Abstract
This work presents Pareto Front Guided Sampling (PFGS), a model-guided Design of Experiments (DoE) strategy for bioprocess development that makes the exploration-exploitation trade-off explicit and integrates human expertise into experiment selection. Starting from an initial experimental design, PFGS fits a probabilistic surrogate and then proposes new experiments by solving a multi-objective design problem that simultaneously rewards (i) high predicted performance (posterior mean) and (ii) high information gain (posterior uncertainty). Rather than collapsing this trade-off into a single acquisition value, PFGS generates a Pareto set of candidate experiments, that reflect different balances between improvement-seeking and learning. To prevent wasted runs, an automated screening step is performed to remove candidates in (i) low predicted-mean regions unlikely to yield near-optimal performance and (ii) low-uncertainty regions already well explained by the surrogate, concentrating effort on promising yet under-explored areas and encouraging coverage of multiple near-optimal basins. For industrial application, PFGS was extended to batch selection by choosing a diverse subset of Pareto candidates to support parallel experimentation without substantial loss in sample efficiency, and a termination rule to stop experimentation once user-defined accuracy thresholds are met. PFGS is benchmarked against Latin Hypercube Sampling (LHS) and Bayesian Optimisation (BO) on two representative problems: (i) a bioprocess case study with shallow performance gradients, where naive exploitation is unreliable and non-adaptive designs are inefficient, and (ii) a multi-modal landscape, where methods that focus on a single basin can miss alternative optima. Across these settings, PFGS allocates experimental resources more effectively than non-adaptive baselines and, in the multi-modal case, uniquely identifies and characterises all stationary regions while maintaining strong overall performance. By combining Pareto-transparent decision support, automated filtering, batch practicality, and principled stopping, PFGS provides an interpretable and resource-efficient DoE methodology for biopharmaceutical process optimisation.