Enhanced rAAV Production Monitoring With the BioPAT^® Platform Via the Umetrics^® Digital Twin AI Ecosystem and SIMCA^®-online

Recombinant adeno-associated virus (rAAV) vector production is a complex process in which robust cultivation of human embryonic kidney cells (HEK293) is critical to generate high-quality viral vectors. Process analytical technology (PAT) sensors provide real-time, in situ measurements of critical process parameters that can be used to monitor and automate the process via control software. Enhanced utilization of the information from these sensors can be achieved using tools with multivariate data analysis capabilities, such as SIMCA^®-online, part of the Umetrics^® Digital Twin AI Ecosystem. These tools condense complex data into easy-to-understand plots for on-time decision making. Integrated application of these advanced technologies improves the consistency of production and ensures higher viral vector quality and yield. This study provides guidelines for successful integration of the BioPAT^® Viamass sensor, Biobrain^® Supervise software, and SIMCA^®-online to provide enhanced understanding and monitoring of rAAV upstream production. We highlight the critical importance of such technologies to meet the growing demand for process optimization, reduce process variability, and ensure compliance with regulatory standards for gene therapy processes. Ultimately, this example demonstrates the potential of integrated solutions to achieve more effective, reliable, and scalable gene therapy applications that contribute to improved patient outcomes and broader therapeutic possibilities