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Choosing the calibration model in assay validation.

机译:在分析验证中选择校准模型。

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摘要

Data transformations and weighting schemes are normally used to obtain the best-fit of standard curves in bioanalysis and the calibration model is usually selected during prevalidation. In the present study, a comparison has been made between unweighted and weighted (1/x, 1/x2, and 1/square root of x) regression models with or without an intercept in achieving the best-fit for the standard curve of CDRI compound 81/470, a new anthelmintic agent, in cow milk. Validation samples in milk at the LLOQ, medium, and high concentrations were also analysed by each of the calibration models. An unweighted regression equation with an intercept overestimated the concentrations at the LLOQ. An unweighted equation without intercept and weighted equations with or without an intercept significantly minimized the bias at the LLOQ without distorting the results at higher concentrations. Hence, an unweighted equation for a straight line passing through the origin was found to be the best model for a standard curve of 81/470 in milk. Similar results were obtained for 81/470 and UMF-078 in serum and plasma, respectively. Bioanalysts should routinely test these models to obtain the best fit model for their calibration curves as part of their assay validation not during prevalidation.
机译:数据转换和加权方案通常用于获得生物分析中标准曲线的最佳拟合,并且通常在预验证期间选择校准模型。在本研究中,已对具有或没有截距的未加权和加权(1 / x,1 / x2和1 / x的平方根)回归模型进行了比较,以实现对CDRI标准曲线的最佳拟合牛奶中的一种新型驱虫剂化合物81/470。每种校准模型还分析了LLOQ,中等和高浓度的牛奶中的验证样品。带有截距的未加权回归方程高估了LLOQ的浓度。没有截距的无权方程和有截距或无截距的加权方程极大地减小了LLOQ的偏差,而不会使较高浓度下的结果失真。因此,发现通过原点的直线的未加权方程是牛奶中标准曲线为81/470的最佳模型。分别在血清和血浆中对81/470和UMF-078获得了相似的结果。生物分析人员应常规测试这些模型,以获得校准曲线的最佳拟合模型,这是其检验验证的一部分,而不是在预验证期间进行。

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