Stem cell biomanufacturing under uncertainty: A case study in optimizing red blood cell production

Area of Science:

• Biotechnology and Bioprocessing
• Regenerative Medicine
• Computational Biology

Background:

• Translating cellular therapy discoveries into commercial applications necessitates efficient stem cell biomanufacturing.
• Optimizing bioprocess design and operation is crucial for reliable and scalable production.
• Existing methods may not adequately address uncertainties inherent in biomanufacturing.

Purpose of the Study:

• To propose a rigorous computational framework for stem cell biomanufacturing under uncertainty.
• To demonstrate the advantages of this framework using a case study of red blood cell production.
• To quantitatively evaluate the commercial impact of optimized cellular therapies.

Main Methods:

• Development of a mathematical toolkit incorporating high-fidelity modeling.
• Application of single and multivariate sensitivity analysis.
• Utilizing global topological superstructure optimization and robust optimization techniques.

Main Results:

• The proposed framework was quantitatively demonstrated using a dual hollow fiber bioreactor for red blood cell production.
• The optimization phase achieved a cost reduction of fourfold.
• The cost of insuring process performance against uncertainty was approximately 15% above the nominal optimum.

Conclusions:

• Mathematical modeling and optimization provide a robust approach to guide decision-making in stem cell biomanufacturing.
• The framework enables the reliable commercialization of cellular therapies.
• This disruptive technology paradigm has significant potential commercial impact.