Traditionally, the Six Sigma framework has underpinned quality improvement and assurance in biopharmaceutical manufacturing process management. This paper proposes a neural network (NN) approach to vaccine yield classification and compares it to an existing multiple linear regression approach. As part of the Six Sigma process, this paper shows how a data mining framework can be used to extract further value and insight from the data gathered during the manufacturing process, and how insights into yield classification can be used in the quality improvement process.
Tag: <span>pat</span>
Implementation of āreal-timeā analytics (RTA) in processes for biologics is challenging from a technological and timeline perspective. Therefore, there need to be significant drivers from both a regulatory (quality) and a monetary standpoint to justify investment. Understanding how regulatory agencies define real-time analytics and the expectations for implementations (how and when) is a key component to rational decision-making, and dovetailing process improvement and facility design is important in the planning and development processā¦
Biologics Production Cell & Gene Therapy Cell Lines Mammalian Cell Culture Manufacturing Regulatory Viral Reference Materials Viral Vectors
at-line analytics biolgics design space biologics cpp critical process parameters design-of-experiments doe fda guidance fmea in-line analytics ind investigational new drug mabs monoclonal antibodies on-line analytics pat process analytical technology real-time analytics regulatory considerations rta