For multi-objective process characterization, JAREX reduces experimental costs by 50%+ compared to traditional design-of-experiments approaches by adaptively focusing on the boundaries where products transition from acceptable to unacceptable.
JAREX is a machine learning method that helps pharmaceutical companies efficiently map out which combinations of process parameters produce acceptable products. Instead of running many traditional experiments, it uses Bayesian optimization to intelligently select which experiments to run next, cutting the number of tests needed in half while maintaining accuracy.