Recent advances in data analytics and process analytical technologies (PAT) have enabled real-time monitoring and control of parameters, improving quality and robustness for both batch and continuous processes. This keynote will describe the current state of process control, modeling, and analytics for biologic drug manufacturing. The presentation will encompass process control, mechanistic modeling, data analytics and machine learning, the implementation of PAT, and real-time release testing (RTRT). The effective application of advanced statistics and process modeling approaches, performance monitoring, and deployment of automated feedback/feedforward control will be discussed. The technologies, methodologies, and key points will be illustrated by applications to monoclonal antibody, viral vaccine, and gene therapy manufacturing in collaboration with university and industrial partners.
Discuss an effective strategy for applying data analytics and machine learning methods to biomanufacturing data
Discuss the relative roles and optimal balance between mechanistic modeling, data analytics, and hybrid modeling approaches
Explore connections to Industry 4.0, digital twins, and AI-based approaches for data analytics tool selection
Describe a systematic approach for design of control and real-time release testing strategies
Describe some specific applications of process control, modeling, and analytics to batch and continuous biomanufacturing processes