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首页> 外文期刊>Journal of pain & palliative care pharmacotherapy >Intranasal Pharmacokinetic Data for Triptans Such as Sumatriptan and Zolmitriptan Can Render Area Under the Curve (AUC) Predictions for the Oral Route: Strategy Development and Application.
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Intranasal Pharmacokinetic Data for Triptans Such as Sumatriptan and Zolmitriptan Can Render Area Under the Curve (AUC) Predictions for the Oral Route: Strategy Development and Application.

机译:曲普坦(如舒马曲坦和佐米曲普坦)的鼻内药代动力学数据可以绘制口服途径的曲线(AUC)预测下的面积:策略的开发和应用。

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摘要

Limited pharmacokinetic sampling strategy may be useful for predicting the area under the curve (AUC) for triptans and may have clinical utility as a prospective tool for prediction. Using appropriate intranasal pharmacokinetic data, a Cmax vs. AUC relationship was established by linear regression models for sumatriptan and zolmitriptan. The predictions of the AUC values were performed using published mean/median Cmax data and appropriate regression lines. The quotient of observed and predicted values rendered fold-difference calculation. The mean absolute error (MAE), mean positive error (MPE), mean negative error (MNE), root mean square error (RMSE), correlation coefficient (r), and the goodness of the AUC fold prediction were used to evaluate the two triptans. Also, data from the mean concentration profiles at time points of 1?hour (sumatriptan) and 3?hours (zolmitriptan) were used for the AUC prediction. The Cmax vs. AUC models displayed excellent correlation for both sumatriptan (r = .9997; P < .001) and zolmitriptan (r = .9999; P < .001). Irrespective of the two triptans, the majority of the predicted AUCs (83%-85%) were within 0.76-1.25-fold difference using the regression model. The prediction of AUC values for sumatriptan or zolmitriptan using the concentration data that reflected the Tmax occurrence were in the proximity of the reported values. In summary, the Cmax vs. AUC models exhibited strong correlations for sumatriptan and zolmitriptan. The usefulness of the prediction of the AUC values was established by a rigorous statistical approach.
机译:有限的药代动力学采样策略可能有助于预测曲普坦的曲线下面积(AUC),并且可能具有临床实用性作为预测的前瞻性工具。使用适当的鼻内药代动力学数据,通过舒马曲坦和佐米曲普坦的线性回归模型建立了Cmax与AUC的关系。 AUC值的预测是使用已发布的均值/中值Cmax数据和适当的回归线进行的。观察值和预测值的商表示倍数差计算。使用平均绝对误差(MAE),平均正误差(MPE),平均负误差(MNE),均方根误差(RMSE),相关系数(r)和AUC倍数预测的优度来评估这两者曲普坦。同样,在1?小时(舒马曲坦)和3?小时(佐米曲普坦)时间点的平均浓度分布数据也用于AUC预测。 Cmax对AUC模型对舒马曲坦(r = .9997; P <.001)和佐米曲普坦(r = .9999; P <.001)均显示出极好的相关性。无论使用哪种曲普坦,使用回归模型预测的大多数AUC(83%-85%)的差异都在0.76-1.25倍之内。使用反映Tmax发生的浓度数据来预测舒马曲坦或佐米曲普坦的AUC值接近所报告的值。总之,舒马曲坦和佐米曲普坦的Cmax与AUC模型表现出很强的相关性。通过严格的统计方法确定了预测AUC值的有用性。

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