Kalns J E, Millenbaugh N J, Wientjes M G, Au J L
Division of Pharmaceutics and Pharmaceutical Chemistry, College of Pharmacy, Ohio State University, Columbus 43210, USA.
Cancer Res. 1995 Nov 15;55(22):5315-22.
The relationship between drug concentration (C), exposure time (t), and the resulting effect (h) for a chemotherapeutic agent is expressed as Cn x t = h. The value of n, derived from curve fitting of the C versus t plot, indicates the relative importance of concentration and exposure time. The selection of concentrations and exposure times in a pharmacodynamic experiment may affect the precision and accuracy of parameter estimation. The use of optimal designs is even more critical when the numbers of experimental conditions are limited by tumor availability (e.g., small size of surgical specimens from patients). The present study used computer-simulated data to define the most efficient in vitro pharmacodynamic experimental designs and the optimal method of pharmacodynamic data analysis. All studies used Monte Carlo simulations to compare designs with varying numbers of drug concentrations, exposure times, and replications. For each selected design, 50-100 error-containing data sets were created by addition of experience-based random errors to expected concentration-response profiles. To compare methods of data analysis, the same 1250 simulated data sets were analyzed by two methods (i.e., surface response method and traditional method). The results showed that simultaneous fitting of drug effect at all concentrations and all exposure times by the surface response method yielded n estimates that had greater precision and accuracy than a traditional method that required sequential determination of the effective inhibitory concentration (e.g., IC50) and then the n value using the IC50 at different exposure times. Subsequent studies were analyzed using the surface response method. To evaluate the effect of selection of concentrations and exposure times on the precision of n estimation, between 1100 and 2200 simulated data sets, with 400 observations per data set, were generated using different exposure times and drug concentrations. Because the number of observations was limited to 400, the number of replications at each condition varied depending on the total number of selected conditions.(ABSTRACT TRUNCATED AT 400 WORDS)
化疗药物的药物浓度（C）、暴露时间（t）与所产生的效应（h）之间的关系表示为Cⁿ×t = h。通过C对t的曲线拟合得出的n值表明了浓度和暴露时间的相对重要性。药效学实验中浓度和暴露时间的选择可能会影响参数估计的精度和准确性。当实验条件的数量受肿瘤可获得性限制时（例如，患者手术标本尺寸较小），使用最优设计更为关键。本研究使用计算机模拟数据来确定最有效的体外药效学实验设计以及药效学数据分析的最优方法。所有研究都使用蒙特卡罗模拟来比较具有不同药物浓度、暴露时间和重复次数的设计。对于每个选定的设计，通过向预期的浓度 - 反应曲线添加基于经验的随机误差来创建50 - 100个包含误差的数据集。为了比较数据分析方法，用两种方法（即表面响应法和传统方法）分析相同的1250个模拟数据集。结果表明，与传统方法相比，表面响应法对所有浓度和所有暴露时间的药物效应进行同时拟合得出的n估计值具有更高的精度和准确性，传统方法需要顺序确定有效抑制浓度（例如，IC50），然后使用不同暴露时间下的IC50来确定n值。后续研究使用表面响应法进行分析。为了评估浓度和暴露时间的选择对n估计精度的影响，使用不同的暴露时间和药物浓度生成了1100至2200个模拟数据集，每个数据集有400个观测值。由于观测值数量限制为400，每个条件下的重复次数根据所选条件的总数而有所不同。（摘要截断于400字）
