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...

Full description

Saved in:
Bibliographic Details
Published in:Brazilian Journal of Pharmaceutical Sciences 2018-01, Vol.54 (spe)
Main Authors: Fukuda, Isa Martins, Pinto, Camila Francini Fidelis, Moreira, Camila dos Santos, Saviano, Alessandro Morais, Lourenço, Felipe Rebello
Format: Article
Language:English
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
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
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