# Generative Neural Networks for Addressing the Bioequivalence of Highly Variable Drugs
## 4 Discussion  
The purpose of this study was to investigate the potential of WGANs to solve the problem of high variability in BE studies. These restrictions result in higher costs, longer procedures, and logistical issues in finding enough participants, all of which are substantial hurdles to medication development and licensing processes. By using WGANs to virtually increase sample sizes, this work aims to give a novel method for reducing reliance on large populations. In this study, WGANs were used to generate data that simulated the HVDs in BE studies. The methodology involved applying the WGAN model on data that replicated BE research using Monte Carlo simulations. To model the BE setting, we constructed simulations of 2 \(\times\) 2 crossover studies. The WGAN performance was evaluated by comparing the generated, sample, and original data for BE acceptance and similarity. Figure 6: Percent BE acceptance for the original dataset, a random sample (50% of the original), and the WGAN-generated dataset (2\(\times\), 3\(\times\)) for two actual datasets: (**A**) hydrochloride and (**B**) atorvastatin. N refers either to actual human subjects or to data synthesized using WGANs. The error bars represent the standard error calculated from 1000 Monte Carlo trials performed under each condition. To validate the process's resilience and robustness, thousands of Monte Carlo simulations were executed. In this study, we applied Monte Carlo simulations to systematically investigate a broad spectrum of experimental conditions. Specifically, we simulated 36 unique scenarios derived from a full factorial design encompassing three levels of sample size, three levels of data variability, two regulatory contexts, and two synthetic sample sizes (i.e., \(3\times 3\times 2\times 2\)). This comprehensive simulation framework enabled a rigorous evaluation of the generative model performance and robustness across diverse conditions. Moreover, the use of simulated data was rather important in assessing the similarity between original, sampled, and synthetic data distributions, as described in Tables 3 and 4. Simulated data provided the necessary control and transparency to perform robust comparisons and compute statistical similarity metrics, tasks that would be almost impossible using real-world datasets due to the lack of ground truth and inherent variability. The study findings reveal that creating 3\(\times\)N data using WGANs results in much higher BE acceptance percentages than both the original and sample distributions. For CVw values of 15% and 30%, the generated data consistently outperformed the original and sample distributions according to EMA and FDA criteria for both 2\(\times\)N and 3\(\times\)N. When CVw = 50%, the generated distributions showed higher BE acceptance for smaller sample sizes but lower acceptance for bigger sample sizes for 2\(\times\)N generation. However, for 3\(\times\)N data, the produced distributions consistently demonstrated higher BE acceptance across all scenarios. This was most noticeable at CVw = 50%, when the 3\(\times\)N-generated data obtained BE acceptance percentages greater than 80%, compared to the sample's maximum of 40%. The results showed that the WGANs approach is beneficial in situations with higher variabilities, underlining the possibility for adopting WGANs to increase the efficiency of BE studies. It is crucial to note that WGANs perform better, the smaller the sample and the bigger the generation, as can be seen from our current research, where the 3\(\times\)N results offered better results than the 2\(\times\)N, but also from our previous one [22]. WGANs have significant advantages even over the scaled limits used by regulatory bodies in BE studies for HVDs. One of WGANs key features is their ability to generate data that nearly resembles the original distributions while dealing with variability. Traditional procedures, such as applying bootstrapping, rely on assumptions that may not adequately convey the complexities of real clinical data.