Quality-by-design (QbD) is being used successfully for production processes; the same QbD approach can be applied to analytical procedures. QbD is “A systematic approach to development that begins with predefined objectives and emphasizes product and process understanding and process control, based on sound science and quality risk management.” (1). The analytical procedure can be viewed as a process with a reportable result as its output. To better apply QbD to analytical procedures, it is useful to compare the application of QbD in the two areas. The analogous terms are illustrated in the Figure and discussed in this paper.
Figure: Analogous Terms for the Major Steps in the QbD Approach. QbD for analytical procedures is analogous to that for production processes.
A proposed definition for process validation is “the collection and evaluation of data, from the process design stage throughout production, which establishes scientific evidence that a process is capable of consistently delivering quality products” (2). For an analytical procedure, the analogous definition is “the collection and evaluation of data and knowledge from the method design stage throughout its lifecycle of use, which establishes scientific evidence that a method is capable of consistently delivering quality data” (3). For this series, the method is the same as the analytical procedure and includes one full execution of the method, starting from the original sample, to produce a reportable result as defined in the US Food and Drug Administration out-of-specification (OOS) guidance, Investigating Out-of-Specification (OOS) Test Results for Pharmaceutical Production(4).
QbD in both areas starts with a clear definition of the intended use of the process. For production, this considers items such as the route of administration, dosage form, bioavailability, strength, and stability. For analytical procedures, a decision rule can be used to clearly and quantifiably state the intended use of the reportable result. Decision rules will be discussed in a future papers. For more information on decision rules, read the EURACHEM/CITAC Guide: Use of uncertainty information in compliance assessment (5) and the American Society for Mechanical Engineers (ASME) B188.8.131.52-2001 Guidelines for Decision rules; Considering measurement Uncertainty in Determining conformance to Specifications (6).
The predefined objective for a production process is the quality target product profile (QTPP), defined as “a prospective summary of the quality characteristics of a drug product that ideally will be achieved to ensure the desired quality, taking into account safety and efficacy of the drug product.” (7). For an analytical procedure, the predefined objective is the analytical target profile (ATP), defined as “the combination of all performance criteria required for the intended analytical application that direct the method development process.”(8). Criteria defined in the ATP refer to the quality data attributes of the reportable result (i.e., bias [accuracy] and target measurement uncertainty), which includes all sources of variability, including precision.
For the production process, the critical quality attributes are determined. The analogy for the analytical procedure is the performance characteristics required of the method. These include the bias, linearity, limit of detection, precision, etc.
During the design stage for the production process and analytical procedure, critical variables are identified and a control strategy is defined, which includes operational procedure controls. For an analytical procedure, examples of the critical variables are temperature, flow rate in high-peformance liquid chromatography (HPLC), etc. The controls can include variables and aspects related to any step in the method, such as the sample, sample preparation, standards, reagents, facility, equipment operating conditions, and the format of the reportable value (e.g., number of replicates).
The risk-based and multivariate approach to the development for an analytical procedure results in a method design space. Design space is defined in International Conference for Harmonisation (ICH) Q8, Pharmaceutical Development, as “The multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality.” (7). For an analytical procedure, the input variables are the critical variables identified during the design stage.
The value of comparing QbD for methods with production processes is that the tools, approaches, and experience gained in the production arena can be applied to analytical procedures. Future papers will explore how this can be achieved.
- Eurachem, EURACHEM/CITAC Guide: Use of uncertainty information in compliance assessment, 1st ed., 2007.
- FDA, Guidance for Industry—Process Validation: General Principles and Practices (Rockville, MD, Jan. 2011).
- P. Nethercote, et al., “QbD for Better Method Validation and Transfer,” Pharmamanufacturing.com, available at, www.pharmamanufacturing.com/articles/2010/060.html, accessed October 3, 2013.
- FDA, Investigating Out-of-Specification (OOS) Test Results for Pharmaceutical Production, (Rockville, MD, Oct. 2006).
- Eurachem, Use of uncertainty information in compliance assessment, available at: http://www.eurachem.org/index.php/publications/guides/uncertcompliance.
- ASME, B184.108.40.206-2001 Guidelines for Decision rules; Considering measurement Uncertainty in Determining conformance to Specifications.
- ICH Q8(R2), Pharmaceutical Development.
- M. Schweitzer, et al., “Implications and Opportunities of Applying QbD Principles to Analytical Measurements,” Pharmaceutical Technology 34 (2), 52–59, 2010.