The Role of Analytical Science in Implementing Quality by Design - Pharmaceutical Technology

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The Role of Analytical Science in Implementing Quality by Design
The authors present topics discussed and conclusions that resulted from the PDA QbD workshop.


Pharmaceutical Technology
Volume 37, Issue 3, pp. 74-77, 80

The recognition that the current pharmaceutical industry's manufacturing performance was not as state of the art as other industries has been a key driver behind the increasing adoption of quality-by-design (QbD) concepts. The Gold Sheet from January 2009 compares the operations performance of the pharmaceutical industry with other industries such as automotive, aerospace, computer, and consumer packaged goods and across a long list of measures, the pharmaceutical industry compares poorly.

Industry efforts to address these performance issues have focused on Six Sigma/Operational Excellence concepts and, since the issue of new International Conference on Harmonization (ICH) guidelines (i.e., ICH Q8, 9, 10, and 11), on the concept of quality by design. These initiatives have significant implications for pharmaceutical analysts. The desire to improve first-pass yield with zero defects has seen laboratories come under great scrutiny to ensure that all the methods they run work right first time, every time. To reduce inventories and lead times to those typically achieved in other industries, manufacturing planners must know with absolute confidence how long manufacturing and testing will take; rerunning a problem method is no longer an acceptable option. Zero-defect goals have also seen a focus on understanding and improving process capability (i.e., reducing the overall process variation relative to specification), and therefore, there is a much greater focus on truly understanding the variation contribution of the analytical method to the overall process variation.

QbD concepts described in ICH Q8, 9, 10, and 11 provide approaches that can help achieve the desired improvement in process performance (1-4). ICH Q8(R2) and 11 recognize the importance of effective process development in understanding the relationship between process variables and process performance. ICH Q9 describes how risk-based approaches can be used to determine which variables are crucial to control. ICH Q10 highlights the importance of effective processes for maintaining the control strategy through the lifecycle of the product.

A major focus of discussion and implementation of QbD has been on the concepts included in the ICH Q8 definition (i.e., product and process understanding and process control). Understanding and control, however, cannot be achieved without a solid foundation of measurement or analytical science. During process development, analytical data are needed to provide the basis for process understanding. Control strategies based on this understanding are increasingly being developed based on recognition of the importance of identifying and controlling variation upstream rather than testing the end product. This control of variation has seen increasing use of process analytical testing (PAT) in preference to or in addition to end product testing, and with it, new technologies to be mastered by analytical scientists. QbD has also seen a focus on understanding and controlling attributes of excipients used in the manufacturing process, with increasing use of physical measurement technologies.

In addition to affecting what analytical scientists measure, the concepts of QbD, can also be used to enhance the robustness of the analytical methods themselves (5). One implication of such an approach is that it highlights the potential to re-examine the way that methods are developed and validated, in the same way that application of QbD to manufacturing processes has driven a revision in thinking of how process development and validation are performed (6, 7).

Taking advantage of the potential improvement opportunities through adoption of QbD concepts, however, does have some challenges. The ability to introduce improvements to analytical methods and to adopt new analytical technologies can be constrained, both through the need for industry to acquire new skill sets and through the challenges associated with making postapproval regulatory changes. These challenges are multiplied many times as a consequence of the global nature of the pharmaceutical business, with many stakeholders (e.g., regulators, pharmacopeias, consensus standards bodies) making harmonization of approaches particularly challenging.


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