# Generative Neural Networks for Addressing the Bioequivalence of Highly Variable Drugs
## 4 Discussion
WGANs, on the other hand, can model and create data that accurately represent the original data, including pharmacokinetic parameter fluctuations that are common in HVDs. This flexibility allows WGANs to generate more accurate estimates of BE, even when the observed data does not completely coincide with the regulatory standards. Furthermore, WGANs can improve statistical power by providing more data points to optimize the analysis without the requirement for bigger sample sizes. By creating realistic synthetic datasets, WGANs provide a more nuanced approach to assessing BE, potentially increasing the efficiency of BE studies and providing better tools for regulatory decision-making, particularly in cases where data variability would otherwise prevent clear conclusions from being drawn using traditional methods. The WGAN-generated data were evaluated for BE acceptance percentages under EMA and FDA guidelines for highly variable drugs. It is important to emphasize that the performance of WGANs was compared against the scaled approaches of EMA and FDA. These regulatory approaches permit wider acceptance limits than the traditional 80-125% range, allowing handling the high variability of certain drugs. In contrast, the WGAN-approach was strictly assessed using the classic 80-125% limits, without the benefit of scaling. Additionally, the evaluation included scenarios of high variabilities (i.e., CVw = 50%), further intensifying the stringency of the assessment. The statistical evaluation metrics and the visual inspection showed favorable results considering overfitting/underfitting problems for the synthetic distributions that resembled the original, better than the sample. Despite these challenging scenarios, the WGAN approach demonstrated favorable performance, highlighting its potential as a robust method for addressing challenges in BE studies. To conclude, our study demonstrated the potential of implementing WGANs in real BE data, where the results from the two scenarios that were examined showed promising results. However, more thorough analysis must be conducted to evaluate a variety of different real case scenarios. Furthermore, in our research, the type of input data can mirror variables from BE studies, and the technique of obtaining them is identical. These values may be pharmacokinetic, such as Cmax or AUC. WGANs generate the type of data in the input by producing data points that are similar to the original while yet being uniquely different. Another advantage of using WGANs relies on the fact that the BE study can be performed not only with a reduced sample size, but also with the typical 2 \(\times\) 2 crossover design. In other words, there is no need to use replicate or semi-replicate designs, which are more complex, time-consuming, expensive, and prone to high dropout rates. Once the WGAN approach is validated in practice and a regulatory framework is established, it can provide all these advantages. AI-based solutions have the potential to drastically lower healthcare expenditures in many aspects of the industry. AI enables more personalized healthcare interventions based on an individual's disease profile, diagnostic information, or treatment responses, thereby improving treatment outcome prediction, developing new drug designs, and improving patient care [29]. Furthermore, AI can help minimize variability in care, increase precision, speed discovery, and address inequities, all while empowering patients and healthcare professionals through advanced medical research and analytic technologies [30]. The impact of AI on healthcare is already visible, such as a 30% reduction in emergency room wait times, a 70% reduction in transfer delays from operating rooms, and faster ambulance dispatch times, demonstrating how AI is revolutionizing care delivery, diagnosis, treatment, and patient lifestyle [31]. Furthermore, AI has sped drug discovery procedures and automated target identification for pharmaceutical companies, while also allowing AI-assisted clinical trials to manage massive datasets with high accuracy.