## 4 Discussion  
The aim of this study is to expand the application of a generative AI algorithm (in particular, VAEs) in the BE testing of highly variable drugs. High variability (\"noise\") is a critical issue in the field of bioequivalence [1; 2; 3]. Highly variable drugs are medications that demonstrate considerable variability in their pharmacokinetic parameters, such as absorption, distribution, metabolism, and elimination processes, upon administration to individuals. Achieving strict bioequivalence for these drugs can be challenging due to their inherent pharmacokinetic variability. When conducting BE studies for such drugs,  
Figure 8: Acceptance gains between the VAE-generated and the original datasets when no scaling is applied to either dataset. Three different original sample sizes are used (24 (**A**), 48 (**B**), and 72 (**C**)), while the size of the generated datasets is equal (1\(\times\)) to that of the original sample. In all cases, the average test/reference ratio is 1:1 and the within-subject variability equals 20%, 40%, and 60%. the variability in individual responses can result in broader confidence intervals, making it more challenging to demonstrate equivalence within standard regulatory requirements. Thus, regulatory authorities often establish specific guidelines and acceptance criteria for highly variable drugs to accommodate this expected variability [1,2]. These criteria may involve widening acceptable ranges for certain pharmacokinetic parameters (e.g., Cmax), utilizing alternative statistical methods for assessing bioequivalence (e.g., scaled equivalence), or increasing the sample size in studies [3]. Therefore, it is crucial to develop methods that reduce this undesired variability to avoid the necessity of increasing study participants, costs, and study complexity. The rationale for investigating VAEs in highly variable drugs lies in their inherent property of reducing variability. To demonstrate the utility of VAEs in reducing the need for large sample sizes, Monte Carlo BE studies were simulated (5000 trials under each scenario) with and without the use of VAEs, and the statistical power in each case was recorded [3]. The performance of VAEs in the BE of highly variable drugs was compared against the classic 80-125% acceptance range and the scaled BE limits proposed by the US FDA and EMA [1,2]. Various scenarios were explored, focusing on high-variability values of the drugs under comparison. Also, several sample sizes, T/R ratios, and proportions of actual data used for generating the virtual data were further investigated. The performance of the VAE-generated dataset was compared with the original dataset and the subsampled dataset. We performed extensive hyperparameter tuning to enhance the efficiency of VAEs. In this context, we tested different combinations of parameters to assess the algorithm thoroughly. Preventing the generation of fake data is crucial, and in our research, we implemented all necessary steps to guarantee complete reproducibility of the entire process. In all scenarios investigated in this study, the generated dataset exhibited significantly higher statistical power compared to the subsampled datasets, even when scaled acceptance limits were applied to the subsampled sets. It is noteworthy that the VAE-generated datasets consistently outperformed the much larger original datasets, sometimes by up to twice the statistical power. This trend persisted even when scaled limits were applied to the original dataset. Overall, the utilization of the VAE method resulted in performance that was at least equivalent to the much larger original dataset, and in many cases, it was notably superior. When no scaling was applied to the original dataset, i.e., when the VAE approach was treated equally to the other datasets, its superiority was even more pronounced.