###### Abstract  
Sample size is a key factor in bioequivalence and clinical trials. An appropriately large sample is necessary to gain valuable insights into a designated population. However, large sample sizes lead to increased human exposure, costs, and a longer time for completion. In a previous study, we introduced the idea of using variational autoencoders (VAEs), a type of artificial neural network, to synthetically create in clinical studies. In this work, we further elaborate on this idea and expand it in the field of bioequivalence (BE) studies. A computational methodology was developed, combining Monte Carlo simulations of 2 \(\times\) 2 crossover BE trials with deep learning algorithms, specifically VAEs. Various scenarios, including variability levels, the actual sample size, the VAE-generated sample size, and the difference in performance between the two pharmaceutical products under comparison, were explored. All simulations showed that incorporating AI generative algorithms for creating virtual populations in BE trials has many advantages, as less actual human data can be used to achieve similar, and even better, results. Overall, this work shows how the application of generative AI algorithms, like VAEs, in clinical/bioequivalence studies can be a modern tool to significantly reduce human exposure, costs, and trial completion time. variational autoencoders; bioequivalence studies; artificial neural networks; sample size; clinical trials +
Footnote †: journal: Medical  
they contain the same active substance at the same molar dose, and their equivalence is demonstrated through comparative pharmacokinetic studies, i.e., bioequivalence trials. If bioequivalence is established in the comparative pharmacokinetic trial, the two products can be considered therapeutically equivalent [4; 5]. In the context of BE testing, various approaches have been proposed to address the need of recruiting a large number of volunteers, particularly for highly variable drugs [4; 5]. In recent years, the emergence of in silico methods has led to the adoption of computational alternatives [6]. These computational methods are utilized for virtually increasing the sample size, a process known as data augmentation. Artificial intelligence (AI), particularly deep learning, has proven advantageous in its several aspects of clinical studies [7]. Considering the importance of data accessibility in data-oriented and personalized healthcare, the use of AI becomes imperative. Training AI models has demonstrated considerable benefits, accelerating and simplifying every step within drug research [8; 9]. Recently, our research group introduced the idea of using an artificial neural network, specifically variational autoencoders (VAEs), in clinical studies [10]. VAE models, falling within the category of generative models, leverage deep learning to generate new data once trained. Unlike traditional autoencoders, VAEs are not only capable of reducing variability but they are also capable of generating synthetic data based on real-world data. VAE models utilize two functions: the encoder, responsible for encoding input data, and the decoder, tasked with rebuilding the input data using the output generated by the encoder. This deep learning method was found to offer several advantages in clinical trials with a parallel design [10]. Utilizing clinical data generated by VAE enhanced the statistical power of the studies. The incidence of type I error remained low and consistent with the levels observed in the actual dataset, whereas the statistical power of the VAE approach was even higher compared to that observed in the original datasets [10]. This study is the second step toward using generative AI algorithms in clinical trial data augmentation. In our previous study, the idea was introduced in the case of simple clinical trials; this study proposes the use of artificial neural networks, particularly VAEs, to reduce the need to recruit many subjects in bioequivalence studies.