Design of Experiments (DoE) applied to Pharmaceutical and Analytical Quality by Design (QbD)

According to Quality by Design (QbD) concept, quality should be built into product/method during pharmaceutical/analytical development. Usually, there are many input factors that may affect quality of product and methods. Recently, Design of Experiments (DoE) have been widely used to understand the...

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Bibliographic Details
Published in:	Brazilian Journal of Pharmaceutical Sciences Vol. 54; no. spe
Main Authors:	Fukuda, Isa Martins, Pinto, Camila Francini Fidelis, Moreira, Camila dos Santos, Saviano, Alessandro Morais, Lourenço, Felipe Rebello
Format:	Journal Article
Language:	English
Published:	Sao Paulo Universidade de Sao Paulo Faculdade de Ciencias 01-01-2018 Universidade de São Paulo, Faculdade de Ciências Farmacêuticas Universidade de São Paulo
Subjects:	Accuracy Box-Behnken Designs Central Composite Designs (CCD) Design of experiments Design of Experiments (DoE) Factorial Designs Manufacturing Methods Optimization Pharmaceuticals PHARMACOLOGY & PHARMACY Process controls Product quality Quality by Design (QbD) Response Surface Methodology (RSM) Risk assessment Design of Experiments (DoE) Quality by Design (QbD) Factorial Designs Central Composite Designs (CCD) Response Surface Methodology (RSM) Box-Behnken Designs
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Summary:	According to Quality by Design (QbD) concept, quality should be built into product/method during pharmaceutical/analytical development. Usually, there are many input factors that may affect quality of product and methods. Recently, Design of Experiments (DoE) have been widely used to understand the effects of multidimensional and interactions of input factors on the output responses of pharmaceutical products and analytical methods. This paper provides theoretical and practical considerations for implementation of Design of Experiments (DoE) in pharmaceutical and/or analytical Quality by Design (QbD). This review illustrates the principles and applications of the most common screening designs, such as two-level full factorial, fractionate factorial, and Plackett-Burman designs; and optimization designs, such as three-level full factorial, central composite designs (CCD), and Box-Behnken designs. In addition, the main aspects related to multiple regression model adjustment were discussed, including the analysis of variance (ANOVA), regression significance, residuals analysis, determination coefficients (R2, R2-adj, and R2-pred), and lack-of-fit of regression model. Therefore, DoE was presented in detail since it is the main component of pharmaceutical and analytical QbD.
ISSN:	2175-9790 1984-8250 2175-9790
DOI:	10.1590/s2175-97902018000001006