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Methodological comparison of in vitro binding parameter estimation: sequential vs. simultaneous non-linear regression.

机译:体外结合参数估计的方法学比较:顺序非线性回归与同时非线性回归。

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PURPOSE: Analysis of simulated data was compared using sequential (NLR) and simultaneous non-linear regression (SNLR) to evaluate precision and accuracy of ligand binding parameter estimation. METHODS: Commonly encountered experimental error, specifically residual error of binding measurements (RE), experiment-to-experiment variability (BEV) and non-specific binding (B(NS)), were examined for impact of parameter estimation using both methods. Data from equilibrium, dissociation, association and non-specific binding experiments were fit simultaneously (SNLR) using NONMEM VI compared to the common practice of analyzing data from each experiment separately and assigning these as exact values (NLR) for estimation of the subsequent parameters. RESULTS: The greatest contributing factor to bias and variability in parameter estimation was RE of the measured concentrations of ligand bound; however, SNLR provided more accurate and less bias estimates. Subtraction of B(NS) from total ligand binding data provided poor estimation of specific ligand binding parameters using both NLR and SNLR. Additional methods examined demonstrated that the use of SNLR provided better estimation of specific binding parameters, whereas there was considerable bias using NLR. NLR cannot account for BEV, whereas SNLR can provide approximate estimates of BEV. CONCLUSION: SNLR provided superior resolution of parameter estimation in both precision and accuracy compared to NLR.
机译:目的:使用顺序(NLR)和同时非线性回归(SNLR)对模拟数据进行分析,以评估配体结合参数估计的准确性和准确性。方法:使用这两种方法,检查了常见的实验误差,特别是结合测量的残余误差(RE),实验到实验的变异性(BEV)和非特异性结合(B(NS)),以评估参数估计的影响。与使用NONMEM VI同时拟合(SNLR)来自平衡,解离,缔合和非特异性结合实验的数据,相比之下,通常的做法是分别分析每个实验的数据并将其分配为精确值(NLR),以估计后续参数。结果:参数估计中影响偏差和变异性的最大因素是配体结合浓度的RE。但是,SNLR提供了更准确和更少的偏差估计。从总配体结合数据中减去B(NS)不能同时使用NLR和SNLR估算特定的配体结合参数。检验的其他方法表明,使用SNLR可以更好地估计特定的结合参数,而使用NLR则存在很大的偏差。 NLR无法解释BEV,而SNLR可以提供BEV的近似估算。结论:与NLR相比,SNLR在参数估计和精度方面均提供了出色的分辨率。

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