# Generative Neural Networks for Addressing the Bioequivalence of Highly Variable Drugs
## 3 Results
It should be emphasized that in all scenarios with low to medium sample sizes and variability, the synthetic datasets exhibited a higher percentage of similarity compared to the sample data. However, as expected, the percentage similarity decreased under conditions of high sample size and variability--an underlying factor contributing to the superior performance of the synthetic data. Figure 3: Percent BE acceptance for the original dataset, a sample, and the WGAN-generated dataset was evaluated at three variability levels: 15% (**A**), 30% (**B**), and 50% (**C**), under the EMA regulations. The sample sizes of the original datasets were 24, 40, and 60, while the corresponding sample sizes of the WGAN-synthesized data were three times larger than those of the original. The error bars represent the standard error calculated from 1000 Monte Carlo trials performed under each condition. Figure 4: Percent BE acceptance for the original dataset, a sample, and the WGAN-generated dataset was evaluated at three variability levels: 15% (**A**), 30% (**B**), and 50% (**C**), under the FDA regulations. The sample sizes of the original datasets were 24, 40, and 60, while the corresponding sample sizes of the WGAN-synthesized data were three times larger than those of the original. The error bars represent the standard error calculated from 1000 Monte Carlo trials performed under each condition. In terms of BE acceptance gain, in all scenarios except N = 60 at CVw = 15% (where generated data matched the original), the WGANs showed superior performance. For CVw = 30%, the generated data showed higher acceptance percentages with decreasing N values, while for CVw = 50%, the percentages increased with increasing N values. The results for N = 24 were notably similar between CVw = 30% and CVw = 50%. Interestingly, WGAN performance improved with increasing CVw, showing significantly better BE acceptance gain at 50% variability compared to 15%. Figure 5 demonstrates the WGANs' effectiveness in terms of the BE acceptance gain between generated and original data. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{4}{c}{**\% Similarity**} \\ \hline
**CVw** & **Augmentation Scale** & **N** & **Original vs. Sample** & **Original vs. Synthetic** \\ \hline \multirow{6}{*}{15\%} & \multirow{3}{*}{2\(\times\)} & 60 & 99.8\% & 100.0\% \\ \cline{3-5}  & & 40 & 99.0\% & 99.6\% \\ \cline{3-5}  & & 24 & 93.4\% & 94.0\% \\ \cline{3-5}  & & 60 & 99.8\% & 100.0\% \\ \cline{3-5}  & & 40 & 99.0\% & 99.6\% \\ \cline{3-5}  & & 24 & 93.4\% & 94.0\% \\ \hline \multirow{6}{*}{30\%} & \multirow{3}{*}{2\(\times\)} & 60 & 99.8\% & 100.0\% \\ \cline{3-5}  & & 40 & 99.0\% & 99.6\% \\ \cline{3-5}  & & 24 & 93.4\% & 94.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Similarity assessment between original and synthetic datasets across three levels of intra-subject variability (CVw) and three original sample sizes (N). Bioequivalence evaluations were conducted according to FDA guidelines. Results are shown for both 2\(\times\)N and 3\(\times\)N data augmentations. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{4}{c}{**\% Similarity**} \\ \hline
**CVw** & **Augmentation Scale** & **N** & **Original vs. Sample** & **Original vs. Synthetic** \\ \hline \multirow{6}{*}{15\%} & \multirow{3}{*}{2\(\times\)} & 60 & 99.8\% & 100.0\% \\ \cline{3-5}  & & 40 & 99.0\% & 99.6\% \\ \cline{3-5}  & & 24 & 93.4\% & 94.0\% \\ \cline{3-5}  & & 40 & 99.0\% & 99.6\% \\ \cline{3-5}  & &