The Usefulness of Definitive Screening Design for a Quality by Design Approach as Demonstrated by a Pharmaceutical Study of Orally Disintegrating Tablet

Abstract

Definitive screening design (DSD) is a new class of small three-level experimental design that is attracting much attention as a technical tool of a quality by design (QbD) approach. The purpose of this study is to examine the usefulness of DSD for QbD through a pharmaceutical study on the preparation of ethenzamidecontaining orally disintegrating tablet. Model tablets were prepared by directly compressing the mixture of the active pharmaceutical ingredient (API) and excipients. The five evaluated factors assigned to DSD were: the contents of API (X1) and lubricant (X2), and the compression force (X3) of the tableting process, the mixing time (X4), and the filling ratio of powder in the V-type mixer (X5). After tablet preparation, hardness and disintegration time were measured. The same experiments were performed by using the conventional design of experiments [i.e., L8 and L16 orthogonal array designs and central composite design (CCD)]. Results showed that DSD successfully clarified how various factors contribute to tablet properties. Moreover, the analysis result from DSD agreed well with those from the L8 and L16 experiments. In additional experiments, response surfaces for tablet properties were created by DSD. Compared with the response surfaces created by CCD, DSD could produce reliable response surfaces for its smaller number of experiments. We conclude that DSD is a powerful tool for implementing pharmaceutical studies including the QbD approach.

Design Space by Design of Experiments

“…Therefore, the type and level of numeric and categorical factors are suitable (i.e. the values within the design) [13,14,15]. In a quantitative study, the independent variables (factors) influence the response variable.…”

Definitive Screening Designs to Optimize Library-Free DIA-MS Identification and Quantification of Neuropeptides

“…This model enables detection of both independently impactful parameters, classified as main effects, and parameters that complement one another to achieve a particular response, classified as two-factor interactions, in addition to predicting optimal parameter values to be used, all from a single round of data acquisition. 10,29,30 DSDs are versatile and compatible with both continuous and categorical, or discrete, parameters. Following the split-plot design of specific combinations of parameter values for experimentation, DSDs prescribe a set of strategically varied parameter combinations to ensure enough statistical power to impute the optimal combinations while limiting the number of required experiments.…”

“…Additionally, neuropeptide identification is not well-suited for DDA’s top n selection criteria, as neuropeptides tend to coelute with several higher abundance competing matrix components. , To minimize sample requirements and maximize identifications, we applied a specific class of DoE, a definitive screening design (DSD), to decrease required experimental runs while maintaining a high level of statistical power to interpret the effects of each parameter. This model enables detection of both independently impactful parameters, classified as main effects, and parameters that complement one another to achieve a particular response, classified as two-factor interactions, in addition to predicting optimal parameter values to be used, all from a single round of data acquisition. ,, DSDs are versatile and compatible with both continuous and categorical, or discrete, parameters. Following the split-plot design of specific combinations of parameter values for experimentation, DSDs prescribe a set of strategically varied parameter combinations to ensure enough statistical power to impute the optimal combinations while limiting the number of required experiments. , …”

A Customized Bayesian Algorithm to Optimize Enzyme-Catalyzed Reactions

“…This task requires a detailed understanding of RSM, making it challenging to apply it correctly for scientists with limited knowledge of the tool. To overcome some of these challenges, definitive screening designs have been introduced as a method to model a surface response. , One of the reasons for the massive increase in the experimental cost is that the sampling points are selected nearly homogeneously from the entire parameter hyperspace. Indeed, the RSM is a single-iteration optimization process, whereby all experiments are performed simultaneously to determine the optimal parameters.…”