The Application of Quality by Design to Analytical Methods

To monitor and control processes or products, analytical methodology must be fit for purpose. An approach to apply quality by design principles to the design and evaluation of analytical methods has therefore been developed to meet these needs.

This article features a downloadable template on which to conduct a failure mode effect analysis (FMEA).
Oct 02, 2007
Volume 31, Issue 10

During the past few years, regulatory agencies have placed an increased emphasis on pharmaceutical process understanding. The US Food and Drug Administration's Pharmaceutical CGMPs for the 21st Century—A Risk Based Approach describes how combining a focus on process understanding with a structured risk-assessment process can help develop control strategies that enhance process robustness (1). Similar concepts are also discussed in ICH Q8 Pharmaceutical Development and International Conference on Harmonisation (ICH) Q9 Quality Risk Management (2, 3). Collectively, these principles are described as "quality by design" (QbD). As defined by Janet Woodcock in 2004, "QbD means that product and process performance characteristics are scientifically designed to meet specific objectives, not merely empirically derived from performance of test batches." This article describes how the principles of QbD being developed for manufacturing processes can equally well be applied to ensure that analytical tests methods and process analytical technology (PAT) are robust, as defined by ICH Q2(R1), and rugged, as defined by the United States Pharmacopeia (USP) (4, 5).

Analytical method robustness testing typically involves evaluating the influence of small changes in the operating conditions. Ruggedness testing identifies the degree of reproducibility of test results obtained by the analysis of the same sample under various normal test conditions such as different laboratories, analysts, and instruments. The term robustness has been used differently when describing chemical processes and includes factors deemed as robustness parameters and ruggedness factors in the analytical world. Processes, for example, have been defined as robust if they have the ability to tolerate the expected variability of raw materials, operating conditions, process equipment, environmental conditions, and human factors (6).

The examples in this article are based on chromatographic and PAT methods to support new-product development. Nonetheless, the concepts can be applied to any method type and can be used to improve the reliability of established methods. As the principles described in this article bring fundamental method understanding, the adoption of this approach will reduce manufacturing resources involved with investigating out-of-specification results as well as increase the confidence in analysis testing cycle times. They also lay the foundations for less regulatory oversight of method changes, which should facilitate innovation and continuous improvement.

Processes must meet current good manufacturing practices to ensure drug products meet safety and efficacy requirements (7). Traditionally, this requirement has been met by performing process validation studies on three batches. It has been recognized that this approach is unlikely to fully represent routine manufacture and unlikely to cover all potential sources of variability (e.g., raw materials, operators, shifts, reactor vessels). Moheb Nasr, director of the Office of New Drug Quality Assessment at FDA, has identified this issue as a challenge to the regulatory process and has stated that there is currently a "focus on process validation and not process understanding" (8).

In a similar way, the traditional approach to analytical method validation described in ICH Q2(R1) and analytical method transfer represents a one-off evaluation of the method and does not provide a high level of assurance of method reliability. The limited understanding of a method obtained through these traditional approaches has often led to it passing the technology transfer exercise (by chance) from development into use in commercial manufacturing facilities. In reality a significant method variable had not been fully explored, thereby leading to method failure some months post-transfer. Even when transfers have failed (the more desirable scenario if the method is poor, enabling issues to be identified), significant resources are often required to attempt to remedy the causes of the transfer failure, usually at a time when there is considerable pressure to support the introduction and launch of a new product. Both of these scenarios are undesirable and would be recognized in many pharmaceutical analysis laboratories as significant issues.

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