Background
This study aims to explore relationships between baseline demographic covariates, plasma antibiotic exposure, sputum bacillary load, and clinical outcome data to help improve future tuberculosis (TB) treatment response predictions.
Conclusions
Patterns of early bacillary clearance matter. Static measurements such as month 2 sputum conversion and pharmacokinetic parameters such as C MAX/MIC and AUC0-24/MIC were predictive of treatment failure, but modeling of quantitative longitudinal data was required to assess the risk of recurrence. Pooled individual patient data analyses from larger datasets are needed to confirm these findings.
Methods
Data were available from a longitudinal cohort study in Malawian drug-sensitive TB patients on standard therapy, including steady-state plasma antibiotic exposure (154 patients), sputum bacillary load (102 patients), final outcome (95 patients), and clinical details. Population pharmacokinetic and pharmacokinetic-pharmacodynamic models were developed in the software package NONMEM. Outcome data were analyzed using univariate logistic regression and Cox proportional hazard models in R, a free software for statistical computing.
Results
Higher isoniazid exposure correlated with increased bacillary killing in sputum (P < .01). Bacillary killing in sputum remained fast, with later progression to biphasic decline, in patients with higher rifampicin area under the curve (AUC)0-24 (P < .01). Serial sputum colony counting negativity at month 2 (P < .05), isoniazid C MAX (P < .05), isoniazid C MAX/minimum inhibitory concentration ([MIC] P < .01), and isoniazid AUC0-24/MIC (P < .01) correlated with treatment success but not with remaining free of TB. Slower bacillary killing (P < .05) and earlier progression to biphasic bacillary decline (P < .01) both correlate with treatment failure. Posttreatment recurrence only correlated with slower bacillary killing (P < .05). Conclusions: Patterns of early bacillary clearance matter. Static measurements such as month 2 sputum conversion and pharmacokinetic parameters such as C MAX/MIC and AUC0-24/MIC were predictive of treatment failure, but modeling of quantitative longitudinal data was required to assess the risk of recurrence. Pooled individual patient data analyses from larger datasets are needed to confirm these findings.