A human‐in‐the‐loop Bayesian optimization framework for constraint‐aware bioprocess development

We present Pareto Front Guided Sampling (PFGS), a human‐in‐the‐loop (HiL) Bayesian Optimization (BO) framework that reformulates surrogate‐model predictions as a multi‐objective optimization problem, allowing domain knowledge to be added intuitively to any BO problem. PFGS addresses constrained optimization by treating the posterior probability of constraint satisfaction as an explicit Pareto objective, and robust optimization via a Monte Carlo estimate of expected lower‐confidence performance under input perturbations, capturing likely degradation. The multi‐dimensional Pareto front makes trade‐offs between predicted performance, model uncertainty, constraint satisfaction, and robustness visible through pairwise two‐dimensional projections on an interactive dashboard, allowing selection criteria to be refined as the model and objectives evolve. The framework is showcased on a eight‐dimensional in‐silico fed‐batch Chinese Hamster Ovary cell culture case study. We showed improved performance over BO when the expert has perfect domain knowledge; however, PFGS is not designed to outperform BO, but rather to present a HiL acquisition function framework.