Srinivas Nuggehally R, Syed Muzeeb
J Pain Palliat Care Pharmacother. 2016;30(1):13-24. doi: 10.3109/15360288.2015.1124165. Epub 2016 Feb 11.
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.
有限的药代动力学采样策略可能有助于预测曲坦类药物的曲线下面积(AUC),并且作为一种前瞻性预测工具可能具有临床应用价值。利用适当的鼻内药代动力学数据,通过线性回归模型建立了舒马曲坦和佐米曲坦的Cmax与AUC的关系。使用已发表的平均/中位数Cmax数据和适当的回归线对AUC值进行预测。观察值与预测值的商用于计算倍数差异。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)以及AUC倍数预测的优度来评估这两种曲坦类药物。此外,1小时(舒马曲坦)和3小时(佐米曲坦)时间点的平均浓度曲线数据也用于AUC预测。Cmax与AUC模型对舒马曲坦(r = 0.9997;P < 0.001)和佐米曲坦(r = 0.9999;P < 0.001)均显示出极好的相关性。无论使用哪种曲坦类药物,使用回归模型时,大多数预测的AUC(83%-85%)的倍数差异在0.76-1.25倍之间。使用反映达峰时间(Tmax)出现的浓度数据对舒马曲坦或佐米曲坦的AUC值进行预测,结果与报告值相近。总之,Cmax与AUC模型对舒马曲坦和佐米曲坦均表现出强相关性。通过严格的统计方法确定了AUC值预测的有用性。