Computational design of clinical trials using a combination of simulation and the genetic algorithm

Artificial intelligence (AI) has come to be used in various technological fields in recent years. However, there have been no reports of AI-designed clinical trials. In this study, we tried to develop study designs by a genetic algorithm (GA), which is an AI solution for combination optimization problems. Specifically, the computational design approach was applied to optimize the blood sampling schedule for a bioequivalence (BE) study in pediatrics and optimize the allocation of dose groups for a dose-finding study. The GA could reduce the number of blood collection points from 15 (typical standard) to seven points without meaningful impact on the accuracy and precision of the pharmacokinetic estimation for the pediatric BE study. For the dose-finding study, up to 10% reduction of the total number of required subjects from the standard design could be achieved. The GA also created a design that would lead to a drastic reduction of the required number of subjects in the placebo arm while keeping the total number of subjects at a minimum level. These results indicated the potential usefulness of the computational clinical study design approach for innovative drug development.