## 1 Introduction  
Two medicinal products are considered bioequivalent if their concentration-time (C-t) profiles are sufficiently similar to provide comparable clinical performance [1]. Formally, a test (T) product is considered bioequivalent to the original (Reference, R) product if it contains the same active ingredient and there are no significant variations in the rate and amount of absorption when consumed at the same molar dose as the R formulation [2, 3]. The peak plasma concentration (Cmax) is traditionally used to reflect the rate of absorption, whereas the area under the concentration-time curve from time zero to the time of the last measurable concentration (AUCt) is utilized to describe the extent of absorption [1, 2, 3]. Other pharmacokinetic (PK) indicators are also used to provide further information. In the case of US-FDA, the area under the plasma time-concentration curve extrapolated to infinity (AUCinf) is additionally used as a primary endpoint for total exposure [2]. Other measures include the time (Tmax) at which Cmax occurs and the terminal slope (lambda) of the time-concentration curve [4, 5, 6, 7, 8]. For AUCt, there are no reservations about its use as a measure of the extent of absorption. However, despite the fact that Cmax is widely employed as a measure of absorption rate in bioequivalence (BE) assessments, it has been criticized as a metric indicating the extent of absorption in addition to the rate of absorption [9; 10]. Another limitation of Cmax is due to the fact that concentration measurements are only obtained at discrete time periods; thus, the direct determination of Cmax can be sensitive to poor sampling tactics. Several studies have questioned the use of Cmax as an absorption rate measure [11; 12; 13; 14; 15]. To address some of the Cmax inadequacies, other pharmacokinetic measures such as the Cmax/AUC ratio, Tmax, intercept and modified intercept approaches, as well as partial AUCs, have been proposed [10; 11; 16; 17; 18; 19]. Even though that substantial research has been done on other aspects of BE assessment (e.g., statistical framework, clinical designs), Cmax is still being used in BE studies all around the world. Computational approaches have greatly contributed to bioequivalence, and many of the regulatory guidelines (e.g., highly variable drugs, two-stage designs, etc.) have been built through the implementation of modeling and simulation methods [20]. Machine learning (ML) is a recent tool that is used in many different areas of research [21; 22]. Models can be created from sample data using ML to automate decision-making processes based on data inputs. ML approaches are frequently classified into broad categories based on how the system learns or receives feedback on what it learns. The most common ML methods are \"supervised learning\", which teaches algorithms using labeled input and output data, \"unsupervised learning\", in which the algorithm is not given labeled data and must discover structure in the input data; and \"reinforcement learning\", where a desired behavior is rewarded while an undesired is punished [21; 22]. In a recent study, machine learning approaches were used to explore the relationships among the PK parameters and identify the most suitable metric for absorption rate [23]. Among the PK metrics explored, a new metric, the Cmax/Tmax ratio, was investigated together with the existing measures for their usefulness in characterizing absorption rate. The machine learning analysis showed that the metric best reflecting the rate of absorption was Cmax/Tmax [23]. The latter better reflected the absorption rate, regardless of the absolute kinetic properties of absorption. Since machine learning offers new ways of investigating BE, it was further used in this analysis to define a new metric for an absorption rate.