by This guideline presents elements for consideration during the validation of analytical procedures included as part of registration applications. Analytical procedure validation forms a part of the analytical procedure lifecycle, as described within ICHQ14 Analytical Procedure Development. ICH Q2(R2) provides guidance on selection and evaluation of the various validation tests for analytical procedures. This guideline includes a collection of term sand their definitions, which are meant to bridge the differences that often exist between various compendia and documents of the ICH member regulatory authorities.
The objective of validation of an analytical procedure is to demonstrate that the analytical procedure is fit for the intended purpose. Further general guidance is provided on validation studies for analytical procedures.
This guideline applies to analytical procedures used for release and stability testing of commercial drug substances and products, hereafter referred to as ‘products’. The guideline can also be applied to other analytical procedures used as part of the control strategy (ICHQ10 Pharmaceutical Quality System) following a risk-based approach. The scientific principles described in this guideline can be applied in a phase-appropriate manner to analytical procedures used during clinical development.
The guideline is directed to common uses of analytical procedures, such as assay, potency, purity, impurity (quantitative or limit test), identity or other quantitative or qualitative measurements.
GENERAL CONSIDERATIONS FOR ANALYTICAL PROCEDURE VALIDATION
This guideline indicates the data which should be presented in a regulatory submission. Analytical procedure validation data should be submitted in the corresponding sections of the application (ICH M4Q The Common Technical Document For The Registration Of Pharmaceuticals For Human Use). Relevant data collected during validation (and any methodology used for calculating validation results) should be submitted to demonstrate the suitability of the procedure for the intended purpose. Suitable data derived from development studies (see ICH Q14) can be used as part of validation data. When an established platform analytical procedure is used for a new purpose, validation testing can be abbreviated, if scientifically justified.
Approaches other than those set forth in this guideline may be applicable and acceptable with appropriate science-based justification. The applicant is responsible for designing the validation studies and protocol most suitable for their product.
Reference materials, or other suitably characterised materials, with documented identity, purity, or any other characteristics as necessary, should be used in the validation study.
In practice, the experimental work can be designed so that the appropriate performance characteristics are considered simultaneously to provide sound, overall knowledge of the performance of the analytical procedure, for instance: specificity/selectivity, accuracy, and precision over the reportable range.
As described in ICH Q14, the system suitability test (SST) is an integral part of analytical procedures and is generally established during development as a regular check of performance. Robustness is typically evaluated as part of development prior to the execution of the analytical procedure validation study (ICHQ14). Finally, the analytical procedure validation strategy is developed based on knowledge of the analytical procedure and the intended purpose. This includes the required analytical procedure performance to ensure the quality of the measured result (ICH Q14). If successfully executed, the analytical procedure validation strategy will demonstrate that the analytical procedure is fit for the intended purpose.
Analytical Procedure Validation Study
A validation study is designed to provide sufficient evidence that the analytical procedure meets its objectives. These objectives are described with a suitable set of performance characteristics and related performance criteria, which can vary depending on the intended purpose of the analytical procedure and the specific technology selected. Section 3 “VALIDATIONTESTS, METHODOLOGY AND EVALUATION” summarises the typical methodologies and validation tests that can be used.
The validation study should be documented. Prior to the validation study, a validation protocol should be generated. The protocol should contain information about the intended purpose of the analytical procedure, the performance characteristics to be validated and the associated criteria. In cases where prior knowledge is used (e.g., from development or from previous studies),appropriate justification should be provided. The results of the validation study should be summarised in a validation report.
The experimental design of the validation study should reflect the number of replicates used in routine analysis to generate a reportable result. If justified, it may be acceptable to perform some validation tests using a different number of replicates or to adjust the number of replicates in the analytical procedure based on data generated during validation.
