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A statistical method for the determination of absorption rate constant estimated using the rat single pass intestinal perfusion model and multiple linear regression.


Sutton, S.C., Rinaldi, M.T.S., McCarthy, J.M. and Vukovinsky, K.E.

Journal of Pharmaceutical Sciences, 91(4), 1046-1053 (2002).

The guide "Waiver of In Vivo Bioavailability and Bioequivalence Studies for Immediate Release Solid Dosage Forms Containing Certain Active Moieties/Active Ingredients Based on a Biopharmaceutical Classification System" (Rockville, MD: CDER, 2000) outlined non-in vivo tests of permeability that may satisfy the classification of a compound in the biopharmaceutical classification system. However, absent from that document were specific statistical methods to legitimatize the non-in vivo tests. This report describes the appropriate statistical treatment of absorption data, and recommends its adoption in the estimation of absorption and/or permeability measurements. The calculation of the absorption rate constants (k(a)) of ten compounds by a new multiple linear regression (MLR) method was completed after the separate perfusion of each compound through the rat single pass intestinal perfusion model (n = 3 rats per compound). Studentized residuals were evaluated to determine whether any statistically significant outliers were present in the data. The standard error of k(a) was estimated using variance components from the random effects model. The results were compared with the "traditional method" for k(a) calculations. Although both methods produced similar values of k(a), the MLR method's error estimate included multiple components of variability, which was largely ignored by the traditional method. The MLR method provided objective tests for outliers and achievement of steady-state. A preferred method for the statistical analysis of absorption data was demonstrated. These methods should be applied to all forms of permeability measurements, especially the non-in vivo measurements that classify a compound in the biopharmaceutical classification system.