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

• Published in: Applied sciences Vol. 14; no. 12; p. 5279
• Main Authors: Papadopoulos, Dimitris, Karali, Georgia, Karalis, Vangelis D.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2024
• Subjects: Algorithms; Artificial intelligence; artificial neural networks; Bioequivalence; Clinical trials; Datasets; Design; Drugs; highly variable drugs; Independent regulatory commissions; Monte Carlo method; Monte Carlo simulations; Neural networks; Neurons; Pharmaceutical industry; Probability distribution; Propagation; Statistical power; stochasticity; variational autoencoders; Greece

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 s...