Suarez-Kurtz G, Ribeiro F M, Vicente F L, Struchiner C J
Instituto Nacional de Câncer, Coordenação de Pesquisa, Rio de Janeiro, Brazil.
Antimicrob Agents Chemother. 2001 Nov;45(11):3029-36. doi: 10.1128/AAC.45.11.3029-3036.2001.
Amoxicillin plasma concentrations (n = 1,152) obtained from 48 healthy subjects in two bioequivalence studies were used to develop limited-sampling strategy (LSS) models for estimating the area under the concentration-time curve (AUC), the maximum concentration of drug in plasma (C(max)), and the time interval of concentration above MIC susceptibility breakpoints in plasma (T>MIC). Each subject received 500-mg amoxicillin, as reference and test capsules or suspensions, and plasma concentrations were measured by a validated microbiological assay. Linear regression analysis and a "jack-knife" procedure revealed that three-point LSS models accurately estimated (R(2), 0.92; precision, <5.8%) the AUC from 0 h to infinity (AUC(0-infinity)) of amoxicillin for the four formulations tested. Validation tests indicated that a three-point LSS model (1, 2, and 5 h) developed for the reference capsule formulation predicts the following accurately (R(2), 0.94 to 0.99): (i) the individual AUC(0-infinity) for the test capsule formulation in the same subjects, (ii) the individual AUC(0-infinity) for both reference and test suspensions in 24 other subjects, and (iii) the average AUC(0-infinity) following single oral doses (250 to 1,000 mg) of various amoxicillin formulations in 11 previously published studies. A linear regression equation was derived, using the same sampling time points of the LSS model for the AUC(0-infinity), but using different coefficients and intercept, for estimating C(max). Bioequivalence assessments based on LSS-derived AUC(0-infinity)'s and C(max)'s provided results similar to those obtained using the original values for these parameters. Finally, two-point LSS models (R(2) = 0.86 to 0.95) were developed for T>MICs of 0.25 or 2.0 microg/ml, which are representative of microorganisms susceptible and resistant to amoxicillin.
在两项生物等效性研究中,从48名健康受试者获得的阿莫西林血浆浓度(n = 1152)用于建立有限采样策略(LSS)模型,以估算浓度-时间曲线下面积(AUC)、血浆中药物的最大浓度(C(max))以及血浆中浓度高于MIC药敏断点的时间间隔(T>MIC)。每名受试者接受500 mg阿莫西林,分别作为参比胶囊和受试胶囊或混悬液,并通过经过验证的微生物测定法测量血浆浓度。线性回归分析和“留一法”程序显示,三点LSS模型能够准确估算(R(2),0.92;精密度,<5.8%)受试的四种制剂中阿莫西林从0小时至无穷大的AUC(AUC(0-无穷大))。验证试验表明,为参比胶囊制剂建立的三点LSS模型(1、2和5小时)能准确预测(R(2),0.94至0.99):(i)相同受试者中受试胶囊制剂的个体AUC(0-无穷大),(ii)另外24名受试者中参比和受试混悬液的个体AUC(0-无穷大),以及(iii)11项先前发表研究中单次口服不同阿莫西林制剂(250至1000 mg)后的平均AUC(0-无穷大)。利用LSS模型估算AUC(0-无穷大)时相同的采样时间点,但使用不同的系数和截距,推导了一个线性回归方程用于估算C(max)。基于LSS得出的AUC(0-无穷大)和C(max)进行的生物等效性评估提供的结果与使用这些参数的原始值获得的结果相似。最后,针对对阿莫西林敏感和耐药的微生物,分别为0.25或2.0 μg/ml的T>MIC建立了两点LSS模型(R(2)=0.86至0.95)。