## 4 Discussion  
In a previous study of our lab, the idea of using artificial neural networks, and particularly VAEs, for virtually increasing the sample size of clinical trials was introduced [10]. Variational autoencoder models, or simply VAEs, are generative models that use deep learning methodologies to generate novel synthetic data. Our lead study demonstrated the various advantages of this deep learning approach in the context of simple clinical trials. Leveraging clinical data generated by VAEs maintained the type I error at a minimum  
Figure 6: Bioequivalence acceptance of the VAE-“generated”, “original”, and “subsampled” dataset for within-subject variability of 15% (**A**) and 30% (**B**). Two possibilities of the VAE-“generated” datasets are shown: one with the same sample size as the “original” dataset and another with twice the “original” sample size. Three Test/Reference (T/R) ratios of the average endpoint were considered: 1.0, 1.1, and 1.2. In all cases, a range of subsample proportions (25%, 50%, 75%, and 100%) was investigated. and consistent level with the actual data, even though only a small part of the actual is used [10]. In this study, the concept of utilizing VAEs in clinical trials is extended to the field of bioequivalence studies. So, this study aims to explore how the utilization of a VAE-generated dataset can be used to lower the need of actual human data and substitute them with AI-generated data. To accomplish this task, we simulated the conditions of 2 \(\times\) 2 (i.e., two-period, two-treatment) crossover BE trials. Initially, a group of original data (i.e., study volunteers) was simulated, and synthesized data were created by taking parts of them (e.g., 25%, 50%, 75%, or all of them, i.e., 100%) using the VAE model. The entire procedure was repeated multiple times through Monte Carlo simulations, exploring various conditions (scenarios). These conditions included within-subject variability (CVw), different average endpoint values for the Test and Reference products (i.e., the T/R ratios), subsampled proportions, and how many times the generated data were larger than the original (i.e., 1\(\times\), 2\(\times\) the original dataset). These scenarios were selected to explore various conditions of special interest. For instance, a T/R ratio of 1 corresponds to the case where the two groups under comparison exhibit identical performance, resulting in a relatively high percentage of bioequivalence acceptance. In contrast, the case of T/R = 1.1 implies a 10% average difference between the two groups, leading to reduced BE acceptances. Also, the role of within-subject variability (i.e., CVw) is crucial in bioequivalence studies, as the statistical assessment relies on estimating a 90% confidence interval where the variability term used is CVw. Thus, in this study, two levels of CVw were examined: a moderate value of 15% and a marginal value of 30%, which is the threshold for a pharmaceutical product to be considered highly variable. It is worth mentioning that an advantage of using VAE neural networks lies in their inherent ability to reduce the variability of input data. This is particularly important for BE studies, especially in the case of topical pharmaceutical products. In fact, additional scenarios involving highly variable drugs and/or pharmaceutical products should be studied, but this could be the focus of an entirely new study. Additionally, this study investigated the robustness of AI-driven generation of virtual subjects concerning the amount of data used. To address this, subsamples as low as 25% of the original dataset were examined, demonstrating the desired performance. Thus, after tuning the VAE hyperparameters, the performance of the VAE system was evaluated in terms of the percentage acceptance of bioequivalence compared to the original and subsampled datasets.