# Generative Neural Networks for Addressing the Bioequivalence of Highly Variable Drugs
###### Abstract
Models like generative adversarial networks (GANs) can play a vital role in assisting the field, such as in trial design and participant recruitment, thus automating the existing procedures, identifying complex patterns and offering innovative solutions [16; 17; 18; 19]. This can revolutionize the clinical setting by improving the decision-making process and providing diagnoses and treatments more efficiently than traditional methods [18]. Currently, generative AI demonstrates its impact in cancer diagnoses and detection, bariatric surgery, neurological clinical decisions, etc. Undoubtedly, the emergence and applicability of generative AI reinforces its characterization as the \"second machine age\" [15]. Existing studies that use AI algorithms (particularly early-generation models such as vanilla GANs), often encounter substantial challenges when applied to tabular clinical datasets. In contrast to image or text data, tabular clinical data are inherently heterogeneous, typically comprising a complex mix of continuous and categorical variables. These datasets frequently exhibit skewed distributions, missing values, and intricate inter-variable dependencies, all of which pose significant modeling difficulties [20]. Vanilla GANs, originally developed for image generation tasks, are not inherently designed to manage these complexities. Consequently, they often struggle to accurately capture the true underlying structure of tabular clinical data, resulting in synthetic datasets that lack fidelity and realism. This limitation becomes particularly critical when attempting to replicate key pharmacokinetic parameters, such as Cmax and AUC, which are fundamental to BE evaluations. Inadequate synthetic reproduction of these parameters can compromise the reliability of BE assessments, diminish the regulatory applicability of the generated data, and ultimately constrain the broader adoption of synthetic data solutions in clinical research and drug development. Also, they frequently need large datasets for training, which increases the computational cost, as well as result in low interpretability due to model complexity (e.g., TVAE, CTGAN, CTAB-GAN, StaSy) [21]. In our previous study, we introduced the idea of using Wasserstein GANs (WGANs) in clinical studies and used Monte Carlo simulations to evaluate their performance [22]. In that study, the data generated from the WGANs outperformed the sample, in terms of \(t\)-test evaluation, while also performing similarly to the original distributions. The similarity was assessed on the simultaneous success or not of the \(t\)-test criterion between the original and sample distribution, and the original and generated distribution [22]. This behavior of the WGANs model led us to further investigate its applicability in more extreme scenarios such as those of the HVDs in BE studies. The aim of the study is to suggest a novel approach for incorporating WGANs in the traditional BE study setting to address the problem of patient recruitment and decision-making. The methodology was further implemented in real BE datasets after the Monte Carlo simulations to assess their validity. By providing a framework for generating realistic synthetic datasets (\"virtual patients\") that preserve the inherent variability of pharmacokinetic parameters we aim to potentially reduce the need for large sample sizes and complex replicate designs. By exploiting the ability of WGANs to model complex, high-variability distributions with stable training, this approach could improve the efficiency of BE studies, reduce the false negative rejections of HVDs based on the small sample, decrease participant burden, and ultimately facilitate faster access to generic drugs.