Validation method performance characteristics

In a widely accepted definition, an analytical method can be defined as the series of procedures from receipt of a sample to the production of the final result. Often, not all procedures can be validated in an adequate way. However, even in such cases, where all procedures of a method are validated, the performance characteristics obtained do not reflect all sources of error. In a recent paper,the complete ladder of errors is described in the following way ... 

The purpose of an analytical method is the deliverance of a qualitative and/or quantitative result with an acceptable uncertainty level. Therefore, theoretically, validation boils down to measuring uncertainty . In practice, method validation is done by evaluating a series of method performance characteristics, such as precision, trueness, selectivity/specificity, linearity, operating range, recovery, LOD, limit of quantification (LOQ), sensitivity, ruggedness/robustness, and applicability. Calibration and traceability have been mentioned also as performance characteristics of a method [2, 4]. To these performance parameters, MU can be added, although MU is a key indicator for both fitness for purpose of a method and constant reliability of analytical results achieved in a laboratory (IQC). MU is a comprehensive parameter covering all sources of error and thus more than method validation alone. 

The literature gives a wide range of practical guidelines for the evaluation of method performance characteristics [58]. Besides the diversity of approaches, also the terminology and way of reporting results vary widely. Differences may occur depending on the purpose and the application field of the method, and validation studies may become more difficult as the complexity of the analysis increases [86]. In what follows, terms and formulas are taken from the accepted IUPAC nomenclature for the presentation of results of chemical analysis [66]. For each validation parameter, definitions, ways of expression, determination guidehnes, and acceptance criteria are reported in Table 5. 

Use of Standardized Methods The first level of AQA is the use of validated or standardized methods. The terms validated and standardized here refer to the fact that the method performance characteristics have been evaluated and have proven to meet certain requirements. At least, precision data are documented, giving an idea of the uncertainty and thus of the error of the analytical result. In both validated and standardized methods, the performance of the method is known. 

Due to the demand for reliable and comparable methods, performance requirements have been established at a national and international level for implementation of official methods, e.g. by European legislation, by the CEN or the Association of the Analytical Communities (AOAC) International, and worldwide by Codex Alimen-tarius (CAC). Thus any method proposed to be used for official purposes must be validated in a collaborative trial study, resulting in defined method performance characteristics [4], The framework for the design and conduct of such collaborative trial studies, as well as the statistical evaluation, are also defined in appropriate protocols [5]. Any method that has been successfully validated according to these protocols can be recognised as an official method for use in legal cases or for international trade control purposes. 

The purpose of this protocol is to define the method performance characteristics to be observed, the design and execution of the validation experiments, the data analysis, and the acceptance criteria for the validation of SOP 123 for use on Product W. 

To validate SOP 123 for measuring mass of Product W, the quantitative method performance characteristics of accuracy, precision, linearity, and range will be assessed using the validation assays shown in the design matrix over two days and using two operators. As per ICH guideline Q2A, the validation experiments will be run from 40 pg to 180 jUg. The test lot will be diluted out and concentrated up to specific expected masses using the mass cited on certificate of analysis for the lot of Product W selected for the validation. The points on the Product W curve will be as follows 40 /xg, 50 /xg, 70 /xg, 90 /xg, 110 /xg, 130 pg, 150 pg, and 180 pg. 

A detailed review of the state of the art will be carried out by the University of Gent (occupational health issues), the University of Pau (environment issues) and the CSL Food Science Laboratory (food issues) in close collaboration with the SM T programme. This review, along with the summaries of the round-table discussions, will serve the basis of a book to be edited by the three partners and the SM T programme and published by a selected publisher. The review will include aspects of Community policy, industrial competiveness and analytical methods currently available for speciation (with validation and performance characteristics), and will identify future requirements and research needs. 

Scope. The scope of assay validation is to assess the essential test method performance characteristics of accuracy, reproducibility, repeatability, linear-ity, and limit of quantitation/detection [41]. A test method is evaluated for its readiness for assay validation against the following criteria (1) description of the test basis and scientific purpose, (2) case for relevance, (3) proposed practical application,... 

