Bridging biology and statistics with hybrid Bayesian experimental design for drug dose-response assays

Dose-response cytotoxicity assays are central to evaluating drug potency, yet selecting concentrations that capture both the full response range and key parameters such as the half-maximal inhibitory concentration (IC ₅₀ ) and Hill slope remains challenging. Practical constraints, including limited replicates, variability across culture conditions, and the trial-and-error nature of current practice, make assays time- and resource-intensive. To address this, we introduce a Bayesian inference framework that quantifies uncertainty, incorporates prior knowledge, and extend it with a Bayesian optimal experimental design (OED) strategy to systematically refine concentration selection. We further propose a hybrid OED approach that integrates information-theoretic design with space-filling principles, aligning statistical rigor with biological intuition. Applied to ER+ breast cancer cells in monoculture and co-culture with stromal cells, this framework revealed differential drug responses while improving information efficiency. More broadly, our results highlight how Bayesian OED can bridge computational and biological perspectives, offering a path toward more efficient, reproducible, and interpretable experimental design in cancer research and beyond.