Accounting

Business

Response surface methodology

Regression analysis

Factorial experiment

Quality by Design

Mathematics

Design of experiments

Statistics

Plackett–Burman design

Epistemology

Engineering

Fractional factorial design

Philosophy

Box–Behnken design

Biochemical engineering

Downstream (manufacturing)

Variance (accounting)

Reliability engineering

Operations management

Quality (philosophy)

Main effect

Computer science

Critical quality attributes

Linear regression

Central composite design

Brazilian Journal of Pharmaceutical Sciences

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.

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