Thriassio General Hospital of Elefsina, Greece.
J Pharm Pharm Sci. 2010;13(2):198-217. doi: 10.18433/j35889.
Investigate the role of metabolites in bioequivalence (BE) assessment. METHODS. Sets of ordinary differential equations are used to generate concentration - time data for both parent drug (P) and metabolite (M). The calculations include 24 subjects, two different formulations (Test, Reference), and a range of Test/Reference ratios for the fraction of dose absorbed and the rate of absorption. A summarized view of these results is made through the construction of three dimensional power curves. The criteria for the choice of the preferred analyte (P or M) are based on a sensitivity analysis of the bioequivalence measure (AUC, Cmax). The latter depends on the relative ability of P and M to reflect better the changes of the pharmacokinetic parameters and variability. RESULTS. The different sensitivity properties of P and M were reflected on the power curves. For AUC, the performance of metabolite is very similar to that of the parent drug for all scenarios and models examined. A more complex behaviour is evident for Cmax. In most of these cases, metabolite data show higher permissiveness in the percentages of acceptance. This attribute is more evident when P exhibits high elimination rate and/or the formation of M occurs rapidly. When the Test and Reference products have similar absorption profiles, metabolite data are preferable for the determination of bioequivalence. Parent drug has the advantage for detecting better the differences in the absorption rate of two drugs. The latter is counterbalanced by the increased sensitivity of P data to the variability of the data. CONCLUSIONS. Both parent drug and metabolite share the same ability to declare BE when AUC is used as a bioequivalence measure. In case of Cmax, metabolite data exhibit better performance when the T and R products are truly bioequivalent or the two formulations differ in their extent of absorption. Parent drug data are more sensitive to detect differences in the rate of absorption. However, in such cases, their information is much influenced by the increased variability.
研究代谢物在生物等效性(BE)评估中的作用。方法。使用常微分方程(ODE)方程组生成母体药物(P)和代谢物(M)的浓度-时间数据。计算包括 24 名受试者、两种不同配方(测试、参考)以及一系列吸收分数和吸收速率的测试/参考比值。通过构建三维幂曲线对这些结果进行总结。首选分析物(P 或 M)的选择标准基于生物等效性测量(AUC、Cmax)的敏感性分析。后者取决于 P 和 M 更好地反映药代动力学参数和变异性变化的相对能力。结果。P 和 M 的不同敏感性特性反映在幂曲线上。对于 AUC,对于所有检查的场景和模型,代谢物的性能与母体药物非常相似。对于 Cmax,表现出更复杂的行为。在大多数情况下,代谢物数据在接受百分比方面显示出更高的宽容度。当 P 表现出高消除率和/或 M 快速形成时,这种特性更为明显。当测试和参考产品具有相似的吸收曲线时,代谢物数据更适合用于确定生物等效性。母体药物在检测两种药物吸收速率差异方面具有优势。而 P 数据对数据变异性的敏感性增加则弥补了这一优势。结论。当 AUC 用作生物等效性测量时,母体药物和代谢物都具有相同的能力来声明 BE。在 Cmax 的情况下,当 T 和 R 产品真正等效或两种配方在吸收程度上存在差异时,代谢物数据的性能更好。母体药物数据对检测吸收速率差异更敏感。然而,在这种情况下,它们的信息受到变异性增加的很大影响。