Industry 4.0 in Biomanufacturing: Predictive Real-Time Models Using Process Analytical Technology

In biomanufacturing, process analytical technology (PAT) has become an essential tool for improving product quality, reducing costs, and increasing efficiency. In this thesis, we collect capacitance and optical density data from two in-line sensors in production bioreactors to compute real-time readings of viable cell density (VCD) and viability, two critical metrics that drive product quality and batch yield. Comparing predictions with manually collected samples, a Gaussian Process Regressor model with Matern Kernel (nu=0.5) is found to be optimal, achieving an MAPE of 7.46%, well within 10% error as defined by Amgen process development scientists. We then utilize this VCD model in conjunction with the optical density probe, which measures total cell density (TCD), in a novel way to obtain real-time measurements of viability within 5% of offline measurements conducted using a cell counter. Our results demonstrate the effectiveness of using real-time sensor data and ML models for monitoring critical quality attributes in biomanufacturing. This will enable an estimated $2M per year in savings in avoidable product losses in Amgen’s new manufacturing plant in North Carolina, approximately 50% reduction in manual sampling efforts, and offer further process improvement opportunities, particularly for advanced process control. This use case demonstrates the potential of PAT for improving biomanufacturing processes.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2023

Author dc:creator

• Murr, Michaela

Advisors dc:contributor.advisor

• Boning, Duane S.
• Jónasson, Jónas O.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/151710

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/151710