Department of Pharmaceutical Sciences, Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, Canada.
Department of Pharmaceutical Sciences, Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, Canada
Drug Metab Dispos. 2018 Nov;46(11):1796-1804. doi: 10.1124/dmd.118.082636. Epub 2018 Aug 22.
Drug absorption data are critical in bioequivalence comparisons, and factors such as the maximum drug concentration (C), time to achieve C (or T), as well as the area under the curve (AUC) are important metrics. It is generally accepted that the AUC is a meaningful estimate of the extent of absorption, and T or C may be used for assessing the rate of absorption. But estimation of the rate of absorption with T or C is not always feasible, as explicit solutions relating T and C to the absorption (k) and elimination rate (k) constants exist only for the one and not multicompartmental oral model. Therefore, the determination of T or C for multicompartmental models is uncertain. Here, we propose an alternate, numerical approach that uses the point-slope method for the first and second derivative(s) of the concentration-versus-time profiles and the Newton-Raphson iteration method for the determination of T and C We show that the method holds for multicompartmental oral dosing under single or steady-state conditions in the absence of known microconstants, even for flip-flop (k < ) models. Simulations showed that the C and T estimates obtained with the Newton-Raphson method were more accurate than those based on the noncompartmental, observation-based method recommended by the US Food and Drug Administration. The %Bias attributable to sampling frequency and assay error were less than those determined by the noncompartmental method, showing that the Newton-Raphson method is viable for the estimation of T and C.
药物吸收数据在生物等效性比较中至关重要,最大药物浓度 (C)、达到 C 的时间 (T) 以及曲线下面积 (AUC) 等因素都是重要的指标。通常认为 AUC 是吸收程度的有意义的估计,T 或 C 可用于评估吸收速度。但 T 或 C 用于估计吸收速度并不总是可行的,因为仅对于单室模型存在将 T 和 C 与吸收 (k) 和消除率 (k) 常数相关联的显式解,而不是多室模型。因此,多室模型的 T 或 C 的确定是不确定的。在这里,我们提出了一种替代的数值方法,该方法使用浓度-时间曲线的一阶和二阶导数的点斜率法以及牛顿-拉普森迭代法来确定 T 和 C。我们表明,该方法适用于在不存在已知微常数的情况下,单剂量或稳态条件下的多室口服给药,即使是翻转 (k < ) 模型也是如此。模拟表明,与美国食品和药物管理局推荐的非房室、基于观察的方法相比,牛顿-拉普森法得到的 C 和 T 估计值更准确。采样频率和分析误差引起的 %偏差小于非房室法确定的偏差,表明牛顿-拉普森法适用于 T 和 C 的估计。