## 1 Introduction  
The goal of bioequivalence (BE) testing is to determine the in vivo \"equivalence\" of two drug products containing the same active moiety, namely the test (T) and the innovator's formulation of the same active substance known as the reference (R) product [1,2]. In turn, showing bioequivalence is needed to make sure that the therapeutic effects of the two products under consideration are the same. The idea behind bioequivalence is that a product's therapeutic profile is a function of the concentration of the active ingredient in the effect site, which relies on the concentration of drug in the general circulation. Thus, two drugs (T and R) are termed bioequivalent if their time-concentration profiles are similar enough to assure comparable clinical performance [3]. Formally, a T product is considered bioequivalent to the original R product if it has the same active ingredient and, when taken at the same molar dose, there are no significant differences in the rate and amount of absorption compared to the R formulation [1,2]. To standardize and clarify the BE assessment procedure, regulatory health agencies around the world have issued many guidelines with a focus on pharmacokinetic (PK) issues, such as the type of PK parameters to be estimated, statistical analysis, and the selection of the appropriate clinical design that will allow the reliable estimation of the PK parameters [4]. In the case of immediate-release formulations, it is now widely accepted that the rate of absorption should be assessed by the peak plasma concentration (Cmax), whereas the extent of absorption is expressed by the area under the time-concentration curve (AUC)from time zero to the last sampling point [1; 2]. Other PK metrics are also utilized to provide further information. These include the area under the plasma time-concentration curve extrapolated to infinity (AUCinf), the time (Tmax) at which Cmax occurs, and the time-concentration curve's terminal slope (lambda). Other PK characteristics, such as the highest and lowest drug concentrations at steady state, the AUC at each interval of administration, and the peak-to-trough fluctuation, are used for modified release products [4; 5; 6; 7; 8; 9; 10]. Even though Cmax is routinely used in BE assessment worldwide, it has been questioned as a metric that expresses the extent of absorption in addition to the rate of absorption [6; 11; 12]. In this vein, several studies [13; 14; 15; 16; 17] have revealed similar problems concerning the use of Cmax as a parameter of absorption rate. Other pharmacokinetic metrics, such as the Cmax/AUC ratio, Tmax, and partial AUCs, have been proposed to address some of the shortcomings of Cmax [12; 14; 18]. However, even though much work has been done towards the statistical assessment (e.g., scaled bioequivalence limits) and the clinical designs (e.g., replicate, two-stage adaptive), the problematic Cmax is still used, and the inherent problems that it carries seem to have been neglected. This means that despite the fact that in BE assessment there is a detailed and carefully specified framework, the rate of absorption is not actually evaluated. The use of machine learning (ML) approaches, a subfield of artificial intelligence, is a useful tool for addressing this question [19; 20]. ML techniques enable data to be trained, followed by statistical analysis to provide values that fall inside a particular range. ML enables the creation of models from sample data in order to automate decision-making processes based on data inputs. ML tasks are often divided into broad categories based on how the system learns or receives feedback on what it learns. The two most common ML methods are \"supervised learning,\" in which algorithms are taught using labeled input and output data, and \"unsupervised learning,\" in which the algorithm is not given labeled data and must identify structure in the input data [19; 20].