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.
Table I: FMEA for a PAT NIR method used for drying monitoring.
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.
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
Figure 3: Flow chart overview of a 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.