On the Interplay between Machine Learning, Population Pharmacokinetics, and Bioequivalence to Introduce Average Slope as a New Measure for Absorption Rate

Abstract

The scientific basis for demonstrating bioequivalence between two drug products relies on the comparison of their extent and rate of absorption. For the absorption extent, the area under the C-t curve (AUCt) is used without a doubt. For absorption rate, the maximum observed plasma concentration (Cmax) is still suggested by the authorities, despite the numerous concerns. In this study, the concept of average slope (AS) is introduced as a metric to express the absorption rate of drugs. Principal component analysis and random forest models were applied to actual and simulated two × two crossover bioequivalence studies to show that AS expresses the appropriate properties for characterizing absorption rate. Several absorption kinetics (slow, typical, fast) and sampling schemes (sparse, typical, dense) were simulated. The two machine learning algorithms, applied to all these scenarios, proved the desired properties of AS while showing the non-desired performances of other metrics currently used or proposed in the literature. The estimation of AS does not require any assumptions, models, or transformations and is as simple as that of AUCt. A modified version of AS, termed “weighted AS”, is also introduced in order to place emphasis on early time points where the C-t profile describes more clearly the absorption process.