- Other quantitative measurements can follow the scheme for impurity, if the range limit is close to the DL/QL; other quantitative measurements can follow the scheme for assay (content or potency), if the range limit is not close to the DL/QL
- someperformancecharacteristicscanbesubstitutedwithtechnology-inherentjustificationinthecase of certain analytical procedures for physicochemical properties
- lack of specificity of one analytical procedure should be compensated by one or more other supporting analytical procedures, unless appropriately justified
- alternatively, a combined approach can be used to evaluate accuracy and precision
- where reproducibility has been performed and intermediate precision can be derived from the reproducibility data set, an independent study for intermediate precision is not required
Validation study design and evaluation
Validation During the Lifecycle of an Analytical Procedure
Changes may be required during the lifecycle of a validated analytical procedure. In such cases, partial or full revalidation may be required. Science and risk-based principles can be used to justify whether or not a given performance characteristic needs revalidation. The extent of revalidation depends on the performance characteristics impacted by the change.
Transfer of a validated analytical procedure should be considered in the context of analytical lifecycle changes inline with ICHQ14. When transferring analytical procedures to a different laboratory, a partial or full revalidation of the analytical procedure performance characteristics and/or comparative analysis of representative samples should be performed. Justification for not performing additional transfer experiments should be provided if appropriate.
Co-validation can be used to demonstrate that the analytical procedure meets predefined performance criteria by using data generated at multiple sites and could also satisfy the requirements of analytical procedure transfer at the participating sites.
The required reportable range is typically derived from the specification and depends on the intended use of the procedure. The reportable range is confirmed by demonstrating that the analytical procedure provides results with acceptable response, accuracy and precision. The reportable range should be inclusive of the upper and lower specification or reporting limits, as applicable.
recommended reportable ranges for common uses of analytical procedures; other ranges may be acceptable if justified. In some cases, e.g., at low amounts, wider upper ranges may be more practical.
Demonstration of Stability-Indicating Properties
A validated quantitative analytical procedure that can detect changes in relevant quality attributes of a product during storage is considered to be stability-indicating. To demonstrate specificity/selectivity of a stability-indicating test, samples containing relevant degradation products should be included in the study. These can include: samples spiked with target analytes and known interferences; samples that have been exposed to various physical and chemical stress conditions; and actual product samples that are either aged or have been stored under stressed conditions.
Considerations for Multivariate Analytical Procedures
For multivariate analytical procedures, results are determined through a multivariate calibration model utilising more than one input variable (e.g., a spectrum with many wavelength variables).The multivariate calibration model relates the input data to a value for the property of interest (i.e., the model output).
Successful validation of a multi variate procedure should consider calibration, internal testing
And validation.
Typically, development and validation are performed in two phases.
- In the first phase, model development consists of calibration and internal testing. Calibration data are used to create the calibration model. Test data are used for internal testing and optimisation of the model. The test data could be a separate set of data or part of the calibration setused in a rotational manner. This internal test step is used to obtain an estimate of the model performance and to fine-tune analgorithm’s parameters (e.g., the number of latent variables for partial least squares (PLS)) to select the most suitable model within a given set of data. For more details, see ICH Q14.
- In the second phase, model validation, a validation set with independent samples is used for validation of the model. For identification libraries, validation involves analyzing samples (i.e.,challenge samples) not represented in the library to demonstrate the discriminative ability of the library model.
Examples of reportable ranges for common uses of analytical procedures
| Use of analytical procedure | Low end of reportable range | High end of reportable range |
| Assay of a product (1) | 80% of declared content or 80% of lower specification acceptance criterion | 120% of declared contentor 120% of the upper specification acceptance criterion |
| Potency | Lowest specification acceptance criterion-20% | Highest specification acceptance criterion+20% |
| Content uniformity | 70% of declared content | 130% of declared content |
| Dissolution: Immediate release One point specification Multiple point specification Modified release | Q-45% of the lowest strength Lower limit of reportable range (as justified by the specification) or QL, as appropriate. Lower limit of reportable range (as justified by the specification) or QL, as appropriate. | 130 % of declared content of the highest strength |
| Impurity(1) | Reporting threshold | 120 % of specification acceptance criterion |
| Purity (as area%) | 80% of lower specification acceptance criterion | Upper specification acceptancecriterionor100% |
(1)Where assay and impurity are performed as a single test and only one standard is used, linearity should be demonstrated for both the reporting level of the impurities and up to 120% of the specification acceptance criterion for assay.
