## 3 Results
### Relationships among the PK Variables, under Different Absorption Kinetic Conditions
#### 3.2.2. Simulate Different Absorption Kinetics  
In order to explore how absorption rate affects the above-mentioned findings, simulated BE datasets (2 \(\times\) 2 crossover, with N = 26 subjects each) were additionally used. Based on the population estimates and all variables derived from the NLME analysis, three conditions were simulated: (a) slower absorption by adjusting the mean Ka to be half of the original (0.5x-), (b) equal to the original Ka (i.e., 1x-), and (c) faster absorption by adjusting  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters (Units)** & **Estimate** & **Standard Error** & **Relative Standard Error (\%)** \\ \hline \multicolumn{4}{c}{Fixed Effects} \\ \hline Ka (h\({}^{-1}\)) & 0.18 & 0.01 & 6.2 \\ \hline Cl/F (mL/h) & 14,466.77 & 785.84 & 5.43 \\ \hline V1/F (mL) & 44,386.59 & 9454.34 & 21.3 \\ \hline Q/F (mL/h) & 80,120.11 & 5805.65 & 7.25 \\ \hline V2/F (mL) & 905,886.42 & 59,624.17 & 6.58 \\ \hline \multicolumn{4}{c}{Random Effects} \\ \hline omega\_Tlag & 0.01 & 0 & 22.5 \\ \hline omega\_ka & 0.07 & 0.01 & 18.2 \\ \hline omega\_Cl & 0.27 & 0.04 & 14.6 \\ \hline omega\_V1 & 1.16 & 0.18 & 15.5 \\ \hline omega\_Q & 0.25 & 0.05 & 20.1 \\ \hline omega\_V2 & 0.31 & 0.05 & 16.4 \\ \hline \multicolumn{4}{c}{Error Model Parameters} \\ \hline a & 0.17 & 0.02 & 12.5 \\ \hline b & 0.22 & 0.01 & 3.77 \\ \hline \end{tabular}  
Ka: absorption rate constant, F: bioavailable fraction of dose, Cl/F: apparent clearance, V1/F: apparent volume of distribution of the central compartment, V2/F: apparent volume of distribution of the peripheral compartment, Q/F: apparent inter-compartment clearance, omega: between-subject variability for each pharmacokinetic parameter, a: additive component of the residual error model, b: proportional component of the error model.  
\end{table}
Table 2: Parameter estimates of the final population pharmacokinetic model of donepezil.  
Figure 4: Goodness of fit plots for the finally selected population pharmacokinetic model for donepezil. (**a**) Visual predictive check plot where the blue lines show the 10 th, 50 th, and 90 th percentiles of the empirical data, while the shaded areas represent the anticipated 90% confidence intervals. A number of 1000 Monte Carlo simulations were utilized. (**b**) Individual observed vs. predicted individual concentrations of donepezil. Closed circles refer to (predicted, observed) pairs, solid lines denote the ideal condition of unity (i.e., y = x), and dotted lines represent the 90% prediction interval.  
the mean ka to be twice the original (2x-). As a result, three donepezil BE datasets were simulated. The simulated donepezil C-t data for these three datasets are illustrated in Figure 5.  
Using the simulated C-t data, the PK parameters (Cmax, Tmax, AUC, AUCinf, lambda, Cmax/AUC, Cmax/Tmax) were estimated using non-compartmental approaches. Then, the analysis continued by re-applying the two ML algorithms (PCA and RF) to each BE dataset. Thus, it was possible to study the effect of the absorption rate directly on the relationship among the PK parameters and how each one of them is influenced by absorption rate.