Improving prior knowledge assessment in process characterization

A critical aspect of biologics manufacturing is creating a safe, reliable and consistent manufacturing process. The manufacturing process design includes process characterization (PC) experiments to demonstrate process robustness and provide data necessary for planning, risk mitigation, development of the control strategy, and successful execution of process validation. Performing PC experiments is resource intensive, both human and capital, so leveraging prior knowledge from previous experiments is essential. Until now, using data from past experiments data relied on a centralized static document called Prior Knowledge Assessment (PKA). The PKA aggregates the results of many statistical models that were created during past PC studies. Using the PKA provides insight, but leaves a lot of room for subjective decision making around questions, such as: How should products be grouped together? and What operating parameters are more important? The PKA also lacks uncertainty quantification for statistical significance. In this thesis, we aggregated data from past PC experiments across multiple molecules, and developed a machine learning framework to holistically analyze cross-product data from process characterization DOE studies. The model developed through this project provides interpretable predictions of sensitivity of Performance Indicators to Process Parameters variation. The model enables, for the first time, to assess and quantify the impact of parameters on indicators, even if they were not tested originally for a specific molecule. A novel user interface was created in order to bring the framework to life and create a "one-stop shop" for a scientist to interact with the model. This work improves process characterization decision quality. Potential benefits of this approach would be to increase speed and agility in process development and reduce number of future experiments.

Name thesis:degree_name

Master

Department dc:contributor.department

Sloan School of Management

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Dan, Or.

Advisor dc:contributor.advisor

• Retsef Levi and John N. Tsitsiklis.

• Sloan School of Management.
• Operations Research Center.
• Leaders for Global Operations Program.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/126952

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/126952