Bioequivalence Studies of Highly Variable Drugs: An Old Problem Addressed by Artificial Neural Networks

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.

Keywords

• bioequivalence
• highly variable drugs
• artificial neural networks
• variational autoencoders
• stochasticity
• Monte Carlo simulations