Introducing an Artificial Neural Network for Virtually Increasing the Sample Size of Bioequivalence Studies

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 × 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.

Implementation of a Generative AI Algorithm for Virtually Increasing the Sample Size of Clinical Studies

“…To our knowledge, there has been no prior exploration of implementing the WGAN algorithm to generate "virtual patients" aimed at reducing the time and expenses associated with clinical studies and minimizing human exposure. Two recent studies [17,18] by our research group, highlighted the positive role of variational autoencoders in clinical and bioequivalence trials. This study expands these two previous works by using an alternative generative AI algorithm, the Wasserstein Generative Adversarial Networks.…”

“…In healthcare, there are numerous use cases of AI and machine learning, including drug discovery, medicine, dentistry, anesthesiology, and ophthalmology [11][12][13][14][15][16]. One recent application of AI proposed by our research group is data augmentation, which involves virtually increasing a sample by generating new data from existing data [17,18]. In this context, several other studies have evaluated the effectiveness of diverse augmentation approaches where the Wasserstein Generative Adversarial Networks (WGANs) have exhibited superior performance compared with other methods [19].…”

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

“…VAEs are a special case of neural networks that can be used to generate new data based on existing data. They are composed of two parts: the "encoder" and its "mirrored" image, the decoder [22,27]. The encoder and the decoder are linked with the "latent space".…”

“…The sampled datapoints can be used as input from the decoder to generate novel datapoints. An extensive description of VAEs and autoencoders was presented in our previous studies [22,27]. VAEs are a special case of neural networks that can be used to generate new data based on existing data.…”

“…The sampled datapoints can be used as input from the decoder to generate novel datapoints. An extensive description of VAEs and autoencoders was presented in our previous studies [22,27].…”