Strategic Integration of Mechanistic, Hybrid, and AI Models in Biopharmaceutical Manufacturing

Abstract

The integration of mechanistic, hybrid, and AI-driven modeling methods has great potential to change biopharmaceutical manufacturing. This combination aids in gaining a better understanding of processes, predictive control, and successful lifecycle management. In this chapter a case driven framework is presented for selection and use of modeling methods in both upstream and downstream operations. This involves optimization and modeling of bioreactors and chromatography steps. In upstream operations, mechanistic models provide foundational insights into cell growth and metabolic dynamics, while hybrid approaches and Physics-Informed Neural Networks (PINNs) are increasingly leveraged to predict critical quality attributes and optimize process performance, especially in data-limited environments. In downstream processing, mechanistic and hybrid models are useful for the characterization of the resin, mass transfer analysis, impurity clearance and process design in chromatography steps such as Protein A, ion-exchange, and hydrophobic interaction chromatography. The models can be used for effective process development, to aid tech transfer, and to manage deviations by integrating first principles knowledge with data-driven corrections. This chapter includes a few case studies to illustrate and discuss the selection of advanced modeling strategies according to the complexity of the processes, availability of data, and operational goals. The review highlights model selection and use within real-world manufacturing situations to demonstrate how these integrated models accelerate the development process, enhance product robustness, and enable model-driven platforms as per Biopharma 4.0.