Validation may mean different things to different people, depending on the context and the application of analytical science. For food control and monitoring purposes, it is generally expected that validation includes the establishment of performance characteristics and evidence that the method fits the respective purpose. ... 

The assessment of validation data of CEN methods does not differ significantly from other validation schemes. The most important quantitative performance characteristics are trueness and precision. Additionally, some information about sensitivity... 

Even if most examples and procedures presented apply to in-house validation, the procedure does not distinguish between validations conducted in a single laboratory and those carried out within inter-laboratory method performance studies. A preference for inter-laboratory studies can be concluded from the statement that laboratories should always give priority to methods which have been tested in method performance studies. Within the procedure a profound overview of different categories of analytical methods according to the available documentation and previous external validation is given. For example, if a method is externally validated in a method performance study, it should be tested for trueness and precision only. On the other hand, a full validation is recommended for those methods which are published in the scientific literature without complete presentation of essential performance characteristics (Table 9). 

If analytical methods are validated in inter-laboratory validation studies, documentation should follow the requirements of the harmonized protocol of lUPAC. " However, multi-matrix/multi-residue methods are applicable to hundreds of pesticides in dozens of commodities and have to be validated at several concentration levels. Any complete documentation of validation results is impossible in that case. Some performance characteristics, e.g., the specificity of analyte detection, an appropriate calibration range and sufficient detection sensitivity, are prerequisites for the determination of acceptable trueness and precision and their publication is less important. The LOD and LOQ depend on special instmmentation, analysts involved, time, batches of chemicals, etc., and cannot easily be reproduced. Therefore, these characteristics are less important. A practical, frequently applied alternative is the publication only of trueness (most often in terms of recovery) and precision for each analyte at each level. No consensus seems to exist as to whether these analyte-parameter sets should be documented, e.g., separately for each commodity or accumulated for all experiments done with the same analyte. In the latter case, the applicability of methods with regard to commodities can be documented in separate tables without performance characteristics. 

Analytical methods, particularly those used by accredited laboratories, have to be validated according to official rules and regulations to characterize objectively their reliability in any special field of application (Wegscheider [1996] EURACHEM/WELAC [1993]). Validation has to control the performance characteristics of analytical procedures (see Chap. 7) such as accuracy, precision, sensitivity, selectivity, specificity, robustness, ruggedness, and limit values (e.g., limit of detection, limit of quantitation). 

According to USP 28 [1], validation of an analytical method is the process by which it is established, through the conduct of laboratory studies, that the performance characteristics of the method meet the requirements for the intended analytical applications. Therefore, validation is an important step in determining the reliability and reproducibility of the method because it is able to confirm that the intended method is suitable to be conducted on a particular system. 

The list will probably contain a mixture of processes that lead to values of the performance parameters and quality control checks. A more structured approach will now be taken to method validation. The important performance characteristics are shown in Table 4.6. 

Validation is the process of collecting documented evidence that the method performs according to the intended purpose. " The validation characteristics and the acceptance criteria to be applied in validation of HPLC methods for MAA/NDA filings and marketed products should comply with the international guidelines on method validation. Table 11 details validation activities to be conducted for type 1, type 2, and type 3 methods ... 

Changes in the analytical method or manufacturing processes may necessitate re-validation to ensure that the analytical method maintains its performance characteristics. The degree of re-validation depends on the nature of the change and should be assessed on a case-by-case basis. 

Methods used to determine the performance characteristics of finished products fall into Category III. Dissolution tests (excluding measurement) and drug release tests are examples of these types of methods. Precision is the only parameter required for these methods according to the regulatory guidances, although all validation parameters may be determined based on the intent of the method. 

