Quality by Design (QbD) is a systemic approach to develop a pharmaceutical product with ensure that the intended performance of a final drug product is as expected. Where the quality of a product ensure by design not only the final product testing.
In QbD, product and process understanding is the key enabler of assuring quality in the final product. Among various mathematical modeling approaches, Design of Experiments (DoE) is extensively used for the implementation of QbD in both research and industrial settings to avoid trial and error method was used in the past that led to several problems like non-reproducible, high-cost, and time consuming approach.The basic objectives of DoE are screening, optimization, and robustness. It involves the execution of experimental design on the basis of suitable variables along with statistical evaluation of obtained responses and exploration of the design space using mathematical or graphical approach.When considering interactions of multiple variables that cannot be modelled simply, perform empirical tests (e.g., Design of Experiments - DOE) to map out appropriate operating ranges. It is also useful in understanding how typical fluctuations around mean input values (e.g., starting materials) can influence the final product.In DoE approach, the controlled input factors are systematically varied to determine their effects on the output responses, which allows the determination of the most important input factors, the identification of input factors setting leading to optimized output responses, and the elucidation of interactions between input factors.To perform this job Minitab application will be a better option. Which is a software based analytical tool used in pharmaceutical industry to analyze, improve, and validate pharmaceutical processes.Selections of best experimental design should consider several aspects, such as defined objectives, number of input factors and interactions to be studied, and statistical validity and effectiveness of each design. In order to provide a better understand of DoE application, experimental designs may be divided into two types: a) screening designs and b) optimization designs.One of the most important limitations of screening designs rely on the fact that they only allow modeling 1st order (linear) response surface, because they have only two level for each input factor. Optimization designs uses 3 to 5 levels of each input factors, which allow modeling 2nd order (quadratic) response surface. Three-level full factorial design are often used only when two or three input factors need to be study, because an increased number of experiments is required.