## 4 Discussion
Given that the BE limits are 0.80-1.25 and the T/R ratios used in this study were narrower (i.e., 1.0, 1.1, and 1.2), the desired outcome would be a situation where the percentage acceptance of the generated dataset is higher than that of the original data and certainly much higher than the subsampled data from which they were created. In other words, this would imply that the utilization of AI-driven generation of virtual clinical data could lead to high statistical power by using fewer human subjects. This, in turn, could result in less human exposure to interventions, lower study costs, and faster study completion. If the statistical power of the synthesized VAE data is greater than that of the original data, then these advantages become even more pronounced. Indeed, after investigating all these scenarios, the current analysis showed that the utilization of VAE systems in a clinical trial setting succeeds in all the aforementioned properties (Figures 2-6) and offers all the advantages mentioned earlier. In all scenarios studied, it was demonstrated that the VAE system can successfully generate data superior to any subsampled group and exhibit performance at least equal to the original dataset (Figures 2-6). To clearly emphasize this advantageous performance of the VAE model, Figure 5 was constructed to illustrate that, in all cases, the VAE-generated data lead to at least equal or better performance compared to the original data. As expected, the reduced sample dataset (referred to as \"subsample\") exhibited inferior performance compared to the original dataset. The subsampled data failed to reproduce the characteristics of the original dataset, lacking the necessary statistical power to establish bioequivalence, particularly when it was present, as a result of the diminished sample size (Figures 2-4). However, what is crucial is that relying on this \"inferior\" dataset (i.e., the \"subsampled\") and applying the VAE model to it leads to synthesized data that exhibit performance at least equal to the original whole data. In certain instances, the efficiency of the data generated by the VAE is notably superior, even compared to the original data. For example, with low within-variability (15%) and when the mean endpoints of the reference and test groups were the same (Figure 5A), the data generated by the VAE exhibited comparable performance to the original dataset, even when only a small portion of the original data was utilized (i.e., 25%). In other words, only a few actual human subjects were included in the study. Generating even more data than the original will lead to an even higher acceptance rate (Figure 6). The VAE-generated data performed well, even in cases of high variability and when the mean endpoints between the groups differed by 20%, resulting in an increase in statistical power (Figures 3 and 4). It was demonstrated that an increase of up to 40% in statistical power can be achieved when the mean endpoints of the two groups have a 20% difference, even with a 25% subsample proportion, namely, when only a few actual volunteers will participate in the trial (Figure 5C). It should be underlined that high variability poses a significant challenge in the field of BE assessment [4; 5; 6]. However, achieving strict BE for such drugs can be daunting due to their inherent pharmacokinetic variability. As a result, regulatory bodies have developed particular guidelines and criteria for the acceptance of highly variable drugs to address the expected variability [6; 16]. Therefore, employing methods that mitigate unwanted variability without necessitating an increase in the number of study participants, costs, or study complexity becomes paramount. In recent years, the emergence of machine learning and deep learning methodologies has allowed us to re-examine old problems in science with a fresh perspective and innovative tools. Recently, machine learning approaches were used to address the challenge of finding a suitable pharmacokinetic metric for expressing the rate of drug absorption [17; 18].