the treatment of rifampicin-/multidrug-resistant tuberculosis (RR/MDR-TB) are limited. We aimed to describe the pharmacokinetics of high-dose isoniazid, estimate exposure target attainment, identify predictors of exposures, and explore exposure-response relationships in RR/MDR-TB patients. METHODS: We performed an observational pharmacokinetic study, with exploratory pharmacokinetic/pharmacodynamic analyses, in Indonesian adults aged 18-65 years treated for pulmonary RR/MDR-TB with standardized regimens containing high-dose isoniazid (10-15 mg/kg/day) for 9-11 months. Intensive pharmacokinetic sampling was performed after ≥2 weeks of treatment. Total plasma drug exposure (AUC0-24) and peak concentration (Cmax) were assessed using non-compartmental analyses. AUC0-24/MIC ratio of 85 and Cmax/MIC ratio of 17.5 were used as exposure targets. Multivariable linear and logistic regression analyses were used to identify predictors of drug exposures and responses, respectively. RESULTS: We consecutively enrolled 40 patients (median age 37.5 years). The geometric mean isoniazid AUC0-24 and Cmax were 35.4 h·mg/L and 8.5 mg/L, respectively. Lower AUC0-24 and Cmax values were associated (P < 0.05) with non-slow acetylator phenotype, and lower Cmax values were associated with male sex. Of the 26 patients with MIC data, less than 25% achieved the proposed targets for isoniazid AUC0-24/MIC (n = 6/26) and Cmax/MIC (n = 5/26). Lower isoniazid AUC0-24 values were associated with delayed sputum culture conversion (>2 months of treatment) [adjusted OR 0.18 (95% CI 0.04-0.89)]. CONCLUSIONS: Isoniazid exposures below targets were observed in most patients, and certain risk groups for low isoniazid exposures may require dose adjustment. The effect of low isoniazid exposures on delayed culture conversion deserves attention.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".