QUALITY BY DESIGN - Risk Management Applications in Pharmaceutical and Biopharmaceutical Manufacturing - page 95

brings to light new efficiencies that can be applied in the development of other
products. Alternatively, it may be that new technologies, developed at research
and development (R&D), can later be applied in manufacturing.
The important part is to understand that learning is a continuum and that such
learning should be applied across the whole product life cycle. It also highlights
the need for R&D and manufacturing to work together.
5.4 EXAMPLES OF QBD APPROACHES
At the time of writing, some examples of QbD approaches for small molecule
and large molecule products are available, as follows:
• the EFPIA Mock P2 paper issued 2006 [6];
• the ACE tablet study, issued 2009 [8];
• Mock QOS P2: “Sakura” tablet issued 2008 [7];
• A-Mab biotechnology study issued 2009 [9].
Reference should also be made to International Society of Pharmaceutical
Engineers' (ISPE's) Product Quality Lifecycle Implementation (PQLI ® ) [10]
published documents, particularly their Part 1-Concepts and Principles and
Part 2-Illustrative Example, and Parenteral Development Associations (PDAs)
Paradigm Change in Manufacturing Operations (PCMO* SM ) [11] initiative, as
well as QbD training offered by other reputable organizations. The FDA has
also issued case studies for immediate release and modified release tablets.
5.5 CONCLUSION
The implications of ICH developing Q8R2, Q9, Q10 and Q11 guidances have
yet to be fully felt. It is important to understand there is no single way of
implementing QbD in practice.
At the time of writing, the major pharmaceutical companies generally are
starting to adopt these QbD principles for developing new products and smaller
companies and generic and consumer health companies are also taking an
interest, but overall adoption has been relatively slow. Some of this may be
because of concerns that additional time is needed for this new approach and
hence product launch may potentially be delayed or concerns about complexity
and the need for learning new techniques in DOE or multivariate statistical
methods or maybe how quickly the regulators themselves will become trained in
these new techniques. Regardless of this, QbD does require the use of some new
skills and approaches and it will be necessary for both industry and regulators
to become familiar with applying these.
However, despite the potential concerns, it is important to understand QbD is
about enabling improved product and process understanding and that its approach
will certainly lead to greater clarity about what is needed for achieving product