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

TLDR

Quality by Design builds quality into pharmaceutical and analytical development, and Design of Experiments is widely used to understand how multiple input factors and their interactions affect product and method quality, making DoE a core component of QbD. This paper provides theoretical and practical considerations for implementing Design of Experiments in pharmaceutical and/or analytical Quality by Design. The review explains common screening designs (two‑level full factorial, fractional factorial, Plackett‑Burman) and optimization designs (three‑level full factorial, central composite, Box‑Behnken), and discusses multiple regression model adjustment including ANOVA, regression significance, residuals, R² metrics, and lack‑of‑fit.

Abstract

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.