Abstract

Despite ongoing concerns, the primary metric utilized in bioequivalence studies to quantify absorption rate remains the maximum plasma concentration (Cmax). To more accurately depict absorption rate, the concept of “average slope” (AS) has been recently introduced. The objective of this study is to elucidate and compare the characteristics of AS and Cmax in their representation of the drug-absorption rate. For this purpose, an investigation was conducted on five drugs (nintedanib, methylphenidate, nitrofurantoin, lisdexamfetamine, and theophylline) with different absorption and disposition kinetics. The properties of AS and Cmax, as well as their correlations with other pharmacokinetic parameters, were assessed using supervised and unsupervised machine-learning algorithms, namely principal component analysis, random forest, hierarchical cluster analysis, and artificial neural networks. This study showed that, regardless of the absorption kinetics and across every ML algorithm, AS was more sensitive in reflecting the absorption rate compared to Cmax. In all drugs and methods of analysis, AS demonstrated significantly superior performance in expressing the absorption rate compared to Cmax. The joint use of different techniques complemented each other and verified the findings. Moreover, AS can be easily calculated and has the appropriate units and properties to be used as a metric to express the absorption rate in bioequivalence studies. The adoption of AS by regulatory authorities, as an absorption-rate metric, could significantly improve the accuracy and reliability of BE assessments. Overall, this study focused on addressing the longstanding problem of finding an appropriate absorption-rate metric by demonstrating the desirable properties of AS.

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