## 4 Discussion
More specifically, under the assumption that the ratio of the mean endpoints of the T and R groups was equal to 1 (indicating similar average performance between the two groups) with moderate within-subject variability (20%), the acceptance rate of the VAE-generated dataset was consistently above 95%, whereas the acceptance rate for the original dataset dropped to 80% for low sample sizes and subsampled proportions (Figures 3 and 5). With 40% within-variability and T/R equal to 1, the VAE-generated dataset exhibited performance comparable to that of the original dataset, despite the fact that scaling was applied to the original dataset, but unscaled limits were used in the VAE-synthesized datasets. Additionally, the generated dataset outperformed the original in the case of CVW = 60%. Under the assumption that T/R equals 1:1, and CVw is 20% and 40%, the generated dataset performed at least as well as the original for high sample and subsample sizes but considerably better for low sample sizes. When within-variability was 60%, the VAE-generated dataset performed as well as the original when sample sizes were high but significantly better for the EMA case. In all cases, the generated dataset performed significantly better than the subsampled dataset, highlighting the added value of the VAE model. As expected, the original dataset showed much better performance than the subsampled dataset in all scenarios studied. Overall, it is important to note that in all scenarios presented above, no scaling was applied to the new VAE approach, but either the EMA or FDA scaled BE limits were applied to the original or subsampled data. Thus, if scaling were further applied to the VAE-generated dataset, the performance of the VAE would be incomparable to that of the original dataset and of course against that of the subsampled dataset. Overall, these findings suggest that utilizing VAEs to virtually increase the sample size in BE studies for highly variable drugs can effectively enhance the statistical power of the study while requiring fewer subjects, thereby reducing human exposure, study completion time, costs, and overall study complexity. Also, the statistical analysis will be simpler and unified in all cases, since no scaling of BE limits or its a priori statement in the study protocol would be necessary. Additionally, it is worth noting that this study simulated the typical conditions of 2 \(\times\) 2 crossover BE studies by integrating Monte Carlo simulations, stochastic terms, and VAEs. However, in practical application, simulations would not be necessary; VAEs could be applied directly to limited actual human data. The Monte Carlo simulations in this study were conducted solely to compare the performance of the VAE approach against the typical sample data used today. A limitation of this study was the high computational time required for training the VAE model and the extensive number of scenarios tested, which constrained the study to 5000 runs. Another limitation is the use of the 2 \(\times\) 2 typical crossover design. Thus, when the scaled BE approach was used for the \"original\" and \"subsampled\" datasets, the within-subject variability of the R drug was used in the reference scaled approach [1, 2], namely, the stochastic term \(\sigma_{\text{w}}\). For simplicity reasons, the same stochastic term \(\sigma_{\text{w}}\) was set for both T and R products, supposing that there is no reason to have different variabilities, i.e., assuming similar product performances. It should also be mentioned that while Monte Carlo simulations are invaluable for testing hypotheses and understanding theoretical outcomes, the use of actual (real) data is crucial for validating these findings in real-world scenarios. Simulated data, although controlled and replicable, may not capture the complexities and nuances present in real-world situations. Actual data provide a more accurate and reliable basis for analysis, ensuring that the proposed methods are applicable and effective in practical applications.