# Generative Neural Networks for Addressing the Bioequivalence of Highly Variable Drugs
## 3 Results  
Initial scenarios were focused on generating 2\(\times\)N data, while the second part of the analysis focused on the generation of 3\(\times\)N data. Figure 14 shows the BE acceptance percentage results for the three distributions (original, sample, and generated) according to EMA guidelines. For a CVw value of 15% the generated data exhibited better performance compared to the original data for N = 24 and N = 40 (for 75% sampling), while for the other scenarios the generated data performed equally to the original. For CVw = 30% the generated data consistently outperformed the original data. Similarly, Figure 15 presents the BE acceptance results under FDA criteria. For CVw values of 15% and 30%, distributions generated at twice the original size showed better performance across all parameter combinations, achieving higher BE acceptance percentages. The WGAN datasets capture variability and uncertainty in two ways. The first is the application of the BE acceptance percentage as a probabilistic metric. For each scenario, the percentage is based on thousands of simulation iterations and captures the uncertainty in the outcome, indicating how frequently synthetic data can produce an acceptable result. The second one is the variation between runs. The standard deviation across the scenarios is represented by error bars in each figure. At CVw = 50%, the generated distributions achieved higher percentages for smaller sample sizes (N = 24) but lower percentages for larger samples (N = 40, N = 60) under both EMA and FDA guidelines. Due to the limited number of available samples, data augmentation was restricted to 2\(\times\)N and 3\(\times\)N of the original dataset size. The process initially began with a 2\(\times\)N augmentation; however, based on preliminary results, it was subsequently extended to a 3\(\times\)N augmentation. This larger augmentation yielded improved performance, approaching the optimal results one might expect. The decision to limit augmentation at this scale was made to avoid over-reliance on a small sample base and to prevent the generation of synthetic data that could lead to overfitting. The generated distributions of the 3\(\times\)N augmentation consistently showed better BE acceptance compared to both original and sample distributions, under the EMA guidelines. Figure 3 illustrates the BE acceptance percentages under EMA guidelines when generating 3\(\times\)N distributions. The 3\(\times\)N generation scenarios demonstrated superior BE acceptance compared to 2\(\times\)N scenarios across all parameter combinations. While WGANs struggled with 2\(\times\)N data at CVw = 50%, the 3\(\times\)N approach overcame this limitation, achieving BE acceptance percentages consistently above 80%, compared to the sample maximum of 40%. WGANs consistently outperformed both original and sample distributions across all CVw values and parameters, under the FDA guidelines, showcasing similar behavior as Figure 3. According to Figure 4, for CVw ranging from 15% to 50%, the generated data achieved higher BE acceptance percentages than original or sample distributions, exceeding 89% in all scenarios. Notably, 10 of the 18 scenarios reached 100% generated BE acceptance. These superior results with 3\(\times\)N generation confirm the appropriateness of this approach. Considering the similarity parameter, for the EMA scenarios the original distribution and the synthetic one have a higher similarity in 14/18 scenarios compared to the original and sample. For the FDA guidelines, 12/18 scenarios performed better in the similarity parameter of the original and the synthetic distributions compared to the original and sample. In the following tables (Tables 3 and 4) are presented the results for the EMAand FDA scenarios, respectively. As CVw and the augmentation scale (\(\times\)N) increase, the similarity original vs. synthetic becomes lower.