Engineering Consortium Focuses on Cell Therapy Production

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As a new consortium of researchers, clinicians, and engineers from academia, industry, and government work toward a common goal of expanding the use of cell therapies, engineers are playing a critical role in developing proper manufacturing techniques and standards for mass production for this emerging industry.
 
To highlight the important role engineers will play in the industry, the National Science Foundation (NSF) last fall awarded nearly $20 million to support a new engineering research center to facilitate mass production.
 
The grant was awarded around the same time the Food and Drug Administration approved the first two gene therapy treatments, Novartis’s Kymriah and Kite’s Yescarta, which were followed by the approval of Spark Therapeutics’ Luxturna in December. More than 300 biotech and pharmaceutical companies are currently working on cell and gene therapy products.
 
The skills needed to meet the manufacturing complexities, scale and quality assurance in the production of those therapies is creating a demand for engineering expertise.
 
“It’s time has come because these products are now coming onto market without any clear path on how people can manufacture them for a broader base,” says Krishnendu Roy, director of the recently established NSF Engineering Research Center for Cell Manufacturing Technologies (CMaT) and Marcus Center for Cell Manufacturing at Georgia Institute of Technology. The Atlanta-based university leads a consortium that includes other universities in the U.S. and abroad, industry, and U.S. national laboratories.
 
“We started thinking in this area about three or four years ago,” Roy says.
 
One of the first major steps in creating the consortium was developing a 60-page, 10-year roadmap that laid out challenges and barriers, as well as a technological wish list for the next 10 years. More than 25 companies and 15 academic institutions collaborated to produce the roadmap.
 
CMaT’s mission is to bring together a highly multi-disciplined group of researchers, engineers, clinicians, and biologists to develop new technologies for the production of high-quality therapeutic cells on a large scale, and to train those working in biotechnology and manufacturing in this area.
 
The NSF award abstract formally describes the project as a “convergence-science effort where engineers will work closely with industry partners, clinicians, biologists, and workforce experts, as well as standards and regulatory agencies to transform the production of therapeutic cells into a large-scale, low-cost, reproducible, and high-quality engineered manufacturing process.” Addressing those engineering issues is the industry’s greatest challenge, according to one of the roadmap’s key findings.
 
“Fundamentally, this isn’t very different from manufacturing a car or an airplane in terms of the processes, although it’s far more complex because you are manufacturing a living product that changes its properties with every manipulation and process change,” Roy says.
 
Reproducibility and quality control of cells during large-scale manufacturing has been a pressing concern.
 
“When you go from treating 10 or 50 patients to 10,000 or 100,000 patients, you are getting into the engineering domain of manufacturing,” Roy says. “It’s unlike anything we have ever manufactured before.
 
The sophisticated techniques and accepted standards used in manufacturing of goods to control and assure product consistency and quality in areas such as raw material control and characterization, supply chain robustness, distribution logistics, process modeling and scalability, and measurement tools aren’t consistently used in the cell and gene therapy production industry, experts say.
 
To help improve those shortcomings, CMaT is focusing on three areas: what to measure, how to measure, and process scalability, which includes distribution logistics.
 
“We are taking essentially a big-data approach, characterizing the cells in any possible way known to man and bringing in concepts of predictive modeling, big data, artificial intelligence - all engineering concepts – with a goal to figure out the critical properties of the product and process to generate predictive parameters that in day one or day two of manufacturing could tell us whether we will have a successful, potent, and safe batch,” Roy says.
 
The second area focuses on tool development for measurement, such as sensors, sampling techniques, and microfluidic tools. “As in product manufacturing, we need to monitor and understand the quality of the product throughout the manufacturing process – preferably in real time.” Roy says.
 
The third area will bring together engineers typically involved in scalable manufacturing and supply chain modeling, but with additional bio training since manufacturing and industrial engineers do not usually have much knowledge of biology and cells, Roy says. On the other hand, bioengineers need to know more about how to integrate sensors and model processes, how to build robust supply chains and logistics, and how to do data-driven manufacturing. The roadmap revealed that one of the industry’s biggest bottlenecks is the lack of a well-trained multi-disciplinary workforce.
 
“Workers in this area need a broad range of skills and knowledge and hands-on experience with scalable cell bioprocessing and analytics,” Roy says. “The current workforce does not have that.”
 
The work will involve developing a complete engineered system applied to three different test-beds that are of most interest today: regenerating tissue, developing immunotherapy for cancer and other diseases, and treating heart diseases.
 
“These are incredible treatments coming out,” Roy says, adding that an important question remains.
 
“How do you take these truly life-saving therapies and make them accessible to people of all nations, regardless of their socio-economic status?” Roy says. “How do you bring these to the masses at an affordable cost? Robust, low-cost, reproducible manufacturing should be a part of the solution.”
 
Nancy S. Giges is an independent writer.