Effect of statistical screening

Because of the complexities involved in understanding cause-effect relationships, an alternative approach to control the thin film microstructure has been pursued by some investigators—the use of statistically designed experiments to identify key processing parameters.114115 In these approaches, as illustrated in Table 2.6 for a Plackett-Burman screening study,114 limiting values for various experimental parameters are chosen. Films are then prepared from solutions synthesized under these conditions, and resulting film... 

By comparison of arithmetic values of the t-criterion with tabular ones, we can see that the effects of factors Xj, X4 and X7 are statistically significant. Screening then, continues by response correction or by annulling the screened out effects and repeating the procedure. Finally, the following effects were screened out and compared to additional effects in the phase of defining this example. 

For a two-level factorial design, only two excipients can be selected for each factor. However, for the filler-binder, a combination of brittle and plastic materials is preferred for optimum compaction properties. Therefore, different combinations such as lactose with MCC or mannitol with starch can count as a single factor. Experimental responses can be powder blend flowability, compactibility, blend uniformity, uniformity of dose unit, dissolution, disintegration, and stability under stressed storage conditions. The major advantage of using a DOE to screen prototype formulations is that it allows evaluation of all potential factors simultaneously, systematically, and efficiently. It helps the scientist understand the effect of each formulation factor on each response, as well as potential interaction between factors. It also helps the scientist identify the critical factors based on statistical analysis. DOE results can define a prototype formulation that will meet the predefined requirements for product performance stability and manufacturing. 

The ruggedization of the analytical procedure was performed by applying statistical screening techniques to minimize the effort required and, therefore, reduce the time and the cost substantially. The statistical approaches used in this study were those first introduced by Plackett-Burman (3.) and Youden-Steiner (4). Both techniques reduce the required effort since they use balanced incomplete block design experiments which can clearly indicate the non-affecting parameters from those that may have an effect. In this study the important variables of the analytical method were identified by using the Plackett-Burman technique. 

Shortfalls of the common control-based HTS data analysis approach have been suggested (Gribbon et al., 2005 Malo et al., 2006). For example plate-based normalization using controls (Table 14.1) intrinsically assumes a random error distribution for all wells in a plate because of the positional bias of the controls dictated by the formats of common screening libraries. Edge effects are particularly relevant in cell-based assays and vary from plate to plate (Malo et al., 2006). Classical HTS analysis relies on non-robust statistics means and standard deviations are greatly influenced by outliers. 

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