# Generative Neural Networks for Addressing the Bioequivalence of Highly Variable Drugs
## 4 Discussion
While VAEs encode and decode data in a latent space and may produce blurrier outputs due to their probabilistic nature, WGANs use a generator-critic architecture that produces sharper and more realistic synthetic data, better reflecting the variability in pharmacokinetic parameters inherent in HVDs. Furthermore, our method focuses on modeling and conserving variability rather than simply minimizing it through sample enlargement. Importantly, we address regulatory acceptability more explicitly by suggesting validation paths and integrating our strategy with existing EMA and FDA standards, laying the groundwork for future real-world integration. Thus, while both studies aim to improve BE evaluation using generative AI, they differ greatly in technique, output quality, and regulatory direction. It should be mentioned that analyzing the behavior of the latent space, particularly under high-variability scenarios, can provide valuable insights into how WGANs learn and represent the underlying data distribution. However, the primary focus of the present study was to evaluate the quality of the generated data. We intend to investigate the latent space structure in future work, with the aim of gaining a deeper understanding of model robustness and data diversity. As the use of synthetic data will expand in healthcare and pharmaceutical research, there is a pressing need for a specific regulatory framework to ensure its appropriate and reliable application. To address this need in the case of BE of HVDs, we are currently developing a framework to support the acceptance of synthetic data in clinical and scientific evaluations. This effort is grounded in a thorough analysis of BE outcomes across a wide range of scenarios, based solely on real-world data. By systematically evaluating BE acceptance under diverse and extensive conditions, we aim to establish clear, evidence-based criteria that can inform regulatory bodies on when and how synthetic data may be accepted. This work is a crucial step toward building trust and promoting the responsible integration of synthetic data into regulatory decision-making. Overall, it should be stated that this work sets the groundwork for introducing WGANs as a new approach to tackling BE studies for highly variable drugs. The research highlights how WGANs can help address challenges like reducing input samples while maintaining solid, reliable output results, essential for studying drugs with high variability. It should be underlined that this study aimed to present a generalized framework for the application of WGANs in the evaluation of BE for HVDs. For this reason, all simulations were deliberately designed to be broad and not tailored to specific drug characteristics. Consequently, the findings are not confined to any single drug type and are applicable across a wide range of drug classes. The proposed methodology is inherently replicable and can be used irrespective of the therapeutic category. In this study, our aim was to develop a robust framework for generating realistic synthetic datasets (referred to as \"virtual patients\") that can preserve the inherent variability of pharmacokinetic parameters critical for BE assessments. By creating synthetic data that captures the complexity of real clinical datasets, we seek to potentially reduce the need for large sample sizes and intricate replicate study designs, which are often necessary to manage high inter-individual variability. To achieve this, we exploited the capabilities of WGANs, which are particularly well-suited for modeling complex, high-variability distributions with enhanced training stability. Through this approach, the aim was to improve the efficiency and statistical power of BE studies, minimize the risk of false negative outcomes, especially for highly variable drugs. While this study highlights the potential of WGANs for generating virtual populations in BE studies involving HVDs, several limitations must be acknowledged.