Research

Continuous Cell Culture for Viral Vaccines

Even as the stakes for vaccine development have risen, the manufacturing technology for efficiently producing vaccines flexibly and at scale have lagged behind. MIT, together with Merck & Co., Repligen, and the University of Massachusetts Medical School will use mechanistic modeling to design and construct a continuous vaccine manufacture process. Successful completion of this project will demonstrate the suitability of continuous manufacturing for vaccine production, with potentially transformative effects on supply chains and response times, along with improved product quality attributes.

MIT Investigators: Prof. Richard Braatz (Chemical Engineering); Prof. Scott Manalis (Biological Engineering & Koch Institute for Integrative Cancer Research); Prof. Anthony Sinskey (Biology); Dr. Stacy Springs (Center for Biomedical Innovation)
Partners: Merck & Co., Repligen, and University of Massachusetts Medical School
Funding: National Institute for Innovation in Manufacturing Biopharmaceuticals (NIIMBL) and the Massachusetts Life Science Center (September 2019)
Related Links: https://niimbl.force.com/s/news/a0a6A000002xxq7QAA

Continuous Viral Vector Manufacturing Based on Mechanistic Modeling and Novel Process Analytics

Along with the commercialization of the initially-approved gene therapies, and reports of clinical success in products still in the pipeline, have come concerns about the vector supply chain. Future gene therapies may require significantly more vector and/or treat much larger numbers of patients than the currently approved products. Current production capacity for clinical-grade vector is relatively low and there a clear requirement for additional cGMP grade vector production capacity. Continuous manufacturing is one approach to achieving higher productivity, increased product quality, and, potentially, better utilization of production infrastructure. However, it is still in the early stages of being applied to viral vector manufacturing. To help address this gap, we are developing and demonstrating a continuous upstream viral vector manufacturing platform by developing a first principles mathematical model for continuous viral vector cell culture unit operation process design; developing and demonstrating novel analytics for the in-line measurement of vector production parameters; and leveraging these learnings to demonstrate the continuous cell culture production of viral vectors.

MIT Investigators: Prof. Richard Braatz (Chemical Engineering); Prof. Scott Manalis (Biological Engineering & Koch Institute for Integrative Cancer Research ); Prof. Anthony Sinskey (Biology); Dr. Stacy Springs (Center for Biomedical Innovation )
Funding: FDA Center for Biologics Evaluation & Research (September 2018)
Related Links: http://www.biopharminternational.com/fda-awards-five-grants-advanced-biomanufacturing-research-0

Model Predictive Control for Improved Product Quality in the Production of Viral Vectors

Viral vectors are considered the vehicle of choice in gene therapies and gene-modified cell therapies to deliver genetic material into a specific cell. However, viral vector production can represent up to 40% of the cost of goods of gene-modified cell therapies. Vector production processes can be inconsistent in how many virus capsids actually get filled with the target DNA material, rendering many of the viral capsids with no therapeutic value. Current production processes remove empty capsids from the viral harvest through additional purification steps, adding cost, time, and complexity, while reducing viral yields. In collaboration with MilliporeSigma, Sanofi and Artemis Biosystems, we are using mechanistic modeling to improve the vector production and purification processes. This project will result in improvements in viral vector product quality and reduced production costs which are needed to enable large-scale adoption of these types of therapies.

MIT Investigators: Prof. Richard Braatz (Chemical Engineering); Prof. Scott Manalis (Biological Engineering & Koch Institute for Integrative Cancer Research); Prof. Anthony Sinskey (Biology); Dr. Stacy Springs (Center for Biomedical Innovation)
Partners: MilliporeSigma, Sanofi, and Artemis Biosystems
Funding: Massachusetts Life Science Center (May 2019)
Related Links: http://www.masslifesciences.com/massachusetts-life-sciences-center-awards-funding-to-projects-to-enable-biomanufacturing-innovation/#more-8325

Smart Data Analytics for Risk Based Regulatory Science and Bioprocessing Decisions

This project will advance the regulatory science around the increasingly common practice of applying advanced data analytic solutions to challenges encountered throughout biomanufacturing operations. At the level of the manufacturing process, we are developing and validating tools that help manufacturers select the appropriate mathematical models to describe their processes and upon which to base their control strategies. We are developing tools which can quantitatively assess the process risks associated with their choice of manufacturing model, and are validating these tools experimentally in a continuous monoclonal antibody manufacturing testbed that features state-of-the art process equipment and in-line and at-line analytics. This project also applies advanced data analytics such as machine learning, natural language processing, and artificial intelligence to incorporate all of the data sources available within the manufacturing plant and the biomedical ecosystem to develop tools to optimize manufacturing operations and to use all of the data available to ensure the reliable supply of safe and effective biologic medicines.

MIT Investigators: Prof. Richard Braatz (Chemical Engineering); Prof. Retsef Levi (Sloan School of Management); Prof. Anthony Sinskey (Biology); Dr. Stacy Springs (Center for Biomedical Innovation)
Funding: FDA Center for Drug Evaluation & Research (September 2018)
Related Links: https://www.fda.gov/news-events/fda-brief/fda-supports-critical-research-spur-innovation-continuous-manufacturing-technology-support-and

Singapore-MIT Alliance for Research and Technology – Critical Analytics for Manufacturing Personalized-Medicines (SMART CAMP)

SMART CAMP is addressing key challenges in the manufacture of personalized cell therapies for patients – identifying the critical quality attributes of a cell which contribute to its therapeutic safety and efficacy, and rapidly monitoring and controlling these attributes to improve the ability to manufacture these medicines. Two of SMART CAMP’s Flagship Projects are identifying critical quality attributes for product potency and efficacy in multiple cell types, and developing novel process analytical technologies to assess these attributes. The third Flagship, co-led by Dr. Stacy Springs, is developing novel analytical technologies for rapid assessment of adventitious agent contamination to improve the safety of cell therapy products.

MIT Founding Co-Lead Principal Investigator: Prof. Krystyn Van Vliet (Biological Engineering, Materials Science & Engineering);
MIT Flagship Co-Captains: Prof. Jongyoon Han (Biological Engineering, Electrical Engineering & Computer Science), Prof. Rajeev Ram (Electrical Engineering & Computer Science), Dr. Stacy Springs (Center for Biomedical Innovation)
MIT Principal Investigators: Prof. George Barbastathis (Mechanical Engineering), Asst. Prof. Michael Birnbaum (Biological Engineering, also on CAMP Council), Assoc. Prof. Laurie Boyer (Biology, Biological Engineering, Koch Institute for Integrative Cancer Research); Prof. Patrick Doyle (Chemical Engineering); Prof. Nicholas Fang (Mechanical Engineering); Assoc. Prof. A. John Hart (Mechanical Engineering); Prof. Timothy Lu (Biological Engineering, Center for Synthetic Biology, Broad Institute); Prof. Peter So (Biological Engineering, Mechanical Engineering); Prof. Michael Strano (Chemical Engineering); Prof. Bruce Tidor (Biological Engineering, Electrical Engineering & Computer Science); Assoc. Prof. Xuanhe Zhao (Mechanical Engineering)
Funding: Singapore National Research Foundation (June 2019)
Related Links: https://camp.smart.mit.edu/team