Ebling W F, Levy G
Department of Pharmaceutics, School of Pharmacy, State University of New York at Buffalo, Amherst 14260, USA.
Ann Pharmacother. 1996 Jan;30(1):12-9. doi: 10.1177/106002809603000102.
To explore and evaluate various strategies for drug concentration-and effect-controlled clinical trials, respectively, in the context of studies of population pharmacodynamics (concentration-effect relationships).
The relative utility of drug concentration- and pharmacologic effect-controlled, randomized clinical trials with two or three concentration-effect measurements for each subject has been explored by computer simulation. The basis for these simulations was a sigmoid-Emax (maximum effect) pharmacodynamic model with Emax = 100%, EC50 (drug concentrations required to produce an effective intensity of 50%) = 10 concentration units, gamma = 2, and no hysteresis. Emax and gamma were held constant whereas EC50 was assumed to be log-normally distributed with a 26% coefficient of variation of the natural lognormalized data. A smaller random variability and variability due to measurement error also were incorporated in the simulations. To explore the implications of variable and unknown Emax and gamma values, the suitability of linear and log-linear interpolation procedures for two-point concentration-effect data in different regions of the sigmoid-Emax curve was compared.
Pharmacologic effect-controlled clinical trials with 300 hypothetical subjects and targeted effect intensities of 25% and 75% yielded very good estimates of drug concentrations required to produce effect intensities of 35%, 50%, and 65%, whereas concentration-controlled trials yielded much poorer estimates. Moreover, the concentration-controlled trials, despite optimum choice of targeted concentrations, yielded a large number of data points with poor information content (effect intensities of < 15% or > 85%). Determinations based on targeted effect intensities of 25% and 75% yielded better estimates of individual EC50 values than those targeted for 25% and 50% or 50% and 75% effect intensity. Results were not significantly improved by adding a third measurement (targeted to 50% effect) to the 25% and 75% effect design. Estimations of drug concentrations required to produce an effect intensity of 50%, based on log-linear interpolation of exact concentration-effect data at 25% and 75%, yielded exact results independent of gamma value (0.5-8.0) whereas linear interpolation produced large overestimates at gamma = 0.5 or 1.0 but satisfactory estimates at gamma > or = 2.0. Similar calculations for an effect intensity of 15% based on exact concentration-effect data at 5% and 25% yielded reasonably good estimates by both methods of interpolation over a wide range of gamma values. A review of the clinical literature showed that gamma values are usually 2 or higher.
Population pharmacodynamic studies of reversibly acting drugs without pharmacodynamic hysteresis or time dependency (e.g., tolerance) can be successfully conducted using a pharmacologic effect-controlled randomized clinical trial design with only two properly selected target effect intensities per subject.
在群体药效学（浓度-效应关系）研究背景下，分别探索和评估用于药物浓度和效应控制的临床试验的各种策略。
通过计算机模拟探讨了药物浓度和药理效应控制的随机临床试验的相对效用，每个受试者进行两到三次浓度-效应测量。这些模拟的基础是一个S形Emax（最大效应）药效学模型，Emax = 100%，EC50（产生50%有效强度所需的药物浓度）= 10个浓度单位，γ = 2，且无滞后现象。Emax和γ保持恒定，而EC50假定呈对数正态分布，自然对数归一化数据的变异系数为26%。模拟中还纳入了较小的随机变异性和测量误差导致的变异性。为了探讨可变和未知的Emax和γ值的影响，比较了S形Emax曲线不同区域两点浓度-效应数据的线性和对数线性插值程序的适用性。
对300名假设受试者进行药理效应控制的临床试验，目标效应强度为25%和75%，对产生35%、50%和65%效应强度所需的药物浓度给出了非常好的估计，而浓度控制试验的估计则差得多。此外，浓度控制试验尽管对目标浓度进行了最佳选择，但产生了大量信息含量低的数据点（效应强度<15%或>85%）。基于25%和75%目标效应强度的测定比基于25%和50%或50%和75%效应强度的测定能更好地估计个体EC50值。在25%和75%效应设计中增加第三次测量（目标为50%效应），结果没有显著改善。基于25%和75%时精确浓度-效应数据的对数线性插值来估计产生50%效应强度所需的药物浓度，得到的精确结果与γ值（0.5 - 8.0）无关，而线性插值在γ = 0.5或1.0时产生大幅高估，在γ≥2.0时得到满意的估计。基于5%和25%时精确浓度-效应数据对15%效应强度进行类似计算，两种插值方法在较宽的γ值范围内都给出了相当好的估计。对临床文献的回顾表明，γ值通常为2或更高。
对于没有药效学滞后或时间依赖性（如耐受性）的可逆作用药物，群体药效学研究可以通过药理效应控制的随机临床试验设计成功进行，每个受试者仅需适当选择两个目标效应强度。
