Dutta S, Matsumoto Y, Ebling W F
Department of Pharmaceutics, School of Pharmacy, State University of New York at Buffalo, Amherst 14260.
J Pharm Sci. 1996 Feb;85(2):232-9. doi: 10.1021/js950067y.
Many drug concentration-effect relationships are described by the nonlinear sigmoid E(max) model. Clinical considerations frequently limit the magnitude of effect intensity that may be produced; the most pronounced effect intensity may be considerably below E(max). We have tested and quantified the influence of this limitation on the estimatability of the sigmoid E(max) model parameters. We have used the estimated parameter values to calculate data descriptors (drug concentrations required to produce certain effect intensities) and compared these with concentrations determined by using exact parameter values. We found that when the highest measured effect intensity was less than 95% of E(max), E(max) and EC50 were poorly estimated (high coefficient of variation and pronounced bias). Nevertheless, the fit to the data was quite good and the data descriptors were estimated with precision within the range for which data were available but not beyond. Baseline effect was estimated with good precision but the sigmoidicity parameter (gamma) was highly variable. Thus, where clinical considerations prevent determination of concentration-effect data near the maximum effect intensity, E(max) and EC50 estimations are unreliable. The use of estimable data descriptors is proposed to characterize the concentration-effect relationship under these conditions.
许多药物浓度-效应关系可用非线性S形E(max)模型来描述。临床因素常常限制了可能产生的效应强度大小；最显著的效应强度可能远低于E(max)。我们已测试并量化了这种限制对S形E(max)模型参数可估计性的影响。我们用估计的参数值来计算数据描述符（产生特定效应强度所需的药物浓度），并将这些与使用精确参数值确定的浓度进行比较。我们发现，当最高测量效应强度小于E(max)的95%时，E(max)和EC50的估计较差（变异系数高且偏差明显）。然而，对数据的拟合相当好，并且在数据可用范围内但不超出该范围的数据描述符能精确估计。基线效应估计精度良好，但S形参数（γ）变化很大。因此，在临床因素妨碍在最大效应强度附近测定浓度-效应数据的情况下，E(max)和EC50的估计不可靠。建议使用可估计的数据描述符来表征这些条件下的浓度-效应关系。
