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Analytical method validation (AMV) is required in the biopharmaceutical industry for all methods used to test final containers (release and stability testing), raw materials, in-process materials, and excipients. 1 AMV is also required for test methods that are used to validate the process prior to process validation. This article reviews current regulatory guidelines and the critical elements of analytical method development (AMD) that should be finalized before starting AMV.
Analytical method validation (AMV) is required in the biopharmaceutical industry for all methods used to test final containers (release and stability testing), raw materials, in-process materials, and excipients.1 AMV is also required for test methods that are used to validate the process prior to process validation. This article reviews current regulatory guidelines and the critical elements of analytical method development (AMD) that should be finalized before starting AMV.
Analytical methods are non-microbiological procedures which are used to test final containers, raw materials, in-process materials, and excipients for release or to determine stability. Guidance for microbiological test method validation is reviewed in the Parenteral Drug Association (PDA) Technical Report No. 33 and is outside of the scope of this guide.
The International Conference on Harmonisation (ICH)'s Q2A and Q2B and the United States Pharmacopoeia's USP 28 <1225> provide basic guidance.
However, following just these guidelines may not provide sufficient evidence that a method is suitable for product release.
FDA provides guidance on some of the issues that are not currently covered elsewhere.
In addition to compliance, a biopharmaceutical manufacturer must consider how the acceptance criteria for the process validation and all corresponding test methods are connected to the statistically derived product specifications that support acceptable product release-to-reject ratios.6,7 This article addresses three major deficiencies in the available guidelines:
The US Code of Federal Regulation 21 CFR 211.194(a)(2)) does not require recognized standard reference methods that have already been validated and published in a standard reference (such as USP 28) to be fully revalidated.
However, a biopharmaceutical firm must verify the suitability of these validated and published methods for their specific product and laboratory environment.
The verification of the suitability of methods under actual conditions of use is acceptable in the following cases:
In addition to the validation of new methods and the verification of previously validated methods, there is a third category, analytical method qualification (AMQ), which can be used under appropriate circumstances. However, before AMQ can be used, the exact scope or applicability and the detailed definitions for all three AMV categories must appear in a firm's written procedures. Table 1 summarizes the general expectations for the applicability and requirements for using one of these three validation strategies.10-12
Table 1: Applicability and Requirements for Analytical Method Qualification, Validation, and Verification
The final AMV document must include evidence that a particular test procedure is suitable for its intended use. Formal method validation studies should include all relevant ICH Q2A/B AMV characteristics listed in Table 2. The AMV protocol includes all scientifically justified and logical step-by-step validation studies. Whenever a test method is developed and will be used for drug development from Phase IIb onward, material for lot release, raw materials, in-process, or excipients, the AMD studies should contain a series of critical elements. The evaluation of AMD characteristics should precede AMV and should be summarized in an AMD report.
All AMD data should ideally be generated in a GLP/GMP environment using qualified equipment operated by qualified personnel, properly documented, summarized in an AMD report, and approved by quality assurance (QA). These results can be used as a qualified method for material tested before Phase IIb.
This also ensures that all AMD/AMQ data and results (summarized later in the AMV protocol) are valid from a compliance perspective. Critical method performance criteria that should be evaluated during AMD are described below. The main argument for evaluating these criteria during the AMD phase is to ensure that a test system is truly optimized before AMV. The validation process should not be a trial-and-error effort but rather a formal process demonstrating that all AMV acceptance criteria are readily met.
Table 2: Validation Characteristics Per ICH Q2A and Q2B
Assay performance criteria such as accuracy and specificity are defined for analytical methods in current guidelines and should always be covered during AMV according to assay classification.
The validation characteristics for each test method category are listed in Table 2 and briefly described below. The five ICH Q2A/B test categories are listed across the top row of Table 2, and the corresponding product specifications which would trigger the selection of one of these five categories are listed in the second row. For example, a test that reports levels of a characterized impurity should be validated as a quantitative limit test, and the AMV should include a formal evaluation of a total of seven characteristics.
On the other hand, a qualitative limit test for an impurity will have product specifications reported as less than a particular pre-set limit. The actual impurity concentrations are not reported; only the detection limit (DL) and specificity are required AMV characteristics. We are not quantitatively reporting this result, so we are not required to evaluate the quantitative method characteristics.
Accuracy is usually demonstrated by spiking an accepted reference standard into the product matrix. Percent recovery (observed/expected x 100%) should ideally be demonstrated over the entire assay range using multiple data points for each selected analyte concentration. Demonstrating accuracy is mostly affected by how well the reference standard is characterized and how well systematic sample preparation errors are controlled. Data generated for determining accuracy may be used for other validation characteristics, such as repeatability precision, linearity, assay range, and quantitation limit.
