Hybrid Bioprocess Intelligence Model for Scalable Vaccine and Biopharmaceutical Manufacturing

Abstract

The growing pressure on the need of rapid vaccines and biopharmaceuticals require the manufacturing structures that can be scaled, intelligent, and regulatory-compliant. The traditional methods of process control that are based on either mechanistic model or data-driven model applied independently do not model upstream bioreactor operations as a whole. The paper introduces a new framework, the Hybrid Bioprocess Intelligence Model (HBIM), which is synergistic, which means it combines both mechanistic biokinetic models with advanced machine learning algorithms, such as Long Short-Term Memory (LSTM) networks, Gradient Boosted Trees (XGBoost), and Graph Neural Networks (GNNs). In the proposed architecture, a Bayesian fusion layer with the quantification of uncertainty is embedded, and a closed-loop digital twin with real-time adaptation of the process is provided. HBIM was validated on industrially representative fed-batch mammalian cell culture data with an R2 of 0.971 and RMSE of 0.063 g/L in monoclonal antibody titre prediction, which is superior to all benchmark methods. The system also shows a 34 percent decrease in batch-to-batch variability and an 18 percent increase in process yield which gives it a strong frontier to scalable, quality-by-design biomanufacturing.