Appendix 1
Moving from the Analytical Target Profile (ATP) to robust and rugged analytical measurements.
An example of an approach to method development and evaluation that aligns with the concepts proposed in Figure 1 of this
paper is described below.
Method design:
Once the method requirements are thoroughly understood and the ATP is defined, an appropriate analytical method can be designed
to achieve the desired measurement of the material, process, or product attribute. Information to be considered during the
analytical-method design phase might include: prior experience with similar measurements or matrices, regional or geographic
limitations and/or availability of reagents, supplies, or specific technologies in laboratories that will be conducting the
testing and cycle-time requirements to support process operations.
Having identified a suitable analytical method, the factors that can influence the performance of an analytical method can
be mapped against the unit operations within the method. The method design phase then continues by systematically evaluating
the impact of each factor. This assessment can be performed either through the utilization of appropriate risk assessment
tools or based on experience (prior knowledge) or a combination of both.
Method evaluation:
The next step is the method evaluation phase where the factors that have been identified as having a potential impact on method
performance are evaluated (e.g., through structured multivariate design-of-experiment investigations or appropriate screening
experiments). A preliminary assessment of the potential impact of the factors may also be explored using a robustness screening
design (e.g., a Plackett-Burman fractional factorial experiment).
Figure 2: Diagram proposing how analytical method factors might be evaluated and their relationship to robustness and ruggedness
testing. Adapted from Borman et al (1).
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Method control strategy:
The outcome of the method evaluation phase leads to the final definition of the operating parameters and parameter ranges
required to ensure performance of the analytical method. These set points and operating ranges are specified and a change-control
process is implemented to ensure control of the established method parameters. A diagram proposing how analytical method factors
might be evaluated and their relationship to ruggedness and robustness testing is shown in Figure 2.
Continuous improvement/life-cycle knowledge management:
To get the most out of a QbD method-development process, it is important to consider how the information generated (e.g.,
factors considered, risk-assessment tools used to select variables for experimental study, and outcomes from each study) will
be captured in an appropriate knowledge repository. Once this repository is established, the impact of any proposed future
changes to the analytical method may be assessed. In this assessment, the prior knowledge gained during development is evaluated
relative to the proposed changes to the method, forming a basis for a follow-on risk assessment to determine whether the changes
are justified and within the control strategy established for the product. The critical assessment is whether or not the proposed
change still meets the requirements established in the ATP. Additionally, as appropriate, method equivalency experiments may
be included to further justify the proposed changes.
The combination of a structured, rigorous scientific and statistically sound approach to method development and evaluation,
combined with a robust quality management system based on ICH Q10 Pharmaceutical Quality System, ensures that changes to methods
are appropriately evaluated before implementation and can provide the basis for enhanced flexibility to introduce change without
excessive regulatory oversight.
Appendix II: Glossary of terms and definitions
Analytical Target Profile (ATP):
The combination of all method performance criteria that direct the method development process. An ATP would be developed for
each of the attributes defined in the control strategy and defines what the method has to measure (i.e., acceptance criteria)
and to what level the measurement is required (i.e. performance level characteristics: e.g. precision, accuracy, working range,
sensitivity and the associated performance criterion). For example, the ATP for a specific method might be that it is specific
for impurity X, can quantify X at levels of 0.05% or above with a precision of 1.0% relative standard deviation (RSD) or better,
and an accuracy of not more than 2.0 % bias.
Method control strategy:
The controls on analytical factors and parameters, and monitors (e.g., system suitability checks) that ensure analytical method
performance criteria are met.
Analytical method performance characteristics:
The elements of method performance that must be measured to assess whether a method is capable of producing data that is suitable
for its intended purpose (e.g., selectivity, precision, accuracy, limit of quantification_.
Analytical method performance criteria:
The targets for each analytical method performance characteristic that must be met if the method is to be considered to be
capable of producing data that is suitable for its intended purpose. For example: an RSD of no more than 1.0%, a bias of not
more than 2.0%, and a limit of quantitation of no less than 0.05%.