Repeatability precision indicates how precise the test results are under ideal conditions (same sample, operator, instrument, and day). Repeatability precision should be demonstrated over the entire assay range, as with accuracy. Accuracy data may be used.
Intermediate precision indicates how precise test results are on any given day. Intermediate precision should be demonstrated by generating a sufficiently large data set that includes replicate measurements of 100% product (analyte) concentration. Data should be generated in a well-designed matrix by several operators over several days using different instruments. If other critical assay elements (such as different columns in HPLC) are expected to significantly contribute to assay variability, they also should be integrated into the execution matrix. Different analyte concentrations over the entire assay range can be used to demonstrate intermediate precision, but the results must be converted to percent recoveries before they can be statistically compared.
Assay specificity is usually ensured by demonstrating insignificant levels of matrix interference and analyte interference. The matrix may interfere with assay results by increasing the background signal (noise), or matrix components may bind to the analyte of interest, potentially changing the assay signal. Spiking the analyte into the liquid product and comparing the net assay response increase versus the expected assay response provides information on potential interference. Other analytes that may be present in the product matrix should be spiked in proportional concentrations into the matrix (keeping final analyte concentrations constant). Results of unspiked versus spiked product should be compared.
Linearity of the assay response demonstrates proportionality of assay results to analyte concentration. Accuracy data may be used to evaluate this parameter. Linearity should be evaluated through a linear regression analysis — plotting individual results of either analyte concentration versus assay results or observed versus expected results. However, many biological assays are not linear, even after data transformation (such as logarithmic conversion). The overall fit of the curve for biological assays within the proposed assay range should not be evaluated by this validation characteristic.
A method's assay range must bracket the product specifications. By definition, the quantitation limit (QL) constitutes the lowest point of the assay range and is the lowest analyte concentration that can be quantitated with accuracy and precision. In addition to establishing the accuracy and precision of all analyte concentrations within the assay range, the assay response must also be linear (if applicable) as indicated by the regression line coefficient.
An analyte's detection limit (DL) is the concentration that yields a response significantly different from a blank or background signal. ICH Q2B suggests three different approaches for determining the DL. Other approaches may be acceptable if justified.
The QL is the lowest analyte concentration that can be quantitated with accuracy and precision. Since the QL constitutes the beginning of the assay range, the assay range criteria for linearity (if applicable) must be passed for the particular analyte concentration determined to be the QL.
It is important to remember that AMV provides the formal evidence that a test method is suitable for use under strictly controlled QC-testing conditions. The AMV protocol should be setup to deliver this evidence through appropriate acceptance criteria by varying sample batches, concentrations, operators, instruments, days, and other factors that are expected to vary during routine testing — within established sample and system suitability conditions and operational limits.6,7
All analytical procedures are associated with
. It is particularly true for biological assays that test for the purity, potency, and molecular interactions of biopharmaceuticals. If the product is unique, appropriate reference standards may not be available. The evaluation of the assay's accuracy and bias can be the most difficult part of the development and validation process. When replacing a method, comparing the results of the new method to those of the old method is often meaningful only when assay bias is taken into consideration. If we can compensate for the bias by modifying release specifications, we should be able to properly assess the quality of the process and the product and remain in compliance. Whenever relative percentages of various analytes are estimated using a single assay, the response factors must be established and integrated (normalized) into the calculations in order to consistently report accurate purity and impurity levels.
Samples, standards (secondary, in-house, or working), controls, and critical reagents should be evaluated for degradation during storage and potential freeze-thaw cycles. The negative effects of time on the bench during actual testing (room temperature), repetitive freeze-thaw cycles, and long-term storage of all materials used to generate test results should be evaluated and expiration times should be established.
The test system must be properly controlled to ensure reliable release-testing results. System suitability criteria should be established at the end of AMD, usually by running a set of control points. For each test, results are considered valid if all control points are within established limits. A test system must be able to reproduce measurable results of a homogeneous sample (control) to allow examination of differences among various product batches.
Sample suitability should be established during AMD and should ideally ensure that samples, controls, and standards are prepared identically and run simultaneously. In addition, sample suitability should include a statistical analysis of the number of replicates needed to generate significant release results. Single measurements may be acceptable if the production-process sampling can deliver truly batch-representative samples and the precision of assay repeatability is high compared to the product specifications (and therefore high compared to the batch-to-batch variation upon which these specifications are based).
Sometimes, data transformation (for example, logarithmic conversion) may lead to improved linearity for essentially non-linear assay responses. However, many biological assay response curves are not linear even after transformation.12,13 These are particularly difficult and should only be handled by experienced statisticians. Just as different test methodologies have different biases, changing statistical models may significantly change the final results. Some models may be inappropriate or may not provide acceptable results over the entire assay range.
