Improving Bioprocess Monitoring And Control With Multivariate Data Analysis

Traditional biomanufacturing monitoring depends heavily on tracking individual critical process parameters (CPPs) and quality attributes (CQAs), making it time-consuming and prone to delays in decision-making. Recent advances in multivariate data analysis (MVDA) offer a transformative approach, enabling proactive, near real-time monitoring, control, and prediction of batch quality and productivity. Techniques like principal component analysis (PCA) and partial least squares (PLS) regression extract insights from complex datasets, streamlining analysis and supporting informed decisions. PCA reduces data dimensionality, allowing process engineers to monitor overall process health through simplified visualizations like scores and contribution plots. Meanwhile, PLS regression predicts future batch states, ensuring early identification of potential issues.

A key MVDA application is process monitoring tunnels, which graphically represent process progression and health across multiple stages or within a single stage. These tunnels leverage historical successful runs to define optimal ranges, helping engineers quickly assess deviations, predict outcomes, and optimize processes. Inter-stage tunnels monitor transitions between operations like bioreaction and filtration, while intra-stage tunnels track progress within a unit operation, such as a bioreactor.

Adopting MVDA-enabled tools, such as Bio4C ProcessPad™ software, enhances efficiency, accelerates root-cause investigations, and improves compliance with quality by design (QbD) and process analytical technology (PAT) principles. By combining PCA and PLS for predictive insights, biomanufacturers can move toward continuous, adaptive operations, meeting the demands of cost, sustainability, and regulatory standards in modern biomanufacturing. These innovations ultimately improve biologic drug production while ensuring consistent quality and scalability.