The Application of Quality by Design to Analytical Methods - Pharmaceutical Technology

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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).



Pharmaceutical Technology



Table I: FMEA for a PAT NIR method used for drying monitoring.
Risk assessment tools such as a priority matrix and failure mode effect analysis (FMEA) (16–18) can be used to identify where variability in a factor or failure of part of the system might represent a risk to the method's ability to deliver the design intent. Before any experimentation is performed, the method must be improved where possible by using the output from the FMEA. As many high-risk factors as possible are controlled or fixed to eliminate the potential sources of variability. Table I shows an example of part of an FMEA for a PAT near infrared (NIR) method used for drying monitoring. An additional column entitled "Action" is not shown in Table I. This column is used to lower or mitigate the risk identified for the failure modes that have been identified.

Click here to download a template on which to conduct a failure mode effect analysis (FMEA).

A tool known as CNX can help classify all the factors in the fishbone diagram. The team decides which factors should be controlled (C), which are potential noise factors (N), and which should be experimented on (X) to determine acceptable ranges. The factors have been categorized using CNX in Figure 2.

Robustness studies are performed for the highest risk parameters for which acceptable ranges will need to be identified (X-type factors). Design of experiments (DOE) is used to assess the multidimensional combination and interaction effects of these factors. For the highest risk noise (N-type factors), a ruggedness study is performed using a measurement systems analysis (MSA) design. This study aims to challenge the method, giving as much opportunity as possible for any problems to surface.


Figure 3: Flow chart overview of a risk assessment process.
Once the method has been evaluated by DOE and MSA and improved as necessary, the FMEA can be used once again to assess the risk attached to operating the method and establish the proven acceptable ranges for the method factors. The output of the statistical studies is documented as the analytical method design space. Figure 3 provides an overview of the whole risk-assessment process.

Analytical method control strategy (control verification). Using the appropriate risk-assessment tools, the critical factors and their acceptable ranges (from the risk assessment or experimental work) are explicitly defined in the method. Throughout a method's life cycle, which often stretches over many years, it is inevitable that there will be changes in the method environment that can affect its operation. Changes and improvements to a method should be made with reference to the method knowledge repository, which contains all the information from design space definition activities. This repository should contain the risk-assessment information and results from method ruggedness and robustness studies.

Any proposed changes that take the method outside its proven design space are risk assessed. For high-risk changes, an evaluation or equivalence exercise should be performed to ensure method performance criteria are still met. Method-specific lesssons learned are used to update the method knowledge repository. Technique lesssons learned are used to enhance the risk-assessment process for future methods.

Opportunities of and barriers against a QbD approach to analytical methods

There are several opportunities of this QbD approach to analytical methods, including:

  • Methods will be more robust and rugged, resulting in fewer resources spent investigating out-of-specification results and greater confidence in analysis testing cycle times
  • Resources currently invested in performing traditional technology transfer and method validation activities will be redirected to ensuring methods are truly robust and rugged
  • The introduction of new analytical methods—from research and development to quality control laboratories—using a QbD approach will lead to a higher transfer success rate than with traditional technology-transfer approaches
  • A specified process will help the systematic and successful implementation of the QbD methodology and fosters a team approach
  • A true continuous learning process is established through the use of a central corporate knowledge repository that can be applied across all methods
  • By registering only a commitment to ensure method changes meet the registered method performance criteria, flexibility to continuously improve methods can be achieved.


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