Cell therapy bioprocessing often relies upon equipment repurposed from biologics. However, the different output requirements of the two products—obtaining maximum yield for biologics vs preserving cell viability for cell therapies—can make automation and scale-up difficult and sometimes requires processes to be re-engineered. New equipment specifically designed for cell therapy processing is more effective and efficient.

The question for manufacturers, then, is how to evaluate this new equipment and de-risk its implementation.

In a recent paper, Uma Lakshmipathy, PhD, senior director of R&D, Thermo Fisher Scientific, and colleagues outline a framework to guide the selection and deployment of cell therapy processing equipment.

Although the key considerations are similar to those for biologics, she says, “The evaluation process for equipment used for cell therapy is more involved and is specific for the cell type and intended application, which dictates scale-up or scale-out strategies and manipulation needed to generate the cell product.”

Meeting good manufacturing practice (GMP) requirements and ensuring good performance and successful use in clinical trials isn’t enough.

Scoring system sparks discussion

The paper presents a scoring system, details its use in a hypothetical comparison of three products, and discusses the rationale for the scores as well as the tradeoffs and considerations involved in evaluating each of the products.

“This may result in a device with a clear advantage or propose many options for a given operation,” the authors wrote. The framework isn’t designed to make the selection decision, but to lend objectivity to the evaluation and subsequent discussions. As they note, new equipment may score lower than more prominent devices yet, for some applications, may be better suited.

Additional considerations, not included in the scoring framework, also are discussed, including scalability, the ability to handle modular processes or single-step operations, dual-interface developer/producer modes for flexibility, compatibility with reagents, and how the equipment affects the overall process.

“The most important thing to consider is translation,” Lakshmipathy says. Starting with the end in mind requires processes and analytics to be established early. “It is critical to use high-quality materials and precise analytics for candidate selection and product definition during late-stage discovery and early-stage clinical development.”

Consider, therefore, the manufacturing strategy during the product’s initial stages of development. A clear understanding of the eventual trial design early on shapes the manufacturing strategy and, therefore, influences equipment choices. This derisks the process of product generation for preclinical stages all the way through to commercial manufacturing.

Previous articleBeating Scientific Biases in Bioprocessing
Next articleEMA Predicts Biopharma Industry to Increase Its Use of AI