Mangas-Sanjuan Victor, Colon-Useche Sarin, Gonzalez-Alvarez Isabel, Bermejo Marival, Garcia-Arieta Alfredo
Department of Engineering, Pharmacy and Pharmaceutical Technology Area, Miguel Hernandez University, Elche, Spain.
Analysis and Control Department, University of Los Andes, Mérida, Venezuela.
AAPS J. 2016 Nov;18(6):1550-1561. doi: 10.1208/s12248-016-9971-5. Epub 2016 Aug 29.
The objective is to compare the performance of dissolution-profile comparison methods when f is inadequate due to high variability. The 90% confidence region of the Mahalanobis distance and the 90% bootstrap confidence interval (CI) of the f similarity factor (f -bootstrap) were explored. A modification of the Mahalanobis distance (new D-Mahalanobis) in which those points >85% were not taken into account for calculation was also used. A population kinetic approach in NONMEM was used to simulate dissolution profiles with the first-order or Weibull kinetic models. The scenarios were designed to have clearly similar, clearly non-similar or borderline situations. Four different conditions of variability were established: high (CV = 20%) and low variability (CV = 5%) for inter-tablet (IIV) and inter-batch variability (IBV) associated to the dissolution parameters (k or MDT) using an exponential model. Forty-four (44) scenarios were simulated, considering different combinations of IIV, IBV and typical dissolution parameters. The dissolution profiles simulated using a first-order model modified the profile slope. The Weibull model allows profiles with different shapes and asymptotes and crossing each other. The results show that the f -bootstrap is the most adequate method in cases of high variability. The method based on the 90% confidence region of the Mahalanobis distance (D-Mahalanobis) is not able to detect large differences that can be detected simply with f (i.e. low specificity and positive predictive value due to false positives). The new D-Mahalanobis exhibits superior sensitivity to detect differences (i.e. specificity as a diagnostic test), but it is not as good as the f -bootstrap method.
目的是比较在因高变异性导致f值不足时,溶出曲线比较方法的性能。探索了马氏距离的90%置信区域和f相似因子的90%自抽样置信区间(f-自抽样)。还使用了一种马氏距离的修正方法(新D-马氏距离),其中计算时不考虑那些大于85%的点。在NONMEM中采用群体动力学方法,用一级或威布尔动力学模型模拟溶出曲线。设计的场景具有明显相似、明显不相似或临界情况。建立了四种不同的变异性条件:使用指数模型,与溶出参数(k或MDT)相关的片间变异性(IIV)和批间变异性(IBV)的高变异性(CV = 20%)和低变异性(CV = 5%)。考虑IIV、IBV和典型溶出参数的不同组合,模拟了44种场景。用一级模型模拟的溶出曲线改变了曲线斜率。威布尔模型允许有不同形状和渐近线且相互交叉的曲线。结果表明,在高变异性情况下,f-自抽样是最适用的方法。基于马氏距离90%置信区域的方法(D-马氏距离)无法检测到用f值能简单检测到的大差异(即由于假阳性导致特异性和阳性预测值低)。新D-马氏距离在检测差异方面表现出更高的灵敏度(即作为诊断试验的特异性),但不如f-自抽样方法。