Leveraging mathematical models for optimizing filter utility at manufacturing scale.

Area of Science:

• Bioprocess Engineering
• Filtration Technology
• Pharmaceutical Manufacturing

Background:

• Biopharmaceutical production relies on depth filters and sterile filters to remove cell debris.
• Global supply chain disruptions have limited the availability of critical depth filters.
• Facilities face challenges managing filter inventory, risking batch failures or plant shutdowns.

Purpose of the Study:

• To develop a modeling-based method for predicting depth filter performance under reduced filter numbers and increased flux.
• To quantify the acceptable reduction in filter area without compromising filtration performance.
• To provide a strategy for managing depth filter inventory during supply constraints.

Main Methods:

• Leveraging manufacturing-scale filtration data for model development.
• Implementing predictive modeling to assess filter performance with reduced area and higher flux.
• Analyzing filtration data to determine critical thresholds for filter area reduction.

Main Results:

• The developed model accurately predicts depth filter performance with fewer filters and increased operational flux.
• Quantifiable limits for acceptable depth filter area reduction were established.
• The method demonstrated the ability to maintain process performance under constrained filter availability.

Conclusions:

• A modeling approach enables effective management of depth filter inventory during supply shortages.
• This strategy mitigates risks of batch failures and plant shutdowns in biopharmaceutical manufacturing.
• Optimized filter usage ensures continued production of biologics despite supply chain constraints.