Office of Global Health, University of Texas Southwestern Medical Center, Dallas, Texas.
J Infect Dis. 2013 Nov 1;208(9):1464-73. doi: 10.1093/infdis/jit352. Epub 2013 Jul 29.
Based on a hollow-fiber system model of tuberculosis, we hypothesize that microbiologic failure and acquired drug resistance are primarily driven by low drug concentrations that result from pharmacokinetic variability.
Clinical and pharmacokinetic data were prospectively collected from 142 tuberculosis patients in Western Cape, South Africa. Compartmental pharmacokinetic parameters of isoniazid, rifampin, and pyrazinamide were identified for each patient. Patients were then followed for up to 2 years. Classification and regression tree analysis was used to identify and rank clinical predictors of poor long-term outcome such as microbiologic failure or death, or relapse.
Drug concentrations and pharmacokinetics varied widely between patients. Poor long-term outcomes were encountered in 35 (25%) patients. The 3 top predictors of poor long-term outcome, by rank of importance, were a pyrazinamide 24-hour area under the concentration-time curve (AUC) ≤ 363 mg·h/L, rifampin AUC ≤ 13 mg·h/L, and isoniazid AUC ≤ 52 mg·h/L. Poor outcomes were encountered in 32/78 patients with the AUC of at least 1 drug below the identified threshold vs 3/64 without (odds ratio = 14.14; 95% confidence interval, 4.08-49.08). Low rifampin and isoniazid peak and AUC concentrations preceded all cases of acquired drug resistance.
Low drug AUCs are predictive of clinical outcomes in tuberculosis patients.
基于结核病中空纤维系统模型，我们假设微生物学失败和获得性耐药主要是由药物浓度低引起的，这是由于药代动力学的变异性所致。
从南非西开普省的 142 名结核病患者中前瞻性地收集了临床和药代动力学数据。确定了每位患者异烟肼、利福平、吡嗪酰胺的房室药代动力学参数。然后对患者进行了长达 2 年的随访。分类回归树分析用于识别和排序与微生物学失败、死亡或复发等不良长期结局相关的临床预测因素。
药物浓度和药代动力学在患者之间差异很大。35 名（25%）患者出现不良长期结局。按重要性排序，3 个最重要的不良长期结局预测因素是吡嗪酰胺 24 小时浓度时间曲线下面积（AUC）≤363mg·h/L、利福平 AUC≤13mg·h/L 和异烟肼 AUC≤52mg·h/L。在 AUC 至少有 1 种药物低于确定阈值的 78 名患者中，有 32 名出现不良结局，而在 AUC 无低于阈值的 64 名患者中，有 3 名出现不良结局（比值比=14.14；95%置信区间，4.08-49.08）。低利福平和异烟肼的峰值和 AUC 浓度先于所有获得性耐药的发生。
低药物 AUC 可预测结核病患者的临床结局。
