Quality by Design (QbD) is a promising opportunity for biopharmaceutical companies to capitalize on the current regulatory
and industry focus on drug product quality and process control by leveraging science, compliance, and investment for the benefit
of patients and stockholders. Companies that recognize the potential benefits have made excellent progress with QbD and have
successfully filed applications with the US Food and Drug Administration, QbD's primary advocate. Others, who have used QbD
tools informally, are considering more structured cross-functional approaches for product development. Many others, however,
are standing on the sidelines waiting for what they see as the latest quality management flavor-of-the-month to run its course.
The challenge for QbD implementation is to convince these sideline skeptics. This article discusses reasons for the skepticism
and provides suggestions to managers for how to implement QbD in their companies.
WHAT DOES QbD MEAN?
Depending on whom you talk to, you will get different descriptions of what QbD is. Some see it as a product approval quid pro quo to raise the regulatory compliance bar or the latest flavor-of-the-month that adds bureaucracy, cost, and time to product
development. All QbD really means, however, is that products, and the processes that make them, are designed in advance to
meet their product quality specifications and process control requirements.
The truth is that QbD is based on solid science, valid statistical tools, and sound management techniques that have proven
their industrial value for many decades. These include product profiling, experimental design, process mapping, risk assessment,
and automated process controls. The goal of QbD is to apply these tools in a structured manner.
These techniques are suspected of taking too much time and costing too much compared with the traditional trial-and-error
methods that have been prevalent in development. But a better approach is to structure an experimental design that optimizes
the available development time and project funds while maximizing the product and process data delivered. If this sounds too
good to be true, consider that design of experiments (DOE), the heart of QbD, offers returns that are four to eight times
greater than the cost of running the experiments in a fraction of the time that it would take to run one-factor-at-a-time
experiments. (See box on DOE). Furthermore, these approaches are the only way to unlock interactions of multiple factors,
something trial-and-error and one-factor-at-a-time experiments cannot. When the QbD approach is used, products are purer and
more potent and processes are more robust and have higher productivity. Also, technology transfer risks are reduced because
fewer start-up runs and less start-up time are needed, which can lower start-up costs and speed the time-to-market.
The univariate trial-and-error approach to product development is traditional and can be safe in the short term. With the
time pressure to be first to the market, it is an easy way out. Although this methodology may produce safe and effective products,
there is considerable room for improvement and risk reduction. Product quality, process productivity, and profitability can
be improved with further in-process refinement and reduction of product variability.
If the traditional approach is ok, why should we be concerned with cost and productivity? Companies, governments, and health
maintenance organizations (HMOs) face conflicting budget priorities, expanding demand, and higher costs per person for healthcare.
Product cost and the cost of quality and compliance are under increasing pressure to be justified or reduced. In short, improved
quality, higher productivity, lower cost, and improved compliance are the roadmap for the future. These goals can be achieved
with QbD application. Neway has efficiently made the business case for QbD.1
CONVERTING THE SKEPTICS
Most people are willing to change; they just don't like being changed. The desire for change needs to be internal. Culture
is a big factor in the adoption of new ideas and approaches. Some companies are open to change and others are resistant. Management
must lead the way by setting a good example. Although management support is critical, it can only go so far. At some point,
scientists and engineers must understand and accept the reasons for and benefits of changing to a QbD approach. One way to
do this is to identify and discuss why they are skeptical and less than willing to change.
Activities like QbD and process analytical technology (PAT) have been standard practice at chemical companies since the mid
1950s. These companies embraced these approaches because it saved them large amounts of money and time. But chemical companies,
of course, are not regulated the way the pharmaceutical industry is. Our regulations create a different environment, and we
must adapt to that difference. Validation presents the most obvious constraint.