(567c) Statistical Modeling and Analysis for Safety and Efficacy of Biological Products

Authors: 
Yoon, S., University of Massashusetts
Lawton, C., University of Massachusetts, Lowell
Xu, J., BioManufacturing Center, University of Massachusetts, Lowell
Liu, J. J., Pukyong National University


FDA's new guideline (FDA-2008-D-0059) proposes a new paradigm for manufacturing of biological products intended for human or animal use. The new paradigm requires that manufacturing processes be designed and controlled to assure that in-process materials and finished products meet predetermined quality requirements. It also requires a thorough understanding of the relationship between the critical quality attributes (CQA) and the clinical properties of the product, the relationship between the process and CQA, and the variability in raw materials. So far, benefits and associated flexibility expected from the new initiative implementation have not been fully realized. Biologics manufacturers are still facing challenges due to variations from raw material ingredients, lack of appropriate measurements of intermediate process parameters, and even inappropriate analytical test methods of final product quality attributes. Recent research shows that these challenges can be answered by advanced analytical tools combined with multivariate statistical models.

The proposed study will illustrate how to characterize raw material variation and its impact in cell-culture process. Challenges in measuring and estimating product quality attributes during the biologics manufacturing process will be addressed. High-throughput assays for glycosylation and aggregation will be considered, and the statistical modeling framework for improving productivity and efficiency will be demonstrated. The research will provide the scientific and regulatory community with better approaches to evaluating safety and efficacy of biological products. Statistical modeling framework can explain correlation between process variations and drug safety and efficacy, integrate various types of information from evolving analytical technologies, and apply this information to enhance and inform regulatory evaluation and decision-making.