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Prospective evaluation of a model for the prediction of milk:plasma drug concentrations from physicochemical characteristics.

机译:根据理化特征​​预测牛奶:血浆药物浓度的模型的前瞻性评估。

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摘要

1. Milk:plasma (M/P) drug concentration ratios predicted by a model utilizing pKa, plasma protein binding and octanol:water partition coefficients have been compared with actual M/P values for 10 basic drugs. 2. There was a close relationship between predicted and observed M/P ratios with a coefficient of determination r2 of 0.97. However, there was a proportional error. 3. The data were transformed by taking logs of predicted and observed (M/P + 1) values. Regression analysis resulted in an r2 of 0.95, an intercept on the Y-axis not significantly different from zero and a slope not significantly different from one. 4. The 95% confidence interval around a single prediction revealed an error between 150% for the lowest and 23% for the highest M/P ratios. The error is therefore lowest for the drugs likely to have the greatest transfer into milk. 5. There was no significant bias in the predictions. 6. The model was refined by multiple linear regression analysis utilising the observed M/P ratios for the 10 basic drugs in addition to those of the original drugs. The revised equation resulted in an improvement in the explained variance. 7. Protein binding was the most important single predictor. 8. The results confirm that M/P ratios for basic drugs can be predicted accurately from their physicochemical characteristics.
机译:1.通过使用pKa,血浆蛋白结合和辛醇:水分配系数的模型预测的牛奶:血浆(M / P)药物浓度比已与10种基本药物的实际M / P值进行了比较。 2.预测的M / P比与观察到的M / P比之间存在密切关系,测定系数r2为0.97。但是,存在比例误差。 3.通过获取预测值和观察值(M / P +1)的对数来转换数据。回归分析得出r2为0.95,Y轴上的截距与零没有显着差异,斜率与1没有显着差异。 4.围绕单个预测的95%置信区间显示,最低M / P比为150%,最高M / P比为23%。因此,对于可能最大程度地转移到牛奶中的药物来说,误差最低。 5.这些预测没有明显的偏差。 6.利用多元线性回归分析对模型进行了改进,该线性回归分析利用了观察到的10种基本药物和原始药物的M / P比值。修改后的方程式使所解释的方差有所改善。 7.蛋白质结合是最重要的单一预测因子​​。 8.结果证实基本药物的M / P比可以根据其理化特性准确预测。

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