Using Deep Reinforcement Learning for Optimizing Process Parameters in CHO Cell Cultures for Monoclonal Antibody Production

Abstract

This paper presents a novel deep reinforcement learning (DRL) approach for optimizing process parameters in CHO cell culture for monoclonal antibody production. The proposed system integrates a specialized DRL architecture with comprehensive process monitoring capabilities to achieve real-time parameter optimization. The framework incorporates a multi-objective reward function that balances productivity, product quality, and resource utilization. The system architecture implements a hierarchical control strategy combining traditional feedback loops with DRL-based optimization. Experimental validation demonstrates significant improvements in key performance metrics, including a 25-35% increase in product titer and a 40-50% reduction in process parameter variability. The adaptive control strategy maintains robust performance across different operational conditions while ensuring compliance with quality requirements. Advanced components for data analysis and visualization enable comprehensive process monitoring and proactive control interventions. The system's modular design facilitates scalability and integration with existing production infrastructure. The results confirm the effectiveness of the DRL-based approach in solving the complex challenges of bioprocess optimization and provide a basis for intelligent manufacturing implementation in biopharmaceutical production.