“Quality cannot be tested into products”
The whole ethos of Quality by Design or QbD is to steer product development away from this well known FDA quote. QbD therefore is an approach thats objective is to ensure the quality of medicines through the informed and well thought through design and development of the manufacturing process. This is achieved through the application of scientific & statistical analysis, quality systems and risk-management approaches during that process design, its development ultimately through to the manufacturing of medicine. A key aim of QbD is to make sure that all sources of variability affecting a process are identified, understood and controlled through the appropriate of process controls. This results in the process producing the medicinal product being under control and being able to consistently meet its predefined characteristics or Critical Quality Attributes (CQAs), in essence, ‘getting it right from the get go’.
QbD focuses on the use of multivariate analysis (MVA) such as Design of Experiments (DoE) in combination with the analytical methods to enhance the identification and understanding of both the critical attributes of materials and critical parameters of the manufacturing process. This understanding of the product and the process builds quality into production and allows continuous process improvement that ensures that the product is consistently safe and efficacious as well as commercially efficient to produce through optimal yields and successful production of lots.
The concepts behind quality by design are captured in guidelines that include Q8, Q9, Q10 and Q11 from the International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH). Regulatory agencies embrace the use of QbD approaches by organisations in their applications for marketing authorisation, variations to existing marketing authorisations and scientific advice.
GRA has deep experience of applying QbD approaches in the development of biological products, both applying these approaches to both novel and existing processes. In addition, GRA also performs training on the application of QbD. GRA works closely with statistical associates in the application and statistical training of QbD application to products.
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