Often, the entire range of multi-variate interactions and associated risks are poorly understood. For example, the maker of
a monoclonal antibody found itself unable to reliably meet the market demand for the product, having lost 30% in yield over
time. Although they had recorded voluminous data about the manufacturing of the product, those records were gathering dust
in the company's files. Determined to establish a predictable manufacturing capability for the product and meet market demands
while ensuring sustainable compliance with good manufacturing practices (GMPs), the company was persuaded to bring in outside
experts to assess the problem. Their analysis, using fundamentals of QbD, unearthed variations in a specific raw material
and its relationship with complex process variables. The company was then able to establish and control operating ranges for
each of the critical variables that would accommodate such variations in raw material supplies. The company regained the lost
yield, which allowed it not only to meet existing market demand, but also to enter a new market.
It is the very complexity of developing and manufacturing biotechnology products that makes QbD's approach applicable to the
industry, where testing a product is far more complex than with small molecules, involves many raw materials, numerous upstream
and downstream processing steps, and numerous types of equipment and operating conditions. Because biological processes involve
high levels of variation and because many processes require viable expression systems, it is exponentially more difficult
to understand all of the possible permutations of variables and their impacts. Further, all of these variables and their complex
interactions must be controlled from site to site, country to country, and equipment to equipment, when multiple manufacturing
sites produce the product. QbD works particularly well in such complex contexts. The advanced statistical and analytical techniques
provide precisely the right tools to measure and understand processes, and result in highly robust and reliable systems design.
In addition to the promise of greater understanding of very complex processes, other trends and pressures are also likely
to help bring QbD to biotech companies sooner rather than later.
Regulatory backing: The FDA Office of Biotechnology Products in OPS/CDER clearly believes that QbD applies to biotechnology products and has
encouraged the adoption of this approach. In conversations with the FDA, it is also clear that they are working toward the
development of QbD standards that can be applied by the industry. The agency's Center for Biologics Evaluation and Research
also has been actively involved in the development of QbD and other quality initiatives and in exploring their applicability
to biotech. Meanwhile, the ICH Quality Roundtable agreed in September 2007 that the principles of ICH Q8, Q9, and Q10 apply
to both chemical and biotech drug substances and products. And as the industry continues to globalize, the need for international
harmonization embodied in these initiatives will only grow more urgent.
Economic pressure: QbD can help companies weather today's perfect storm of thin pipelines, skyrocketing costs, and downward pressure on prices.
Greater process understanding means more accurate and thorough validation, and more robust processes, lowering the cost of
quality. More robustness helps reduce manufacturing costs and produce the bottomline benefits of greater yield, increased
uptime, and reduced rework and rejected batches. By increasing the probability that a product will make it smoothly from development
all the way to commercialization, QbD can speed time to market, thus increasing the return on investment.
Therapeutic complexity: Genomics, proteomics, and other molecular tools have helped the scientific community rapidly advance its understanding of
the cellular changes that take place in cells that have transformed and are responsible for disease, whether cancer, neurologic,
or other disease states. Understanding these differences opens up new therapeutic targets for both small and large molecules.
The differences between diseased cells and normal cells may be small, so the specificity for a therapeutic product to act
on the diseased cells and not normal cells may require great precision in order to design a drug product that meets safety
and efficacy requirements. In this scenario, QbD provides the perfect approach to drug development. However, because QbD is
fundamentally better science, why not apply it across the board, to all product development?
The biotech industry, with some exceptions, has generally been slow to adopt QbD. There are various reasons for this. First,
because it is voluntary, there is no regulatory requirement for applying these tools and methodologies. Second, there was
little pressure or incentive to increase efficiencies in drug development and manufacturing until relatively recently. Further,
after years spent learning and developing steps to manufacture a product—from initial cell culture vial through scale-up of
the cell culture, downstream processing, formulation, filling, and packaging steps—it's hard to avoid the development of functional
silos that prevent the diffusion and integration of knowledge throughout the organization.