# Generative Neural Networks for Addressing the Bioequivalence of Highly Variable Drugs
## 4 Discussion
First and foremost, they provide more consistent training and convergence than GANs, allowing them to avoid concerns such as mode collapse. Additionally, the Wasserstein distance might better indicate learning progress and data quality. They offer greater flexibility in capturing complex distributions, such as those observed in the pharmacokinetics of highly variable medicines. However, their usage in clinical simulations is not without difficulties, as interpretability remains limited, particularly in understanding how the model arrives at specific outcomes. Regulatory approval is currently low, as synthetic data must undergo rigorous validation. Bias control and transparency remain active areas of research. As a result, while WGANs are technically great for modeling, their clinical applicability is dependent on thorough validation, real-world testing, and clear regulatory frameworks, all of which we are presently investigating in follow-up work using real BE datasets. Various machine learning and generative AI algorithms have been explored or proposed for synthetic data generation in the context of BE studies, each bringing its own set of advantages and inherent limitations. Variational Autoencoders (VAEs) [38], while effective in learning compact latent representations of complex data, often produce overly smoothed outputs due to their probabilistic nature. This smoothing effect limits their ability to accurately simulate the sharp fluctuations and variability typically observed in highly variable drugs, a critical requirement in BE analyses. Traditional GANs [23], although powerful in certain domains, are prone to issues such as training instability and mode collapse, particularly when applied to small or noisy clinical datasets where sample sizes are constrained. Conditional GANs (cGANs) attempt to address some of these challenges by incorporating auxiliary information to guide generation. However, cGANsgenerally require large, well-annotated datasets to achieve stable training dynamics and maintain generation quality [39], conditions that are often difficult to meet in BE studies. Other conventional synthetic data generation techniques, such as the Synthetic Minority Over-sampling Technique (SMOTE), focus primarily on balancing datasets by generating new synthetic samples but tend to produce limited diversity and struggle with datasets containing a mixture of continuous and categorical variables [39]--a common characteristic of clinical pharmacokinetic data. Recently, diffusion models have emerged as highly effective generators, particularly in the image domain, demonstrating remarkable capabilities in producing high-fidelity synthetic images. Nonetheless, their application to clinical or tabular datasets remains nascent, and their computational demands are considerable [40], posing practical challenges for their widespread adoption in resource-constrained regulatory environments. In contrast, WGANs present a compelling alternative by offering more stable and consistent training, greater resilience against mode collapse, and superior distributional fidelity. WGANs are particularly adept at capturing complex variability, a critical feature for accurately replicating pharmacokinetic profiles and ensuring regulatory acceptability in BE simulations. These comparative advantages, especially the balance between training robustness and synthetic realism under regulatory constraints, motivated our selection of WGANs as the foundational model for this study. The study by [PERSON] et al. [38] of our research group was the first study to introduce the idea of using synthetic data in clinical trials, in particular data that are generated from VAEs. That study proposed the use of synthetic data in BE investigations of HVDs, with the goal of essentially increasing sample sizes and improving statistical power while maintaining the conventional BE acceptance limits (80-125%). In contrast, we use WGANs, which are known for their steady training dynamics and ability to predict complex data distributions.