Problem
Research questions and friction points this paper is trying to address.
pharmaceutical process characterization
factorial design of experiments (DOE)
Bayesian optimization
multi-objective process characterization
high-dimensional spaces
JAREX: An Acquisition Function for Multi-Objective Algorithmic Process Characterization
🤖 AI Summary
本文提出JAREX方法，通过贝叶斯主动学习解决多目标制药过程表征问题，比传统方法更高效准确地识别联合通过区域。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Bayesian active-learning
multi-objective process characterization
Joint Acceptable Region EXploration (JAREX)
adaptive experiment selection
optimistic joint-feasibility mask
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