(4ig) Advancing Sustainable Biopharmaceutical Manufacturing: Integrating Macromolecule Crystallization Mechanisms and Continuous Processes

Batch and continuous crystallization for small molecules and biomolecules (proteins, peptides, monoclonal antibodies), approaches to control nucleation and polymorphism, biomolecule conformation and crystal structures, thermodynamic study of small molecules and macromolecules, integrated continuous process manufacturing

Teaching Interests:

Biochemical Engineering, Chemical Engineering Principles, Reaction Engineering, Fluid Mechanics, Heat and Mass Transfer, Process Control, Separation Processes, Thermodynamics, Particle Science, Colloid and Interface Science, and Process Safety Management.

Advancements in targeted biotechnology research are crucial for developing novel biopharmaceutical therapeutics. Peptides, proteins, and monoclonal antibodies (mAbs) are key components in this field, each playing a vital role in various biological processes and therapeutic applications. However, efficient, cost-effective, and environmentally sustainable technologies for large-scale biomaterial manufacturing remain significant challenges. Crystallization methods significantly enhance purification efficiency and environmental sustainability. By integrating reaction and purification processes into a continuous operation, crystallization reduces separation times and operational complexity, thereby cutting costs and increasing efficiency. This approach aligns with green chemistry principles, utilizing environmentally benign solvents and reagents to minimize the environmental impact of biomolecule production.

Nonetheless, the crystallization process of macromolecules is still challenging due to their complex structures, large sizes, high flexibility, non-uniformity, and strong dependence on solvent conditions. My research focuses on studying macromolecule crystallization mechanism and the continuous manufacturing to improve purification efficiency and environmental sustainability in biomolecule manufacturing. The thermodynamic and kinetic properties of biomolecules, as well as their flexible conformations during crystallization, have been investigated in my study to explain the crystallization mechanism for macromolecules. Additionally, continuous biopharmaceutical crystallization platform has been developed for bio-based materials to integrate the entire downstream process, including purification, crystallization, filtration, and drying.

Thermodynamic Properties and Nucleation Kinetics for Biomolecules: My research investigates the solubility of peptides with different amino acid sequences. Molecular dynamic (MD) simulations are employed to explore the solute-solvent interactions behind dissolution behaviors. The SAFT-γ Mie approach was used to predict the solubility, providing insights into the thermodynamic properties based on the biomolecular structures. The nucleation kinetic parameters of glycine homopeptides were calculated to explore the chain length effect on the classical nucleation mechanism of peptides. This research identifies a critical peptide chain length for non-classical nucleation theory, offering a deeper understanding of how chain length properties influence biomolecule nucleation mechanism.

Relationship Between Biological Macromolecular Structure and Crystallisation: The flexible conformation of peptides and proteins in solution make crystallisation which involves arranging molecules in a well-ordered, repeating pattern become difficult. My research uncovered the interaction between water and peptide molecules in peptide hydrates, exploring water's role in stabilizing the flexible structure of peptides. This demonstrates that water can act as a molecular agent to design the structures of biomolecules in crystalline form, presenting a novel strategy for biomolecule crystallization.

Continuous crystallisation design and optimisation by Process Analytical Technology (PAT) and Machine Learning: PAT allows manufacturers to measure and control processes in real time based on the Critical Quality Attributes (CQAs) of the product, optimizing product quality while reducing the cost and time involved in development and manufacturing, while machine learning extracts accurate information from PAT to develop data-driven crystallization process models for process prediction and model predictive control (MPC).

Based on the above research, a transformative and predictive scale-up biocrystallisation platform for the accelerated manufacturing of biopharmaceuticals can be initially established, which is not only advancing our fundamental understanding of biomolecular crystallisation but also offering practical solutions for large-scale, sustainable biopharmaceutical manufacturing.