## 4 Discussion
Because of the reduced sample size, the subsampled datasets failed to imitate the behavior of the original data since they did not exhibit the required statistical power to show equivalence whenever this existed (Figures 7b and 8b). On the contrary, the generated data using the VAE showed increased statistical power when the data exhibited either low or high variability (Figures 7c and 8c). It should be underlined that for all low variabilities, the VAE-generated data exhibited a performance similar to the one observed with the original data, even when only a small portion of the original data was used. When the variability of the original data was low (i.e., CV = 10% or 20%), the use of one-third of the original sample through the VAE system could lead to statistical power that was only slightly less than that observed with the original data (Figure 7c). By increasing the proportion of subjects to around 50-70%, almost the same statistical power could be achieved. This attribute became even more evident in the case of highly variable data. In this situation, the VAE system not only succeeded in showing a similar performance as that for the original data but also presented even better behavior (Figure 7c). For highly variable data, the VAE could act as a noise (variability) filter and lead to increased statistical power. It should be stated that high variability (\"noise\") is an issue of paramount importance in the field of bioequivalence [4]. Highly variable drugs refer to medicines that exhibit substantial variability in their pharmacokinetic parameters, specifically their absorption, distribution, metabolism, and elimination processes, when administered to individuals. However, for highly variable drugs, achieving strict bioequivalence can be challenging due to the inherent variability in their pharmacokinetics. When conducting bioequivalence studies for such drugs, the variability between individuals' responses can lead to wider confidence intervals, making it more difficult to demonstrate equivalence within the standard regulatory requirements. As a result, regulatory authorities often have specific guidelines and acceptance criteria for highly variable drugs to account for the expected variability. These criteria could include widening the acceptable range for certain pharmacokineticparameters (e.g., Cmax), using different statistical methods to evaluate bioequivalence (e.g., scaled equivalence), or increasing the number of samples in the study [4]. Thus, it is crucial to find methods to decrease this unwanted variability and avoid increasing the number of study participants, the costs, and the complexity of the study. The good performance of the VAE-generated data was also shown in cases where the two comparison groups differed (Figure 8). Again, it was shown that the use of a VAE can mimic the performance of the original data and, in cases of high variability, result in higher statistical power (Figure 8c). These results are consistent with the existing literature in the field of highly variable drugs [4]. The findings indicate that a sufficient level of acceptable probability can be achieved even for data with high variability by utilizing only 40% of the original data for accepting equivalence. Conversely, for data with low variability, a very small proportion (i.e., 10%) of the original data can be adequate (Figure 6). The present investigation is a novel attempt to employ an artificial intelligence technique in the field of clinical trials, aiming at reducing sample size. As mentioned above, the high variability of data encountered in bioequivalence studies is a limiting factor for proving equivalence and leads to inflation in type II errors. There are two ways to tackle this problem: increase the sample size, which is followed by increases in the duration of the study, cost, complexity, and ethical concerns [5; 6], or apply computational approaches (such as the use of scaled limits) [4; 5; 6].