Decision-Support Tools for Monoclonal Antibody and Cell Therapy Bioprocessing: Current Landscape and Development Opportunities
Figure 1: Summary of discussion
Industrial-scale manufacturers in a number of fields — from automobiles to biotherapeutics — have long relied on powerful computational and mathematical tools to aid in the scale-up, optimization, quality control, and monitoring of product development (1–5). Typical process pathways are highly multifactorial, with numerous branch points, feedback steps, instrumental attributes, and target parameters. Moreover, margins for error are minimal for most industrial processes, requiring high standards of precision from industrial and operational pathways (6). For those reasons, the complexity of process engineering and process pathway design necessitates that modeling and decision-support tools (DSTs) be used to ensure high-quality and economically viable end products.
Biologics are no exception to this trend of growing DST use in industrial processes. Compared with classical (small-molecule) pharmaceuticals, biopharmaceuticals exhibit more complex, more expensive, and more delicate production pathways, with long development times and stringent quality standards (7