Applying Nonexperimental Study Approach to Analyze Historical Batch Data
ABSTRACT 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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ABSTRACT: Multivariate data analysis methods such as partial least square (PLS) modeling have been increasingly applied to pharmaceutical product development. This study applied the PLS modeling to analyze a product development dataset generated from a design of experiment and historical batch data. Attention was paid in particular to the assessment of the importance of predictor variables, and subsequently the variable selection in the PLS modeling. The assessment indicated that irrelevant and collinear predictors could be extensively present in the initial PLS model. Therefore, variable selection is an important step in the optimization of the pharmaceutical product process model. The variable importance for projections (VIP) and coefficient values can be employed to rank the importance of predictors. On the basis of this ranking, the irrelevant predictors can be removed. To further reduce collinear predictors, multiple rounds of PLS modeling on different combinations of predictors may be necessary. To this end, stepwise reduction of predictors based on their VIP/coefficient ranking was introduced and was proven to be an effective approach to identify and remove redundant collinear predictors. Overall, the study demonstrated that the variable selection procedure implemented herein can effectively evaluate the importance of variables and optimize models of drug product processes. © 2012 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci.Journal of Pharmaceutical Sciences 12/2012; 101(12). DOI:10.1002/jps.23322 · 3.01 Impact Factor