Meta-analysis of clinical studies supports the pharmacokinetic variability hypothesis for acquired drug resistance and failure of antituberculosis therapy

Abstract

Background: Using hollow-fiber tuberculosis studies, we recently demonstrated that nonadherence is not a significant factor for ADR and that therapy failure only occurs after a large proportion of doses are missed. Computer-aided clinical trial simulations have suggested that isoniazid and rifampin pharmacokinetic variability best explained poor outcomes. We were interested in determining whether isoniazid pharmacokinetic variability was associated with either microbiological failure or ADR in the clinic.

Methods: Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed. Prospective, randomized, controlled clinical trials that reported isoniazid acetylation status and microbiological outcomes were selected. The main effects examined were microbiological sputum conversion, ADR, and relapse. Effect size was expressed as pooled risk ratios (RRs) comparing rapid with slow acetylators.

Results: Thirteen randomized studies with 1631 rapid acetylators and 1751 slow acetylators met inclusion and exclusion criteria. Rapid acetylators were more likely than slow acetylators to have microbiological failure (RR, 2.0; 95% confidence interval [CI], 1.5-2.7), ADR (RR, 2.0; CI, 1.1-3.4), and relapse (RR, 1.3; CI, .9-2.0). Higher failure rates were encountered even in drug regimens comprising >3 antibiotics. No publication bias or small-study effects were observed for the outcomes evaluated.

Conclusions: Pharmacokinetic variability to a single drug in the regimen is significantly associated with failure of therapy and ADR in patients. This suggests that individualized dosing for tuberculosis may be more effective than standardized dosing, which is prescribed in directly observed therapy programs.

Figure 1.

Forest plot of studies analyzed for microbiological failure. Twenty-six antituberculosis regimens were administered, labeled regimens 1–26. Figure shows risk ratios and 95% confidence intervals for microbiological failure in fast versus slow acetylators, as well the percentage of weight contributed by each regimen toward the pooled estimate obtained using random-effects models. Abbreviations: CI, confidence interval; RR, risk ratio.

Figure 2.

Effect of acetylation status on failure during different dosing schedules. Risk of failure among rapid acetylators compared with slow acetylators was examined for once-weekly, twice-weekly, and daily dosing schedules. “Overall” refers to circumstances in which patients receiving once-weekly dosing were pooled together with those receiving twice- or thrice-weekly dosing, including those receiving rifapentine regimens.

Figure 3.

Effect of acetylation status on failure with different numbers of drugs in combination. Shown are pooled risk ratios, number of regimens combined based on the number of different drugs in each regimen, and measures of heterogeneity (I²) between the pooled regimens. Mixed-effects models were used to obtain effect estimates.

Figure 4.

Forest plot for acquisition of drug resistance. Figure shows risk ratios and 95% confidence intervals, as well as the percentage of weight contributed by each regimen toward the pooled estimate obtained using fixed-effects models. Abbreviations: CI, confidence interval; RR, risk ratio.

Figure 5.

Forest plot of studies that reported relapse. I² demonstrates good homogeneity. Results show a higher risk of relapse (risk ratio) for fast acetylators; however, the difference did not achieve statistical significance. Abbreviations: CI, confidence interval; RR, risk ratio.