Quality by Design: The Case for Change - Executive management leadership is essential in the effective implementation of QbD - BioPharm International


Quality by Design: The Case for Change
Executive management leadership is essential in the effective implementation of QbD

BioPharm International
Volume 24, Issue 3, pp. 20-24


As FDA continues to phase out three-batch validation in favor of statistical evidence of process understanding, organizations that can capitalize on this change will have a significant competitive advantage. Yet, in the five years since ICH outlined the concept of design space in its Q8 guideline, pharmaceutical and biotech companies—despite depending on innovation for their livelihood—have been slow to adopt QbD. The reasons for resistance are familiar: fear of change, worry about its cost, and perhaps most importantly, lack of full understanding of the competitive business benefits of QbD on the part of executive decision-makers. As the 21st century continues to shape up as a tough time for the pharmaceutical industry—with major patent expirations, thin pipelines, soaring manufacturing costs, and downward pressure on prices—this resistance looks less and less tenable. While QbD may not be the solution to all of the industry's ills, the improved process understanding and more robust processes it promises can produce significant business and competitive benefits that more than justify its cost.

First, however, those who best understand the full potential of QbD because they best understand the science and technical implications must be able to translate that knowledge into the language of financial, operational, marketing, and strategic advantages that is the lingua franca of CFOs and other top leaders. Those with expertise in R&D, process design, and regulatory and compliance issues and who are fluent in CpK and design space must become equally at home with the business case for QbD. That general business case involves development of a strong cost-benefit analysis, as follows:

  • Organizational readiness to capitalize the emerging compliance paradigm
  • Clearly defining how QbD reduces risk and cost for your specific enterprise, (i.e., product pipeline versus the organization's historical cost and speed profile).

Beyond the general business case lie the challenges of engaging the larger organization in undertaking and developing the specific business case for the company's particular situation and embarking on a QbD initiative. Those challenges include the following:
  • Enrolling management in a more rigorous assessment of costs and benefits
  • Developing a phased, integrated path to organizational competence
  • Recognizing and achieving new paradigms in cross-functional coordination.

By making the general business case for QbD and mobilizing executive management to undertake its careful consideration, you can not only help show the way to untapped business value but also greatly increase the scientific rigor and competence of the organization's technical functions.


QbD is not simply about avoiding the cost of lost active pharmaceutical ingredient (API). Millions of dollars of costs that could be avoided in a QbD environment have occurred in the following areas:

  • Manufacturing downtime
  • Lost sales revenue due to backorder
  • Wasted marketing spending as a launch stalls in backorder
  • Rejected product and disposal costs
  • Deviation investigation and report generation
  • Repeated quality control testing and method revalidation
  • Engineering costs in calibration and product recovery attempts
  • Incremental process characterization
  • Supply chain disruptions and expediting
  • Manufacturing overtime
  • Increased regulatory scrutiny and inspection
  • Lost market share due to erosion of patient confidence in quality and efficacy
  • Managerial distraction from the core business.

All of these costs degrade profit, and companies go to considerable lengths to avoid them. But as anyone who has been involved with pharmaceutical or biotech development and manufacturing knows, the risk and cost profile of a process may be the result of a complex interaction of factors, including raw-material characteristics, process parameters, and environmental factors.

Traditionally, a process has been a kind of black box. The process at the time of validation is not well understood, but as long as the specs are rigidly locked down and maintained for each variable, it is hoped that the box will continue to churn out acceptable product. But variation is inevitable in processes, and raw materials not only almost always vary from batch to batch but also those variations interact in complex ways with the other variable controllable aspects of the manufacturing process. When deviations occur post-commercialization, the search for causes can be time-consuming and costly. There is no guarantee that remedies will be sustainable, and the barriers to regulatory approval of change may be considerable.

By contrast, QbD is a proven, systematic way to achieve maximum process understanding at minimum expense. Employing sophisticated statistical techniques and design of experiments (DoE) to develop a robust and reliable system, QbD may be applied to any manufacturing or development activity, including formulation and process development. By fully understanding the complex interactions of multiple variables, the "design space" can be mapped (i.e., the possible combinations of the critical process parameters that would keep the resulting product within specifications). The process can then be more easily characterized, validated, and controlled, resulting in products within a defined target product profile.

Given these stark differences between the traditional approach to quality and the QbD approach, decision-makers should ask themselves these questions: which approach has a lower risk and cost profile? Is it more cost-effective to understand the contents of the black box before going into production or to proceed by trial and error, investigation, regulatory scrutiny, and corrective action while trying to meet customer demand? Is an understanding of the relationship between factors and having the flexibility to leverage that knowledge to reduce product variability preferable to being locked into the best guess used to produce the first three batches?

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