Method factor:
Any factor that forms part of the method definition.
Noise factor:
Any factor that cannot be controlled or that is allowed to vary randomly from a specified population (e.g., operator, reagent
supplier).
Method parameter:
Any factor that can be varied continuously or can be specified at controllable unique levels. For example: A flow rate would
be an example of a factor that can be varied continuously whereas GC liner type or column type would be examples of parameters
that can be specified at controllable unique levels.
Critical quality attribute:
A quality attribute for which there is a substantial risk of impacting the safety or efficacy of a product. The safety and
efficacy can be achieved by demonstrating measurable control of the quality attribute (i.e., product specification, intermediate
specification, in-process tests or process controls).
Method robustness:
The robustness of an analytical procedure is a measure of its capacity to remain unaffected by small, but deliberate variations
in method parameters and provides an indication of its reliability during normal usage (ICH Q2 Validation of Analytical Procedures:
Text and Methodology). The definition of robustness using a QbD approach adapted from the guideline would be: The robustness of an analytical method is defined as the capacity of its analytical method performance characteristics to
remain unaffected by small variations in analytical method parameters that the method is likely to encounter under normal
conditions of use.
Method ruggedness:
The ruggedness of an analytical method is the degree of reproducibility of test results obtained by the analysis of the same
sample under a variety of normal test conditions such as different laboratories, different analysts, different instruments,
different lots of reagents, different elapsed assay times, different assay temperatures, different days, and so forth (i.e,
US Pharmacopeia). The definition of ruggedness using a QbD approach would then be: The ruggedness of an analytical method is defined as the capacity of a method to remain unaffected by variations due to noise
factors that the method is likely to encounter under normal conditions. Ruggedness may be assessed through structured experimentation
such as intermediate precision studies.
Measurement systems analysis:
A specially designed experiment that seeks to identify the components of variation in the measurement.
Quality attribute:
A physical, chemical, biological or microbiological property or characteristic that should be within an appropriate limit,
range, or distribution to ensure the desired product quality.
Acknowledgments
The authors would like to acknowledge the EFPIA ADS topic team, the PhRMA ATG—and extended team members, the International
Pharmaceutical Aerosol Consortium on Regulation & Science (IPAC-RS)—for their sharing and consultation on concepts, and EFPIA's
Product Development and CMC (EFPIA PDC) ad hoc group for its sharing and consultation on concepts.
Mark Schweitzer* is global director of global analytical R&D at Abbott Laboratories in Abbott Park, IL, and a member of the Pharmaceutical
Research and Manufacturers of America (PhRMA) Analytical Technical Group (ATG). Matthias Pohl is head of TechOps QA at Novartis in Basel, Switzerland, and a member of the European Federation of Pharmaceutical Industries
and Associations (EFPIA) Analytical Design Space (ADS) topic team. Melissa Hanna-Brown is with Pfizer. Phil Nethercote and Phil Borman are with GlaxoSmithKline. Gordon Hansen is with Boehringer-Ingelheim. Kevin Smith is with Cephalon, and Jaqueline Larew is with Eli Lilly. Additional contributors to the article include John Carolan (Merck, Sharp & Dohme), Joachim Ermer (sanofi-aventis),
Pat Faulkner (Pfizer), Christof Finkler (Roche), Imogen Gill (Pfizer), Oliver Grosche (Novartis), Jörg Hoffmann (Merck KGaA),
Alexander Lenhart (Abbott Laboratories), Andy Rignall (AstraZeneca), Torsten Sokoliess (Boehringer Ingelheim), and Guido Wegener
(Bayer Healthcare).
This paper is coauthored by members of the PhRMA ATG and the EFPIA ADS topic team. This paper is not officially sponsored
or endordsed by PhRMA or EFPIA. *Correspondence can be addressed to Mark Schweitzer at mark.schweitzer@abbott.com
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