# Generative Neural Networks for Addressing the Bioequivalence of Highly Variable Drugs
## 4 Discussion
The methodology relies mainly on Monte Carlo simulations, rather than real-world datasets, limiting its direct applicability. The specific use of WGANs, favored for their stability via the Wasserstein loss function, helped reduce risks like mode collapse and overfitting, especially through small augmentation scales and validation against ground truths. However, the absence of extensive real clinical data validation remains a gap. Future research should focus on applying WGANs to real-world BE datasets to verify performance under realistic conditions. Additionally, strategies to optimize computational efficiency without compromising data quality are needed. As WGANs and other generative models often operate as \"black boxes\", establishing regulatory frameworks will be crucial. Clear guidelines could transform perceived opacity into a strength, framing these models as consistent and unbiased tools for clinical research. To truly unlock the potential of WGANs, more studies using real-world clinical data are needed. Testing this method in real-life scenarios would allow researchers to refine it and make it more reliable across diverse situations. On top of that, it is crucial to establish clear criteria and conditions before WGANs can become part of regulatory practice. These criteria would ensure the method meets the strict scientific and compliance standards set by regulatory authorities like the EMA and FDA. Overall, in this study, we address a key challenge in BE assessments for highly variable drugs: the reduced statistical power resulting from high intra-subject variability, which often leads to an increased false negative rate. Traditionally, this issue is moderated by increasing the sample size of the BE study, which enhances statistical power. However, recruiting additional participants is often costly, time-consuming, and raises ethical considerations. To overcome these limitations, our study follows the same principle of increasing sample size, but in a virtual setting. We use a Wasserstein Generative Adversarial Network to generate AI-synthesized virtual volunteers that mimic the statistical characteristics of real participants. By augmenting the dataset with these synthetic samples, we aim to strengthen the reliability of BE outcomes while reducing the practical and ethical constraints of conventional clinical trial expansion. The ultimate aim of this study was to demonstrate how the integration of data augmentation techniques (in particular with the use of generative AI algorithms) could significantly transform clinical research by achieving substantial cost savings, accelerating study time-lines, and minimizing human exposure. It is proposed in this study that this can be achieved by partially replacing real volunteers with \"virtual subjects\", thereby sharply reducing the number of participants needed in certain phases of clinical trials. Our results showed that WGANs were able to generate virtual populations with BE acceptance rates and similarity levels that matched or even surpassed those of the original population. This approach proved highly effective across a range of scenarios, enabling marked expansions in BE study sample sizes, while simultaneously lowering operational costs, reducing logistical burdens, and speeding up trial completion. By requiring fewer human participants, this strategy also promoted more ethical research practices by limiting unnecessary human exposure. Although the application of AI in clinical research held tremendous promise for revolutionizing healthcare, realizing these benefits would require a cautious path forward. Rigorous scientific evaluation, continuous validation, and the development of strong regulatory frameworks are essential to ensure the safe, ethical, and sustainable integration of AI-driven methodologies into clinical research.