Robustness, the lack of a significant effect when small changes are deliberately introduced into the test system, should ideally be addressed during method optimization and not as part of AMV. We should know a method's degree of robustness before starting formal AMV. Critical test system parameters (for example, the acceptable range of diluting the test sample) must be identified and controlled with appropriate operational limits. These limits should be described in the AMD report and documented in the method SOP. The SOP contains operational limits within the context of the overall system suitability criteria. These limits must be adhered to during validation. In addition, robustness should be tested in the AMD phase during or after method optimization because significant differences in the AMV results may be difficult to explain.
Why must we set protocol acceptance criteria? One answer is that we always must validate against limits or specifications, just as release testing results are compared to specifications. Only when all validation results are within established limits is an AMV considered acceptable (assuming that all limits were reasonably set). In addition, a validation document is a contract that not only defines in detail the test parameters but also the exact conditions and contingencies (acceptance criteria). All signing parties agree to the acceptance criteria before the protocol is executed. We must derive appropriate acceptance criteria so validations that should fail do fail and vice versa. Acceptance criteria should demonstrate to regulatory authorities (and ultimately patients) that quality systems and production processes are well designed and maintained to ensure consistent product quality. We also must satisfy acceptance criteria to remain in compliance.
Deriving reasonable AMV protocol acceptance criteria is one of the most difficult AMV tasks. In general, the most critical AMV characteristic for the quantitation of excipients, product potency, and impurities in biopharmaceutical manufacturing is intermediate precision. It provides very valuable information concerning the overall contribution of assay variability to the observed process variability. Accuracy is often of lesser concern because we are mostly concerned with batch-to-batch consistency and how well release batches match the purity and impurity levels of the clinical reference batches.6,7
When setting AMV intermediate precision acceptance criteria, one may only know the observed production-process variability. The best approach is to derive acceptance criteria for intermediate precision from historical process data (observed or expected batch-to-batch variability and content uniformity) and the product specifications (existing or target). The AMV protocol should not permit test method variability to be so high that we could face not being able to release product batches with results that should have fallen within specifications, or vice versa, release product batches with results outside of specifications.14 Establishing an efficient AMV execution matrix has been discussed elsewhere.6,7
In addition to satisfying regulatory requirements, AMV enables reasonable product specifications to be set and helps separate actual process variability from test method and sampling variability. AMV does not improve a test method but merely provides evidence for its suitability and the confidence in reported results. The quality of the development work determines the quality of the test method and, therefore, the quality of the production process and the product.
1. FDA. 21 CFR 211, Sections 165(e) and 194(a)(2). Available at:
2. PDA. Evaluation, validation and implementation of new microbiological testing methods: PDA Technical Report No. 33. PDA Journal of Pharmaceutical Science and Technology 2000; 54(Suppl. TR33).
3. ICH. Validation of Analytical Procedures. Q2A. Federal Register 1995; 60.
4. ICH. Validation of Analytical Procedures: methodology. Q2B. Federal Register 1996; 62.
5. United States Pharmacopoeia. USP 28 <1225>. Validation of Compendial Methods.
6. Krause SO. Development and validation of analytical methods for biopharmaceuticals, part I: development and optimization. BioPharm International 2004; 17(10):52-61.
7. Krause SO. Development and validation of analytical methods for biopharmaceuticals, part II: formal validation. BioPharm International 2004; 17(11):46-52.
8. CDER. Guidance for Industry. Bioanalytical Method Validation. Bethesda MD: FDA; 2001.
9. CBER. Draft Guidance for Industry. Analytical Procedures and Methods Validation. Bethesda MD: FDA; 2000.
10. Ritter N., et al. What is test method qualification? Proceedings of the WCBP CMC strategy forum, 24 July 2003. BioProcess International 2004; 2(8):32-47.
11. Krause SO. Good analytical method validation practice, part I: setting-up for compliance and efficiency. Journal of Validation Technology 2002; (9):23-32.
12. Krause SO. Good analytical method validation practice, part II: deriving acceptance criteria for the AMV protocol. Journal of Validation Technology 2003; (9):162-78.
13. Krause SO. Good analytical method validation practice, part III: data analysis and the AMV report. Journal of Validation Technology 2003; (10):21-36.
14. Krause SO. Analytical method validation. Presented at IVT's 10th Annual Meeting; 2004 Feb 25-28; Philadelphia, PA.
Stephan O. Krause, Ph.D., validation manager of QC assay support, Bayer HealthCare LLC, 800 Dwight Way, Berkeley, CA, 94701-1986, 510.705.4191, firstname.lastname@example.org