JAREX: An Acquisition Function for Multi-Objective Algorithmic Process Characterization
JAREX maps multi-objective pharmaceutical process pass regions with fewer experiments than factorial design.
The paper presents JAREX, a Bayesian active-learning acquisition function for multi-objective pharmaceutical process characterization under Quality by Design. It selects experiments to recover the joint pass region where several threshold criteria are satisfied at once, combining an optimistic feasibility mask with a multi-objective randomized straddle. Benchmarks report more accurate, sample-efficient recovery than factorial design of experiments, space-filling designs, and greedy strategies, and batched use reduced iterative experiments by more than half. The method is implemented in the open-source obsidian package.
- Learns the joint pass region across multiple quality thresholds
- Outperformed factorial DOE, space-filling, and greedy objective-wise designs
- Batched use cut iterative characterization experiments by more than half
- Implemented in the open-source obsidian package
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Pharmaceutical process characterization is central to Quality by Design because it defines how variations in process parameters affect the ability to meet product quality specifications, thereby supporting proven acceptable ranges and robust manufacturing. In practice, however, characterization still relies largely on factorial design of experiments (DOE) approaches, which are inefficient for resolving multivariate pass/fail boundaries in higher-dimensional spaces. While Bayesian optimization has transformed process optimization, adaptive methods for multi-objective process characterization remain lacking. Here, we introduce JAREX (Joint Acceptable Region EXploration), a Bayesian active-learning acquisition function for multi-objective process characterization. JAREX formulates characterization as a joint boundary-learning problem and adaptively selects experiments to recover the joint pass region defined by simultaneous satisfaction of threshold criteria across multiple objectives. JAREX combines an optimistic joint-feasibility mask with a multi-objective extension of randomized straddle, focusing sampling on the joint edge of failure. Our benchmark study suggests that JAREX provides more accurate and sample-efficient recovery of the joint pass region than factorial DOE, space-filling designs, and greedy objective-wise strategies over the full experimental budget range. For batched experimentation, it reduces the number of iterative process characterization experiments by more than half while preserving high accuracy for the boundary-identification task. Implemented in the open-source obsidian package, JAREX provides a modular framework for adaptive, data-efficient multi-objective algorithmic process characterization, supporting sample-efficient range finding in high-dimensional spaces.
Text extracted automatically; images, tables and formatting may be missing. Original: https://arxiv.org/abs/2609.24954