Applying Nonexperimental Study Approach to Analyze Historical Batch Data

Small Molecule Pharmaceutical Development, Genentech, Inc., South San Francisco, California 94080, USA.
Journal of Pharmaceutical Sciences (Impact Factor: 2.59). 05/2012; 101(5):1865-76. DOI: 10.1002/jps.23066
Source: PubMed


One common challenge in pharmaceutical product development is mapping the potential effects of a large number of variables. Conventional experimental tools such as design-of-experiment (DoE) approach demand study scales too large to be practical. In comparison, nonexperimental studies have the advantage to evaluate a large number of variables, but may suffer from the inability to define causal relationships. Given this situation, the current study sought to divide the mapping operation into two steps. The first step screens out potential significant variables and confirms the causal relationships, and the second step involves DoE studies to define the design space. This report demonstrates that nonexperiments can be effectively applied in the first step. The screening task was performed on the nonexperimental dataset consisting of data collected from historical batches manufactured as clinical testing materials. A combination of statistical analysis and technical assessment was applied in the screening. By invoking the variable selection procedure embedded in the multivariate regression analysis, the significance of variables to the responses was assessed. Potential technical mechanisms and variable confounding were then examined for the significant correlations identified. Experimental confirmation was performed to confirm the causal relationships. The last two measures were introduced to remedy the weakness of the nonexperiments in defining causal relationships. Through this effort, the relationships among a large number of variables were quantitatively evaluated and the variables of potential risks to product quality and manufacturability were identified. The results effectively directed further DoE studies to the high-risk variables. Overall, the nonexperimental analysis improve the mapping efficiency and may provide a data-driven decision-making platform to enhance quality risk assessment.

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