How to Make the Business Case for Quality by Design

No time for QbD? How to convince management to make it a priority.
Dec 01, 2008

Even though the US Food and Drug Administration (FDA) and biopharmaceutical industry leaders have touted the merits and business benefits of Quality by Design (QbD) for quite a while, adoption in the industry has been slow. This article examines barriers in the industry and how to overcome them. Only when this happens can QbD reach the manufacturing floor in a way that benefits the public and the long-term viability of biopharmaceutical companies. This article also shares insight into making the business case for QbD based on the typical criteria that decision makers use to evaluate new initiatives and related technology.

Quality by Design (QbD) has become a buzzword in the biopharmaceutical industry, but the concept is not new. For decades, quality pundits have presented QbD as a way to improve manufacturing process outcomes. The biopharmaceutical industry, however, has not been quick to adopt the concept. This is because QbD often falls low on the long list of immediate priorities to be tackled. Making a successful business case for QbD can be done by making the business benefits clear, and planning the initiative in a way that takes near- and long-term returns into account.


In his 1992 book, Juran on QbD, The New Steps for Planning Quality into Goods and Services, manufacturing quality expert Joseph M. Juran explored the reasons a book on quality planning was needed at that time, noting "the gathering awareness by companies that they have been enduring excessive costs due to chronic quality-related wastes." Much of this waste, he adds, consists of "redoing work already 'done'."1

Applying this idea to biopharmaceutical manufacturing today, we can certainly relate to the concept that such waste costs money, maintains needless risks of consumer harm in the system, and hurts companies' and the nation's ability to compete. By failing to find and correct the root cause of problems early in the design phase of the process and product development cycle, companies risk quality and yield problems in their processes.

Other industries have been quicker to adopt QbD because they felt the financial crunch of competition sooner than pharmaceutical manufacturers who were able to leverage blockbuster drugs and patents into large profits in past decades. Today, as many manufacturers struggle with shrinking new drug pipelines and competition from generics caused by expiring patents, QbD should be viewed as an opportunity that brings with it business benefits for the entire organization.


Previous experience is valuable to the accumulation of institutionalized knowledge. When designing processes that can cope with variability, we need to look at historical production data to learn from mistakes and successes. The FDA's Janet Woodcock has frequently stated that QbD is derived from a combination of prior knowledge, experimental assessment, and a cause-and-effect model that links critical process parameters and critical quality attributes.

Achieving the goal of manufacturing process excellence through QbD requires us to begin the work in process development. It's natural to think of the flow of data and information in the forward direction from process development into manufacturing, but data and information also needs to flow backward from manufacturing into process development.

By using data and information from current commercial processes to assist with future process development for new products, we leverage investments that we have already made and experience we have already gained. To do this, on-demand data access and investigational analysis are required for successful collaboration between the process development and manufacturing teams. This allows us to design and build additional risk reduction and ruggedness into the next process so that it can better handle variability.

The FDA's Process Analytical Technology (PAT) guideline shows the value of continuous learning that comes from analyzing process data when coupled with systems that support the acquisition of knowledge from those data, saying:

"Continuous learning through data collection and analysis over the lifecycle of a product is important. These data can contribute to justifying proposals for post-approval changes. Approaches and information technology systems that support knowledge acquisition from such databases are valuable for the manufacturers and can also facilitate scientific communication with the Agency." 2