Current approaches to analytical technology transfer—in which, for example, three batches have been analyzed each in duplicate
at the transferring and receiving sites—represent a one-off exercise aimed at proving that a receiving laboratory can operate
the analytical method (9–12). These approaches really only confirm that the analyst, equipment, and other components involved
in the exercise can operate the method at the time of the transfer exercise. The desired state is proving that the method
will be reliable (robust and rugged) throughout the life cycle of its use. Instead of the generic technology-transfer exercise,
it is more appropriate that a risk assessment be performed to identify potential variables and that this should dictate which
robustness and ruggedness experiments are performed. The latter can be tested as part of a measurement systems analysis study
in which the most likely sources of variability are identified and studied. All the knowledge (not just the technology) should
then be transferred; any future changes to the method or the environment in which it is operated should be risk assessed;
and, if appropriate, equivalency should be demonstrated as part of an overriding change-control procedure.
The current reality is that the pharmaceutical industry has 6-sigma products on the market but only 2.5 sigma processes (13).
The capability of many analytical methods and controls used to monitor these processes is nearly as poor. Nevalainen et al.,
for example, have estimated analytical performance at the 3.85-sigma level (14). To move pharmaceutical processes and analytical
methodology toward much improved sigma capability, an objective, cross-functional, data-driven assessment of all the steps
within a process or method must be performed.
A framework for applying QbD to analytical methods
Figure 1 illustrates the high-level components of how QbD can be applied equally to processes and analytical methods. It
displays how QbD for analytical methods is driven by the overall process-control strategy.
Figure 1: The high-level components of how quality by design can be applied to processes and analytical methods.
Method performance requirements (design intent).
Fundamental to any method development is being clear about the design intent of the method (i.e., the criteria that must
be met). Method-performance criteria and method-operational intent are two important aspects of this design intent.
These criteria are driven by an understanding of the process monitoring and control requirements; that is, the process critical
quality attributes (CQAs) and specification limits. CQAs are identified, through a thorough understanding of those characteristics
of a drug substance or a drug product that may need to be controlled to ensure the safety or efficacy of a product. For methods
measuring these CQAs, criteria, such as the following, would need to be met:
- Precision—the need for method variability to be a small proportion of the specification
- Selectivity—being clear on which impurities actually need to be monitored at each step in a process and ensuring adequate
discrimination between them
- Sensitivity—ensuring the method is sufficiently sensitive relative to the specification limit.
PAT methods often meet the criteria above in a different way from traditional end-point testing methods such as high-performance
liquid chromatography (HPLC). Selectivity, for example, may be achieved through a multivariate model as opposed to resolution
between adjacent peaks in an HPLC method. For these methods, precision could be demonstrated by checking the prediction validity
of the model.
Method operational intent.
These criteria address the aspects of the method that are required to facilitate ease of use in routine operation (e.g.,
analysis time, acceptable solvents , available equipment). Opportunities for the implementation of improved or new technology
also may be identified. These criteria can be generated by performing an analysis of the voice of the customer (VoC) (i.e.,
the aspects of a method that are considered important for the quality control laboratories within manufacturing where the
commercial methods will be operated).