A Mathematical Model for Determining Probabilistic Design Space in Mesenchymal Stem Cell Passage Culture.

Area of Science:

• Regenerative Medicine
• Cell Biology
• Bioprocess Engineering

Background:

• Mesenchymal stem cells (MSCs) hold significant therapeutic potential for regenerative medicine.
• Designing large-scale MSC culture processes is challenging due to the need to understand long-term passage effects on cell proliferation.
• Current methods lack robust tools for predicting cell behavior over extended culture periods.

Purpose of the Study:

• To develop a predictive model for mesenchymal stem cell (MSC) growth rate as a function of cumulative population doubling level (cPDL).
• To establish a framework for designing MSC passage culture processes by integrating growth rate prediction with stochastic simulation.
• To define feasible operation regions for MSC manufacturing based on key quality indicators and probabilities.

Main Methods:

• Passage culture experiments were conducted to establish correlations between apparent growth rate and cPDL.
• A mathematical model was developed to predict MSC growth rate based on cPDL.
• Stochastic simulation was applied to design passage culture processes for bone marrow (BM-MSCs) and umbilical cord (UC-MSCs).

Main Results:

• The study identified feasible operation conditions for MSC passage culture, considering passage number and harvesting time.
• 10 out of 165 conditions were feasible for BM-MSCs, while 62 were feasible for UC-MSCs.
• The developed models provide probabilistic design spaces to meet senescence, confluency, and cell number targets.

Conclusions:

• The proposed model and simulation approach offer a robust method for optimizing MSC passage culture processes.
• This contributes to the industrialization of MSC manufacturing by providing tools for process design and control.
• The findings facilitate the reliable production of MSCs for therapeutic applications.