Automated Anomaly Detection and Adaptive Mitigation in High-Throughput Lipid Nanoparticle (LNP) Formulation using Machine Learning and Real-Time Process Analytics

**Abstract:** The rapid development and scale-up of lipid nanoparticle (LNP) formulations for mRNA therapeutics present significant challenges in maintaining consistent product quality and process control. Traditional quality control (QC) methods are often reactive and insufficient to address subtle process deviations. This paper proposes a comprehensive framework for automated anomaly detection and adaptive mitigation in LNP formulation processes utilizing a Multi-layered Evaluation Pipeline (MEP) coupled with a HyperScore system. Leveraging real-time process analytics and advanced machine learning algorithms, our system proactively identifies anomalies, assesses their impact with high fidelity, and initiates adaptive pipeline adjustments to maintain process robustness and product quality, achieving a projected 30-50% reduction in batch failure rates and a 15-20% increase in overall throughput.