###### Abstract  
The bioequivalence (BE) of highly variable drugs is a complex issue in the pharmaceutical industry. The impact of this variability can significantly affect the required sample size and statistical power. In order to address this issue, the EMA and FDA propose the utilization of scaled limits. This study suggests the use of generative artificial intelligence (AI) algorithms, particularly variational autoencoders (VAEs), to virtually increase sample size and therefore reduce the need for actual human subjects in the BE studies of highly variable drugs. The primary aim of this study was to show the capability of using VAEs with constant acceptance limits (80-125%) and small sample sizes to achieve high statistical power. Monte Carlo simulations, incorporating two levels of stochasticity (between-subject and within-subject), were used to synthesize the virtual population. Various scenarios focusing on high variabilities were simulated. The performance of the VAE-generated datasets was compared to the official approaches imposed by the FDA and EMA, using either the constant 80-125% limits or scaled BE limits. To demonstrate the ability of AI generative algorithms to create virtual populations, no scaling was applied to the VAE-generated datasets, only to the actual data of the comparators. Across all scenarios, the VAE-generated datasets demonstrated superior performance compared to scaled or unscaled BE approaches, even with less than half of the typically required sample size. Overall, this study proposes the use of VAEs as a method to reduce the necessity of recruiting large numbers of subjects in BE studies. bioequivalence; highly variable drugs; artificial neural networks; variational autoencoders; stochasticity; Monte Carlo simulations 2020 IEEE/RSJ Web Conference Conference on, 2019, pp. 1-10. IEEE. Personal notice on this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting,  
to those with a within-subject coefficient of variation of 30% or more, whether due to the drug substance or its formulation [3]. In BE studies, this variability refers to the residual variability that comes from the ANOVA analysis after excluding all other known factors. In the case of a 2 \(\times\) 2 crossover design, residual variability is estimated after subtracting from the total data variability the variability attributed to subjects, periods, sequences, and the administered pharmaceutical product. Variability can arise from factors such as the drug characteristics or physiological conditions in patients. However, as within-subject variability increases, demonstrating BE becomes more challenging without increasing the sample size [3]. To address this issue, various methods have been proposed. Both the EMA and the U.S. FDA currently recommend using scaled BE limits [1,2]. This approach adjusts the BE limits based on the within-subject variability of the reference product. In this context, the EMA and FDA's recommended reference-scaled procedures require full-replicate or semi-replicate study designs. In these designs, the reference product is administered at least twice to each subject, allowing for accurate estimation of within-subject variability [1,2]. This study proposes the integration of generative artificial intelligence (AI) algorithms, specifically variational autoencoders (VAEs), to virtually increase sample sizes in BE studies of highly variable drugs. By using VAEs, which are a type of neural network designed to generate new data samples that mimic the original dataset, synthetic data can be created that closely resemble real patient data. This approach could significantly reduce the number of human subjects required for BE studies, addressing the challenges posed by high within-subject variability. It should be mentioned that sample size in BE testing is defined as the total number of patients or individuals that take part in the study [4].