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: In quality by design (QbD) paradigm, specifications on active pharmaceutical ingredients (APIs) are a critical component of the overall control strategy to ensure drug product quality. In establishing appropriate specifications for highly correlated API properties, multivariate specifications were advocated recently (Duchesne C, MacGregor JF. 2004. J Qual Technol 36:78-94). In this text, we reviewed several scenarios where API properties are of varying degrees of intercorrelation, and discussed the corresponding control strategies. One scenario was further analyzed, in which high degree of property intercorrelation could afford a single univariate specification and, thereby, simplify the control strategy. In the case study provided, we first mapped the potential design space of the API physical properties, and subsequently compared the effectiveness of univariate and multivariate control strategies. On the basis of the comparison, a single univariate control scheme was proposed and boundary was defined. Finally, width of the design space for API physical properties was assessed, and the effectiveness of the API manufacturing process control was preliminarily evaluated.Journal of Pharmaceutical Sciences 09/2011; 101(1):312-21. · 3.13 Impact Factor