Serum Drug Concentrations Predictive of Pulmonary Tuberculosis Outcomes

Autor: Jotam G. Pasipanodya, Pete Smith, Peter Wash, André Burger, Tawanda Gumbo, Helen McIlleron
Rok vydání: 2013
Předmět:
Zdroj: Journal of Infectious Diseases. 208:1464-1473
ISSN: 1537-6613
0022-1899
Popis: In African countries with a high tuberculosis burden, the 2-month sputum culture conversion rate is only 50%–70%, and acquired drug resistance (ADR) continues to be a major problem [1–4]. In the laboratory, the hollow-fiber model of tuberculosis has demonstrated that the microbial kill and ADR prevention of first-line anti-tuberculosis agents are driven by such drug concentration measures as the 0–24 hour area under the concentration–time curve (AUC) to minimum inhibitory concentration (MIC) ratio and the peak concentration to MIC ratio [5–7]. These data have been used in computer-aided clinical trial simulations in the face of 100% adherence, which led us to hypothesize that between-patient pharmacokinetic variability could explain a large proportion of therapy failure and that adherence plays a relatively minor role [8, 9]. Here, we investigated whether such pharmacokinetic variability would indeed lead to a large proportion of patients failing to attain adequate concentrations and then failing therapy. We also sought to identify the drug concentrations that are predictive of clinical outcome. Several attempts to relate drug concentrations to tuberculosis outcomes have been made with conflicting results [10–16]. The reasons are unclear but could be one of several. First, in some studies, a single measure such as the 2-hour drug concentration (peak) was used to dichotomize patients into those with poor vs good outcomes. However, since drug AUCs are strongly associated with efficacy of first-line anti-tuberculosis agents in preclinical models [5–7, 17], a more intensive multisample schedule that allows AUC identification may be more informative. Second, several studies utilized predetermined peak concentration drug cutoff values to classify patients as having either low or high drug concentrations. These are peak concentrations of 3–5 mg/L for isoniazid, 8–24 mg/L for rifampin, and 20–50 mg/L for pyrazinamide [18]. These concentrations need further validation with regard to clinical outcomes. Third, noncompartmental pharmacokinetic analysis was utilized in some studies; however, pharmacokinetics of some anti-tuberculosis drugs may be best described using multiple compartments. A fourth possible reason may be the type of statistical analysis used. Biological systems such as anti-tuberculosis drug pharmacokinetics and the tuberculosis disease are best analyzed using nonlinear statistical approaches since they and most natural phenomena are nonlinear systems [19–23]. In linear analysis, complex problems are broken into smaller components that are then solved, after which the solutions are put together (superimposed) and added up to a solution of the whole problem. Nonlinear systems are characterized by discontinuities and relationships of higher-order complexity between components; the total function of the whole system is often more than the linear sum of its components. Therefore, components need to be analyzed in the context of all parameters interacting within the whole system. Here, we utilized classification and regression tree analysis (CART) to examine the role of several clinical factors, including drug concentrations, in toto, as predictors of clinical outcome in our cohort [24–29]. CART uses nonparametric techniques that examine both linear and nonlinear interactions simultaneously in the whole dataset and creates a hierarchy of predictors, starting with the most predictive to the least predictive.
Databáze: OpenAIRE
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