Quality by Design (QbD) vs Design of Experiments (DoE) (concepts comparison)
Definition:
- Quality by design (QbD): QbD is a systematic approach that focuses on building quality into the product or process from the beginning. It involves identifying critical quality attributes (CQAs), determining critical process parameters (CPPs), and establishing a design space within which the process can operate to consistently meet the desired quality requirements.
- Design of experiments (DoE): DoE is a statistical methodology used to systematically plan, conduct, analyze, and interpret experiments. It aims to identify significant factors affecting a process or product and optimize them by determining their optimal levels.
Focus:
- QbD: QbD primarily focuses on understanding the relationship between critical process parameters (CPPs) and critical quality attributes (CQAs). It aims to establish a robust manufacturing process that consistently produces products meeting predefined quality criteria.
- DoE: DoE focuses on identifying significant factors affecting a process or product and optimizing them to achieve desired outcomes. It helps in understanding how different variables interact with each other and how they influence the response variable.
Application:
- QbD: QbD is widely used in pharmaceutical development, manufacturing, and analytical testing to ensure consistent product quality throughout its lifecycle. It helps in designing robust analytical methods, selecting appropriate equipment, establishing control strategies, and managing risks.
- DoE: DoE is commonly applied in research and development stages of pharmaceutical development for formulation optimization, process optimization, method development, stability studies, etc. It helps in reducing experimental time and resources while maximizing information gained from experiments.
Approach:
- QbD: QbD follows a holistic approach that considers all aspects of product development including formulation design, process development, analytical methods, and control strategies. It involves the use of tools like risk assessment, design of experiments, multivariate analysis, and process analytical technology (PAT).
- DoE: DoE follows a more focused approach that aims to identify and optimize specific factors affecting a process or product. It involves the systematic planning of experiments using statistical principles such as factorial designs, response surface methodology (RSM), or Taguchi methods.
Read also:
Resource Person: Atefe Nasrollahi