Samples used for the validation of quantitative or qualitative multivariate procedures require values or categories assigned to each sample, typically obtained by a reference analytical procedure, i.e., a validated or pharmacopoeial procedure.
When a reference analytical procedure is used, its performance should equal or exceed the expected performance of the multivariate analytical procedure. Analysis by the reference analytical procedure and multi variated at a collection should be performed on the same samples
(whenever possible) within a reasonable period of time to assure sample and measurement stability. In some cases, a correlation or conversion may be needed to provide the same unit of measure. Any assumptions or calculations should be described.
VALIDATION TESTS, METHODOLOGY AND EVALUATION
In the following chapters, experimental methodologies to evaluate the performance of an analytical procedure are described. These methodologies are grouped according to main performance characteristics dictated by the analytical procedure design. It is acknowledged that information about multiple performance characteristics may be derived from the same data set. Different approaches may be used to demonstrate that the analytical procedure meets the objectives and related performance criteria, if justified.
The specificity or selectivity of an analytical procedure can be demonstrated through absence of interference or comparison of results to an orthogonal procedure. In some cases, specificity/selectivity may be inherently given by the underlying scientific principles of the analytical procedure. Some experiments can be combined with accuracy studies.
Selectivity could be demonstrated when the analytical procedure is not specific. However, the test for an analyte to be identified or quantitated in the presence of potential interference should minimize that interference and demonstrate that the analytical procedure is fit for the intended purpose.
Where one analytical procedure does not provide sufficient discrimination, a combination of two or more procedures is recommended to achieve the necessary specificity/selectivity.
Specificity/selectivity can be shown by demonstrating that the identification and/or quantitation of an analyte is not impacted by the presence of other substances(e.g.,impurities, degradation products, related substances, matrices, or other components likely to be present).
Orthogonal Procedure Comparison
Specificity/selectivity can be verified by demonstrating that the measured result of an analyte is comparable to the measured result of a second, well characterised analytical procedure that ideally applies a different measurement principle.
Technology Inherent Justification
In some cases where the specificity of the analytical technology can be ensured and predicted by technical parameters (e.g., resolution of isotopes in mass spectrometry, chemical shifts in NMR spectroscopy), additional experimental studies may not be required, if justified.
For identification tests, a critical aspect is to demonstrate the capability to identify the analyte of interest based on unique aspects of its molecular structure and/or other specific properties. The capability of an analytical procedure to identify an analyte can be confirmed by obtaining positive results comparable to a reference material using samples containing the analyte, along with negative results from samples which do not contain the analyte. In addition, the identification test should be applied to materials structurally similar to or closely related to the analyte to confirm that a positive result is not obtained. The choice of such potentially interfering materials should be based on scientific judgement with a consideration of interferences that could occur.
Assay, Purity and Impurity Test (s)
The specificity/selectivity of an analytical procedure should be demonstrated to fulfil the accuracy requirements for the content or potency of an analyte in the sample.
Representative data (e.g., chromatograms, electropherograms, spectra, biological response) should be used to demonstrate specificity and relevant components should be labelled, if appropriate.
For separation techniques, suitable discrimination should be investigated at an appropriate level (e.g., for critical separations in chromatography, specificity can be demonstrated by the resolution of the two components which elute closest to each other).Alternatively, spectra of different components could be compared to assess the possibility of interference.
For non-separation techniques (e.g., bioassay, ELISA, qPCR), specificity can be demonstrated through the use of reference materials or other suitably characterised materials to confirm the absenceofinterferenceinrelationtotheanalyte.Incaseswheretheanalyteisaprocess-related impurity, specificity (non-interference) must also be confirmed against the product.
In case a single procedure is not considered specific or sufficiently selective, an additional procedure should be used to ensure adequate discrimination. For example, where a titration is used to assay a drug substance for release, the combination of the assay and a suitable test for impurities may be used.
