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Home > QbD Viewpoint > Posts > Transitioning to Quality-by-Design
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10/6/2008
Companies shifting to science-based manufacturing are taking their first steps by transitioning from quality-by-inspection to quality-by-design (QbD). From the FDA's viewpoint, the principles of QbD in pharmaceutical development are fairly straightforward. It requires establishing a clear linkage between the safety and efficacy of the drug product in the patient with its quality as defined by the attributes of the drug product and then linking it all the way back to the process for preparing the drug product. Specifically, QbD requires achievement of understanding at two levels as illustrated below:
- Clinical Understanding, which establishes a link between the attributes of the drug product and safety and efficacy in humans; and
- Process Understanding, which establishes a link between the attributes of the drug product and process parameters, process attributes and material attributes of the active pharmaceutical ingredient (API) and excipients that go into the drug product.
From a practical standpoint, process understanding, and the associated design space, entails:
- identifying and explaining all critical sources of variability;
- managing variability by the process via measurement and control of critical process variables; and
- reliably and accurately predicting and controlling product attributes within specifications (achieve quality).
Sounds simple enough, but arriving at process understanding and putting it to work remains a tall order. Towards that end, the FDA suggests that modeling and simulation must play an increasingly important role in science-based manufacturing-that is, from its current supportive role in empirical-based pharmaceutical development to a central role in QbD-based pharmaceutical development. For modeling and simulation to fulfill this promise will require development and deployment of three critical elements:
- A QbD Modeling and Simulation Infrastructure. An easy-to-use system for assembling, transforming, exploring, analyzing and reporting on QbD data.
- A QbD Work Process. Guidelines and work processes for deciding where and how modeling and simulation should be carried out in QbD. Such work processes typically involves risk assessment, experimental planning, prioritization, and data analysis and documentation.
- Organization and Culture. Establishment of integrated, multidisciplinary, multifunctional development teams trained in the use of QbD modeling and simulation for decision making.
Transitioning to QbD is reflective of a larger business trend aimed at fact-based decision making. In Competing On Analytics, Thomas Davenport of Babson College and Jeanne Harris of the Accenture Institute of High Performance Business outline a roadmap to company competitiveness by wielding analytics. Access to data is not enough. "Even if an organization has some quality data available, it must also have executives who are predisposed to fact-based decision-making." And, management support is critical. "A data-allergic management team that prides itself on making gut-based decisions is unlikely to be supporting. Any analytical initiatives in such an organization will be tactical and limited in impact." |
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