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Cell Culture Glycosylation Multi-scale Data Driven Mechanistic Modeling (Phase 2)

Application of Next-generation Modeling of Glycosylation to Adaptive Process Control of CQAs in Fed-batch CHO Cell Culture
Categories
Proteins/ Antibodies
Drug substance
Process control
Data
Project status
69% Completed

Industry Need

Laboratory experiments increase new product development cost. Special experiments for predictive model calibration reduces benefits of models.

Solution

Use Glycosylation modeling to reduce laboratory experimentation. Calibrate predictive model using AI/ML rather than experiments.

Outputs/Deliverables

  • Modelling Software in Open-Source platform (2Q2024)
  • Model Use Training documentation (2Q2024)
  • Presentation – Model-based Design of Experiments for Glycosylation in CHO Cell Culture – 2023 AIChE Orlando, FL (Jing, Ma, Braatz (MIT))
  • Presentation – GlycoPy: A Multiscale Model-based Simulation, Optimization and Optimal Python Package for mAb Glycosylation in Cell Culture – 2023 AIChE Orlando, FL (Ma, Jing, Braatz (MIT))
  • Presentation – Model-based analysis of glycosylation across cell lines – 2023 BPI Boston, MA (Jing, Ma, Braatz (MIT))

Impacts

Reduce process development time and cost of Antibody products

Additional Project Information (Members Only)

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Project Lead

Massachusetts Institute of Technology

Massachusetts Institute of Technology

Participating Organizations

AstraZeneca

AstraZeneca

Boehringer Ingelheim

Boehringer Ingelheim

Bristol-Myers Squibb

Bristol-Myers Squibb

Cytiva

Cytiva

Federal Stakeholder:  National Institute of Standards and Technology

Federal Stakeholder: National Institute of Standards and Technology

Genentech

Genentech

Merck Sharp & Dohme LLC

Merck Sharp & Dohme LLC

MilliporeSigma/EMD Serono

MilliporeSigma/EMD Serono

NIIMBL

NIIMBL

Pfizer, Inc.

Pfizer, Inc.

Sanofi

Sanofi