Impurities or related substances area vailable or can be intentionally created:
For assay or potency, discrimination of the analyte in the presence of impurities and/or excipients should be demonstrated. Practically, this can be performed by spiking product with appropriate amounts of impurities and consequently demonstrating that the assay result is unaffected by the presence of these materials (e.g., by comparison with the assay result obtained on un manipulated samples).Alternatively, samples containing appropriate amounts of impurities could be generated through deliberate stressing of product materials.
For a purity or impurity test, discrimination can be established by stressing or spiking product to achieve appropriate levels of impurities or related substances and demonstrating the absence of interference.
Impurities or related substances are not available:
If impurities, related substances or degradation products cannot be prepared or isolated, specificity can be demonstrated by comparing the test results of samples containing typical impurities, related substances or degradation products with an orthogonal procedure. The approach taken should be justified.
The range of an analytical procedure is the interval between the lowest and the highest results in which the analytical procedure has a suitable level of response, accuracy and precision. The range can be validated through the direct assessment of reportable results (to generate a reportable range) using an appropriate calibration model (i.e., linear, non-linear, multivariate).
In some cases, the reportable range can be determined using one or more appropriate working ranges, depending on the sample preparation (e.g., dilutions) and the analytical procedure selected.
Typically, a working range corresponds to the lowest and the highest sample concentrations or purity levels presented to the analytical instrument for which the analytical procedure provides reliable results. Mathematical calculations are typically required to generate reportable results. Reportable range and working range could be identical.
In cases where materials of sufficient purity (or containing sufficient amounts of impurities)to validate the full range (e.g., 100%purity) cannot be generated, extra polation of the reportable range may be appropriate and should be justified.
A linear relationship between analyte concentration and response should be evaluated across the range of the analytical procedure to confirm the suitability of the procedure for the intended purpose. The response can be demonstrated directly on the product or suitable reference materials, separate weighings of analyte, or predefined mixtures of the components (e.g., by dilution of a solution of known content), using the proposed procedure.
Linearity can be evaluated with a plot of signals as a function of analyte concentration or content, and should demonstrate the analytical procedure capability across a given range to obtain values that are proportional to the true (known or theoretical) sample values. Test results should be evaluated by an appropriate statistical method (e.g., by calculation of a regression line by the method of least squares).
Data derived from the regression line may help to provide mathematical estimates of the linearity. A plot of the data, the correlation coefficient or coefficient of determination, y- intercept and slope of the regression line should be provided. An analysis of the deviation of the actual data points from the regression line is helpful for evaluating linearity (e.g., for a linear response, the impact of any non-random pattern in the residuals plot from the regression analysis should be assessed).
To assess linearity during validation, a minimum of five concentrations appropriately distributed across the range is recommended.
The measured data can be mathematically transformed if necessary (e.g., through the use of a log function).
Other approaches to the assessment of linearity should be justified.
Non-linear Response
Some analytical procedures may shown on-linear responses. In these cases, a model or function which can describe the relationship between the activity / concentration present and the response of the analytical procedure is necessary. The suitability of the model should be assessed by means of non-linear regression analysis (e.g., coefficient of determination).
For example, immunoassays or cell-based assays may show an S-shaped response. S-shaped test curves occur when the range of concentrations is wide enough that responses are constrained by upper and lower asymptotes. Common models used in this case are four- or five-parameter logistic functions, though other acceptable models exist.
Fortheseanalyticalprocedures,theevaluationoflinearityisseparatefromconsiderationofthe shape of the concentration-response curve. Thus, linearity of the concentration-response relationship is not required. Instead, analytical procedure performance should be evaluated across a given range to obtain values that are proportional to the true (known or theoretical) sample values.
Multivariate Calibration
Algorithms used for construction of multivariate calibration model scan be linear or n on-linear, aslongasthemodelisappropriateforestablishingtherelationshipbetweenthesignalandthe quality attribute of interest. The accuracy of a multivariate procedure is dependent on multiple factors, such as the distribution of calibration samples across the calibration range and the reference analytical procedure error.
