Examining the role of metabolites in bioequivalence assessment.
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.
Karalis
Vangelis
V
Thriassio General Hospital of Elefsina, Greece. vkaralis@pharm.uoa.gr
Macheras
Panos
P
D019540:Area Under Curve; D001682:Biological Availability; D002986:Clinical Trials as Topic / Q000379:methods*; D006801:Humans; D008954:Models, Biological; D004364:Pharmaceutical Preparations / Q000378:metabolism*; D011355:Prodrugs / Q000378:metabolism; D013810:Therapeutic Equivalency
D004364:Pharmaceutical Preparations; D011355:Prodrugs