Automated 3D Cell Culture Microfluidic Assay Design and Optimization via Bayesian Optimization and Digital Twins for Personalized Toxicity Screening

**Abstract:** Current *in vitro* toxicity screening methods relying on traditional 2D cell cultures often fail to accurately predict *in vivo* responses due to their inability to replicate complex 3D tissue structures and microenvironmental conditions. This research introduces a novel framework leveraging Bayesian optimization and digital twin technology to autonomously design and optimize 3D cell culture microfluidic assays for personalized toxicity screening. By incorporating dynamic cell behavior models and extensive experimental data, our system rapidly identifies optimal microfluidic designs (channel geometry, flow rates, media composition) maximizing predictive accuracy for individual patient responses, significantly reducing reliance on animal testing and accelerating drug development timelines. This approach promises a paradigm shift in personalized medicine, enabling highly targeted toxicity assessments at a fraction of the current cost and time.