Optimal synthesis of protein purification processes

Area of Science:

• Biochemical Engineering
• Process Systems Engineering
• Biotechnology

Background:

• Biotechnological product purification is complex and costly, often involving multiple chromatographic steps.
• Selecting optimal purification sequences is challenging due to cost, quality, and purity requirements.
• Systematic methods are needed to design efficient downstream processing for bioproducts.

Purpose of the Study:

• To develop mathematical models for the systematic synthesis of purification processes for biotechnological products.
• To optimize the selection and sequencing of chromatographic steps to achieve desired purity and minimize costs.
• To provide robust solution strategies for designing complex multistep purification trains.

Main Methods:

• Development of mixed integer linear programming (MILP) optimization models.
• Utilizing physicochemical data of protein mixtures for sequence selection.
• Proposing sequential models to first minimize steps and then maximize final product purity.
• Developing smaller-sized alternative models for efficiency when applicable.

Main Results:

• Successfully generated optimal sequences of chromatographic operations for complex mixtures with multiple contaminants.
• Demonstrated the ability to achieve specified purity levels while minimizing the number of purification steps.
• Validated the models' effectiveness in comparison to existing synthesis approaches.
• Showcased the application of MILP for designing efficient and cost-effective bioseparation processes.

Conclusions:

• MILP-based mathematical models offer a systematic and powerful approach for designing biotechnological purification processes.
• The proposed models enable efficient selection and sequencing of chromatographic steps, balancing purity, cost, and process complexity.
• This work provides a valuable framework for optimizing downstream processing in the biopharmaceutical industry.