## 4 Discussion
The traditional way in pharmacokinetics to explore the properties of a pharmacokinetic parameter is to find its relationships with the bioavailable fraction and absorption rate constant assuming a certain type of kinetics (e.g., a one-compartment model with first-order absorption and elimination) and using simple simulations. In this study, for the first time, ML algorithms are applied instead of a simple simulation exercise, and all pharmacokinetic features are explored together. The PCA analysis identifies proximities between PK characteristics, whereas the RF analysis identifies the nature of each parameter, specifically whether it is influenced by the extent or rate of absorption. In addition, a new metric, the ratio Cmax/Tmax, is introduced and explored together with the traditional PK parameters. We assess the contribution of PK characteristics to each one of the potential absorption rate metrics (i.e., Cmax, Cmax/AUC, and Cmax/Tmax). In this analysis, ML techniques were applied to two different types of data (Figure 1): (a) actual data on donepezil obtained from a 2 \(\times\) 2 BE study in 26 volunteers, and (b) simulated donepezil BE data assuming a slower (0.5x-), similar (1x-), and faster (2x-) absorption rate. An overview of the analyses taken in this study and the main findings are summarized in Table 3. Initially, it was shown, as was expected, that AUC and AUCinf  
In order to validate the findings from the analysis of actual BE data, simulations were also performed using scenarios with faster or slower absorption rates than those originally estimated from the donepezil BE data. As a result, three donepezil BE datasets were simulated, with N = 26 for each study. All pharmacokinetic properties of donepezil were kept unaltered, except for the absorption rate, and NCA was applied to each simulated dataset (Table 3). The impact of absorption kinetics on the choice of the most suitable PK metric was assessed. The application of PCA to each of the three simulated datasets verified the findings identified in the case of actual BE data (Figure 6). Especially for the simulations in the \"1x-\" case (Figure 6b), the obtained results are quite similar to those derived from analyzing the actual dataset (Figure 2). Moreover, it was possible to observe that as absorption kinetics became faster, the relationship between AUC (or AUCinf) and the three other metrics (Cmax, Cmax/AUC, and Cmax/Tmax) became less strong (Table 3 and Figure 6). The use of the RF algorithm (Figure 7) verified the findings found in the simple case of actual donepezil data. In addition, RF uncovered the pattern where the contribution of Tmax into Cmax or Cmax/AUC becomes less as absorption kinetics get faster. Similarly, the contribution of Tmax to Cmax/Tmax remains the predominant characteristic under all kinetic conditions. It should be stated that for the needs of this study, a population PK model of donepezil was developed as an intermediate step (Table 2, Figure 4). Even though the development of a population PK model was not the primary goal of this study, it was necessary in order to simulate BE datasets with different absorption kinetic characteristics and provide additional evidence. The use of ML approaches in the actual data allowed identifying the relationships among the PK variables and the contribution of each one to another. However, the additional use of simulated BE datasets offered the opportunity to verify the previous results and explore the role of absorption rate. Because the donepezil BE data were already available, the rationale way was to create a population model characterizing donepezil kinetics (Table 2, Figure 4) and then use this model to simulate different absorption conditions. This method has the advantage of retaining all donepezil PK parameters from the original dataset in the simulated datasets, whereas only the absorption rate constant was changed. As a result, the absorption rate's impact could be isolated and assessed in the ML analysis.