# Generative Neural Networks for Addressing the Bioequivalence of Highly Variable Drugs
###### Abstract  
Bioequivalence assessment of highly variable drugs (HVDs) remains a significant challenge, as the application of scaled approaches requires replicate designs, complex statistical analyses, and varies between regulatory authorities (e.g., FDA and EMA). This study introduces the use of artificial intelligence, specifically Wasserstein Generative Adversarial Networks (WGANs), as a novel approach for bioequivalence studies of HVDs. Monte Carlo simulations were conducted to evaluate the performance of WGANs across various variability levels, population sizes, and data augmentation scales (2\(\times\) and 3\(\times\)). The generated data were tested for bioequivalence acceptance using both EMA and FDA scaled approaches. The WGAN approach, even applied without scaling, consistently outperformed the scaled EMA/FDA methods by effectively reducing the required sample size. Furthermore, the WGAN approach not only minimizes the sample size needed for bioequivalence studies of HVDs, but also eliminates the need for complex, costly, and time-consuming replicate designs that are prone to high dropout rates. This study demonstrates that using WGANs with 3\(\times\) data augmentation can achieve bioequivalence acceptance rates exceeding 89% across all FDA and EMA criteria, with 10 out of 18 scenarios reaching 100%, highlighting the WGAN method potential to transform the design and efficiency of bioequivalence studies. This is a foundational step in utilizing WGANs for the bioequivalence assessment of HVDs, highlighting that with clear regulatory criteria, a new era for bioequivalence evaluation can begin. bioequivalence studies; generative AI algorithms; highly variable drugs; generative adversarial networks; data augmentation +
Footnote †: journal: _Algorithms_  
0000-0001-5000-0001-5000][PERSON] 0000-0001-50  
must fall within acceptable established boundaries, meaning that they do not differ significantly. This statement is quantified in a range that both regulatory bodies agree on 80.00-125.00%, in order to pass the 90% confidence interval for the ratio of Test (T) to Reference (R) products [3; 4; 5]. Highly variable drugs (HVDs) are a class of pharmacological products that have a within-subject variability (CVw) equal to or bigger than 30% of the maximum concentration (Cmax) and/or the area under the concentration-time curve (AUC). The BE of HVDs is a concern because the high variability necessitates a large number of patients to provide acceptable statistical power [1; 6; 7]. Moreover, there have been reported difficulties in achieving the conventional BE requirements for HVDs. There are examples of a highly variable reference product failing to demonstrate BE when compared to itself in BE research with the typical design/sample size [8; 9]. FDA and EMA propose specific scaled limits for HVDs with the magnitude of the widening being determined by the CVw of the BE study [3; 4; 5]. Nowadays, technological advancements can efficiently address modern challenges in the pharmaceutical field [10; 11]. The status quo of traditional clinical studies lacks pioneering solutions, particularly in areas like patient recruitment and monitoring through the entire process. Clinical and BE studies are adversely affected by delays and tremendous costs, with the additional risk of the drug not reaching the market [10; 11; 12]. Across the field, time-consuming procedures can be optimized and automated with the integration of artificial intelligence (AI). From creating recommendation systems to data augmentation, AI holds the potential in the advancement of the pharmaceutical field [10; 11; 13; 14]. Generative AI models can help overcome limitations and accelerate advancements in healthcare. Generative AI has a variety of applications that can contribute, from data analysis to optimization of a clinical study [15; 16; 17].