(3ci) Optimal Design and Control of Advanced Biomanufacturing Systems

Biopharmaceuticals are products derived from biological organisms for treating or preventing diseases. The global sales of biopharmaceuticals have continually increased for many years and will continue to increase, as the pipeline of product candidates continues to grow as more diseases are understood at the molecular and cellular levels. For the drug candidates to move into commercial production, manufacturing processes must be developed with product and process understanding and control to produce high-quality products at lower cost. In fact, the lack of process development and manufacturing capacity is a bottleneck in the development of viral vaccines, which is also evident from the current Coronavirus Disease 2019 (COVID-19) epidemiological situation.

Recent trends in biopharmaceutical manufacturing provide opportunities for process systems engineering to make major advances in biomanufacturing: (1) process analytical technology (PAT) is providing on-line measurements of critical quality attributes (CQAs) for constructing first-principles and data-based models of each unit operation and enable advanced control, (2) a transition from batch to continuous operation provides process control needs to handle the propagation of impurities and other disturbances caused by tight integration of unit operations, and (3) the invention of new designs for downstream processes is creating new processes to control.

During my Ph.D. research at Massachusetts Institute of Technology (MIT), I derived mathematical models and optimal control methods for multiple bioreactor configurations including fed-batch stirred-tank bioreactors and perfusion microbioreactors. I also designed and implemented laboratory systems for protein crystallization and continuous viral inactivation, which are optimally designed and controlled based on mathematical models that I developed. In addition, I am combining my experimentally validated individual unit operations into a plant-wide dynamic model to map the raw materials and operations to the product quality attributes and other variables of interest anywhere in the system.

In my own independent research program, I will build on my experience to address key biomanufacturing needs of today, with an initial focus on viral vaccines and gene therapy. Viral vaccine is the dominant class of vaccine because it activates all phases of the immune system and provides the most durable immunity, and is used for measles, mumps, flu, rubella, varicella (chickenpox), smallpox, polio, rotavirus, and yellow fever [1]. The limitations of current viral vaccine manufacturing are the egg-based technology is not quickly scalable to address the vaccine needs of pandemics, the suspension bioreactor technologies have had low productivity, and the adherent bioreactor technologies have had poor scalability. Preliminary results suggest that order-of-magnitude can be made to both bioreactor technologies to produce highly flexible and low-cost manufacturing.

Gene therapy is the introduction of nucleic acids into a patient’s cells, which then locally manufactures the proteins to treat the disease. Gene therapy treatment has become highly successful recently but the current manufacturing technology has high failure rates and very high costs (current treatments cost between $373K to $1M per person) [2]. Gene therapy manufacturing is based on viruses, just like viral vaccine manufacturing, which suggests that the right combination of novel PAT, unit operation designs, and advanced control strategies could make order-of-magnitude increase in process reliability and reductions in manufacturing costs.

[1] List of Vaccines Used in United States, Centers for Disease Control and Prevention, Atlanta, Georgia, April 13, 2018. https://www.cdc.gov/vaccines/vpd/vaccines-list.html

[2] Emily Mullin, Tracking the Cost of Gene Therapy, MIT Technology Review, October 24, 2017. https://www.technologyreview.com/2017/10/24/148183/tracking-the-cost-of-...

Publications:

https://scholar.google.com/citations?user=616cjRQAAAAJ&hl=en&oi=ao

Teaching Interests:

I have served as a teaching assistant (TA) for the graduate-level Systems Engineering at MIT. The course is for introducing students to Process Systems Engineering and its application in Chemical Engineering Practice. The topics include systems analysis, process simulation software (Aspen Plus), experimental design, applied optimization, and data analytics. My recitation displayed thorough knowledge of subject material, helped students learn, and stimulated their interest (overall rating from subject evaluation: 6.2/7.0; highest among instructors). I have also completed the Subject Design Certificate Program from Teaching and Learning Lab (TLL) to learn the fundamentals of designing college-level courses including how to define learning outcomes, select appropriate assessments, create an inclusive classroom, and write a syllabus. I am interested in teaching students a wide range of core courses in Chemical Engineering including Process Design and Control, Reaction Engineering, Transport Processes, and Engineering Mathematics. In addition, I am passionate about developing or improving elective courses in the fields of Process Systems Engineering and Advanced Manufacturing.