In multivariate analysis, the measured data are commonly pre-treated through derivatives or normalisation.
Linearityassessment,apartfromcomparisonofreferenceandpredictedresults,shouldinclude information on how the analytical procedure error (residuals) changes across the calibration range. Graphical plots can be used to assess the residuals of the model prediction across the working range.
Validation of Lower Range Limits
Ifthequalityattributetobemeasuredrequirestherangeofananalyticalproceduretobeclose to the lower range limits of the procedure, detection limit (DL) and quantitation limit (QL),can be estimated using the following approaches.
Based on Visual Evaluation
Visual evaluation can be used for both non-instrumental and instrumental procedures.
The limit is determined by the analysis of samples with known concentrations and by establishing the minimum level at which the analyte can be reliably resolved and detected or quantitated.
Based on Signal-to-Noise
This approach is relevant for analytical procedures which exhibit base line noise. Determination of the signal-to-noise ratio is performed by comparing measured signals from samples with known low concentrations of analyte with those of blank samples. Alternatively, signals in an appropriate baseline region can be used instead of blank samples. The DL or QL are the minimum concentrations at which the analyte can be reliably detected or quantitated, respectively.Asignal-to-noiseratioof3:1isgenerallyconsideredacceptableforestimatingthe DL. For QL, a ratio of at least 10:1 is considered acceptable.
Thesignal-to-noiseratioshouldbedeterminedwithinadefinedregionand,ifpossible,situated equally around the place where the peak of interest would be found
Based on the Standard Deviation of the Blank
Measurement of the magnitude of background response is performed by analysing an appropriate number of blank samples and calculating the standard deviation of the responses.
Based on the Calibration Curve
A specific calibration curve should be evaluated using samples containing an analyte in the range of the DL and QL. The residual standard deviation of a regression line (i.e., root mean square error/deviation) or the standard deviation of y-intercepts of the regression lines can be used as the standard deviation.
Based on Accuracy and Precision at Lower Range Limits
Instead of using estimated values as described in the previous approaches, the QL can be directly validated by accuracy and precision measurements.
Recommended Data
The DL and the approach used for its determination should be presented. If the DL is determined based on visual evaluation or based on signal-to-noise ratio, the presentation of the relevant data is considered an acceptable justification.
In cases where an estimated value for the DL is obtained by calculation or extrapolation, this estimate can subsequently be validated by the independent analysis of a suitable number of samples known to be near or prepared at the DL.
The QL and the approach used for its determination should also be presented.
IftheQLwasestimated,thelimitshouldbesubsequentlyvalidatedbytheanalysisofasuitable number of samples known to be near or at the QL. In cases where the QL is well below (e.g., approximately 10 times lower than) the reporting limit, this confirmatory validation can be omitted with justification.
Forimpuritytests,theQLfortheanalyticalprocedureshouldbeequaltoorbelowthereporting threshold.
Accuracy and Precision
Accuracy and precision can be evaluated independently, each with a predefined acceptance criterion. Alternatively, accuracy and precision can be evaluated in combination.
Accuracy
Accuracy should be established across the reportable range of an analytical procedure and is typically demonstrated through comparison of the measured results with expected values. Accuracy should be demonstrated under regular test conditions of the analytical procedure (e.g., in the presence of sample matrix and using described sample preparation steps).
Accuracy is typically verified through one of the studies described below. In certain cases, accuracy can be inferred once precision, response within the range and specificity have been established.
Reference Material Comparison
The analytical procedure is applied to an analyte of known purity(e.g.,a reference material, a well characterised impurity or a related substance) and the measured versus theoretically expected results are evaluated.
Spiking Study
The analytical procedure is applied to a matrix of all components except the analyte where a known amount of the analyte of interest has been added. In cases where all the expected components are impossible to reproduce, the analyte can be added to or enriched in the test sample. The results from measurements on unspiked and spiked/enriched samples are evaluated.
