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Protein A Chromatography Multi-scale Data Driven/Mechanistic Modeling (Phase 2)

Develop a hybrid modeling infrastructure for simulating protein A chromatography performance and predicting process/product related impurities in the manufacture of monoclonal antibodies
Categories
Proteins/ Antibodies
Drug substance
Process control
Data
Project status
71% Completed

Industry Need

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

Solution

Use Protein A 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)

Impacts

Reduce process development time and cost of Antibody products

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Project Lead

Rensselaer Polytechnic Institute

Rensselaer Polytechnic Institute

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