Findlay John W A, Dillard Robert F
Pharmacokinetics, Dynamics, and Metabolism, Pfizer Global Research and Development, Groton, CT, USA.
AAPS J. 2007 Jun 29;9(2):E260-7. doi: 10.1208/aapsj0902029.
Calibration curves for ligand binding assays are generally characterized by a nonlinear relationship between the mean response and the analyte concentration. Typically, the response exhibits a sigmoidal relationship with concentration. The currently accepted reference model for these calibration curves is the 4-parameter logistic (4-PL) model, which optimizes accuracy and precision over the maximum usable calibration range. Incorporation of weighting into the model requires additional effort but generally results in improved calibration curve performance. For calibration curves with some asymmetry, introduction of a fifth parameter (5-PL) may further improve the goodness of fit of the experimental data to the algorithm. Alternative models should be used with caution and with knowledge of the accuracy and precision performance of the model across the entire calibration range, but particularly at upper and lower analyte concentration areas, where the 4- and 5-PL algorithms generally outperform alternative models. Several assay design parameters, such as placement of calibrator concentrations across the selected range and assay layout on multiwell plates, should be considered, to enable optimal application of the 4- or 5-PL model. The fit of the experimental data to the model should be evaluated by assessment of agreement of nominal and model-predicted data for calibrators.
配体结合分析的校准曲线通常以平均响应与分析物浓度之间的非线性关系为特征。通常，响应与浓度呈现S形关系。目前公认的这些校准曲线参考模型是四参数逻辑（4-PL）模型，该模型在最大可用校准范围内优化了准确性和精密度。将加权纳入模型需要额外的工作，但通常会提高校准曲线的性能。对于具有一定不对称性的校准曲线，引入第五个参数（5-PL）可能会进一步提高实验数据与算法的拟合优度。使用替代模型时应谨慎，并了解该模型在整个校准范围内的准确性和精密度性能，尤其是在分析物浓度的高低区域，4-PL和5-PL算法通常优于替代模型。应考虑几个分析设计参数，例如校准物浓度在选定范围内的设置以及多孔板上的分析布局，以便能够最佳应用4-PL或5-PL模型。应通过评估校准物的标称数据与模型预测数据的一致性来评估实验数据与模型的拟合情况。
