The importance of Quality by Design (QbD) is being realized gradually, as it is gaining popularity among the generic companies. However, the major hurdle faced by these industries is the lack of common guidelines or format for performing a risk-based assessment of the manufacturing process. This article tries to highlight a possible sequential pathway for performing QbD with the help of a case study. The main focus of this article is on the usage of failure mode and effect analysis (FMEA) as a tool for risk assessment, which helps in the identification of critical process parameters (CPPs) and critical material attributes (CMAs) and later on becomes the unbiased input for the design of experiments (DoE). In this case study, the DoE was helpful in establishing a risk-based relationship between critical quality attributes (CQAs) and CMAs/CPPs. Finally, a control strategy was established for all of the CPPs and CMAs, which in turn gave rise to a robust process during commercialization. It is noteworthy that FMEA was used twice during the QbD: initially to identify the CPPs and CMAs and subsequently after DoE completion to ascertain whether the risk due to CPPs and CMAs had decreased.

Quality by Design (QbD) is of paramount importance not only for patient safety but also for the timely and uninterrupted supply of products at affordable prices into the market. Both of these objectives can be achieved only through a robust process, and one of the major obstacles for developing a robust process is the quality of input materials and reagents used for the manufacture of active pharmaceutical ingredients (APIs). This article demonstrates the use of QbD methodology to optimize the quality of input materials and make the process more consistent, thereby reducing the variation in the quality of API produced. This article highlights the use of failure mode and effect analysis (FMEA) for the unbiased identification of critical process parameters and critical material attributes associated with the manufacturing of key starting materials, which are later used as input for the design of experiments (DoE) study that is used for the optimization.