Orthogonal Procedure Comparison
The results of the proposed analytical procedure are compared with those of an orthogonal procedure. The accuracy of the orthogonal procedure should be reported. Orthogonal procedures can be used with quantitative impurity measurements to verify primary measurement values in cases where obtaining samples of all relevant components needed to mimic the matrix for spiking studies is not possible.
Recommended Data
Accuracyshouldbeassessedusinganappropriatenumberofdeterminationsandconcentration levels covering the reportable range (e.g., 3 concentrations/3 replicates each of the full analytical procedure).
Accuracy should be reported as the mean percent recovery of a known added amount of analyte in the sample or as the difference between the mean and the accepted true value, together with anappropriate100(1-α)% confidence interval(or justified alternative statistical interval).The observed interval should be compatible with the corresponding accuracy acceptance criteria, unless otherwise justified.
For impurity tests, the approach for the determination of individual or total impurities should be described (e.g., weight/weight or area percent with respect to the major analyte).
For quantitative applications of multivariate analytical procedures, appropriate metrics, e.g., root mean-squared error of prediction (RMSEP), should be used. If RMSEP is found to be comparable to acceptable root mean-squared error of calibration (RMSEC)then this indicates that the model is sufficiently accurate when tested with an independent test set. Qualitative applicationssuchasclassification,misclassificationrateorpositivepredictionratecanbeused to characterise accuracy.
Precision
Validation of tests for assay and for quantitative determination of impurity(purity)includes an investigation of precision.
Precision should be investigated using authentic homogeneous samples or, if unavailable, artificially prepared samples (e.g., spiked matrix mixtures or samples enriched with relevant amounts of the analyte in question).
Repeatability
Repeatability should be assessed using:
- a minimum of 9 determinations covering the reportable range for the procedure(e.g.,3 concentrations/3 replicates each)
or
2. a minimum of 6 determinations at 100% of the test concentration.
Intermediate Precision
The extent to which intermediate precision should be established depends on the circumstances under which the procedure is intended to be used. The applicant should establish the effects of random events on the precision of the analytical procedure. Typical variations to be studied include different days, environmental conditions, analysts and equipment, as relevant. Ideally, the variations tested should be based on and justified by using analytical procedure understanding from development and risk assessment (ICH Q14). Studying these effects individually is not necessary. The use of design of experiments studies is encouraged.
Reproducibility
Reproducibility is assessed by means of an inter-laboratory trial. Investigation of reproducibility is usually not required for regulatory submission but should be considered in casesofstandardisationofananalyticalprocedure,forinstance,forinclusionofproceduresin pharmacopoeias and in cases where analytical procedures are conducted at multiple sites.
Recommended Data
The standard deviation, relative standard deviation (coefficient of variation), and an appropriate 100(1-α)% confidence interval(or justified alternative statistical interval) should be reported. The observed interval should be compatible with the corresponding precision acceptance criteria, unless otherwise justified.
Additionally, for multi variate analytical procedures, the routine metrics of RMSE Pen compass accuracy and precision.
Combined Approaches for Accuracy and Precision
An alternative to separate evaluation of accuracy and precision is to consider their total impact by assessing against a combined performance criterion.
Data generated during development may help determine the best approach and refine appropriate performance criteria to which combined accuracy and precision are compared.
Combined accuracy and precision can be evaluated by use of a prediction interval, a tolerance interval or a confidence interval. Other approaches may be acceptable if justified.
Recommended Data
If a combined performance criterion is chosen, results should be reported as a combined value to provide appropriate overall knowledge of the suitability of the analytical procedure. If relevanttojustifythesuitabilityoftheanalyticalprocedure,theindividualresultsforaccuracy and precision should be provided as supplemental information. The approach used should be described.
Robustness
The evaluation of the analytical procedure’s suitability within the intended operational environment should be considered during the development phase and depends on the type of procedure under study. Robustness testing should show the reliability of an analytical procedure in response to deliberate variations in analytical procedure parameters, as well as the stability of the sample preparations and reagents for the duration of the procedure, if appropriate. The robustness evaluation can be submitted as part of development data for an analytical procedure on a case-by-case basis or should be made available upon request.
For further details, see ICH Q14.