Validation is the process of proving that a method is acceptable for its intended purpose. It is important to note that it is the method, not the results, that are validated (Chapter 10). The most important aspect of any analytical method is the quality of the data it ultimately produces. The development and validation of a new analytical method may therefore be an iterative process. Results of validation studies may indicate that a change in the procedure is necessary, which may then require revalidation. Before a method is routinely used, it must be validated. There are a number of criteria for validating an analytical method, as different performance characteristics will require different validation criteria. 

Considering the variety of analytical methods, it becomes obvious that different test methods require different validation schemes. ICH distinguishes mainly four different cases shown in Table 3. It is the responsibility of the applicants to choose the validation procedure and protocol most suitable for their method because different performance characteristics will require different validation criteria. 

The USP requirements for assay validation are very close to the ICH proposal. Here, three categories are distinguished. Category I corresponds to ICH assay, category II corresponds to ICH determinations of impurities. The additional category III includes analytical methods for the determination of performance characteristics (e.g., dissolution, drug release). For this category, the ICH assay characteristics are always sufficient. The objective of the analytical procedure... 

Method Validation is the process of estabhshing the performance characteristics and limitations of a method and of verifying that a method is fit for purpose, i.e. for use for solving a particular analytical problem. 

From the basic calibration of our method we can derive some performance characteristics of the method. This is important for method validation (see chapter 11)... 

Method development can start with minor modifications on an existing method or may require the development of a completely new one. If necessary, the selected method for solving an analytical problem may need further development or validation of more performance characteristics. 

Table 5.1 summarises the characteristics of a method that require validation together with the method features contributing to these characteristics and some example test procedures. It can be seen that there is a certain amount of overlap in the contributions and their test procedures for the various characteristics. For instance, a linearity test can give information on both the accuracy and the sensitivity of a method. The ruggedness test is normally included as part of a precision study, however it can also contribute to other performance characteristics such as sensitivity and method limitations. Despite this it fits best as part of a precision study where it can be used to effectively and efficiently link repeatability to reproducibility tests. 

There are several reasons for careful placement of the ruggedness test in a program of method validation tests. Firstly the ruggedness test itself can be a complex and time consuming task and thus should be carried out as late in the method validation as possible, (i.e. when most other performance characteristics have been established and are acceptable). This reduces the chance of a failed ruggedness test and for this reason it is recommended that the precision study be one of the last experiments in a validation study. 

Before any method validation is started, the scope of validation must be fixed, comprising both the analytical system and the analytical requirement. A description of the analytical system includes the purpose and type of method, the type and concentration range of analyte(s) being measured, the types of material or matrices for which the method is applied, and a method protocol. On the basis of a good analysis lies a clear specification of the analytical requirement. The latter reflects the minimum fitness-for-purpose criteria or the different performance criteria the method must meet in order to solve the particular problem. For example, a minimum precision (RSD, see below) of 5% may be required or a limit of detection (LOD) of 0.1% (w/w) [2,4,15,58]. The established criteria for performance characteristics form the basis of the final acceptability of analytical data and of the validated method [58]. 

Validation is needed to demonstrate that the analytical method complies with established criteria for different performance characteristics [82]. When these different characteristics are being evaluated individually, this is generally done for the analytical method as such—where the input is the purified or isolated analyte and the output is the analytical result. However, MU covers the whole analytical procedure, starting from the original sample lot. The assessment of MU (see Section 8.2.2) is in line with the so-called modular validation approach. Modular validation refers to the modularity of an analytical procedure divided up into several sequential steps needed to analyze the material. These may be sample preparation, analyte extraction, and analyte determination (Figure 7). Each step in the procedure can be seen as an analytical system and can thus be validated separately and combined... 

Extent of Validation Depends on Type of Method On the one hand, the extent of validation and the choice of performance parameters to be evaluated depend on the status and experience of the analytical method. On the other hand, the validation plan is determined by the analytical requirement(s) as defined on the basis of customer needs or as laid down in regulations. When the method has been fully validated according to an international protocol [63,68] before, the laboratory does not need to conduct extensive in-house validation studies. It must only verify that it can achieve the same performance characteristics as outlined in the collaborative study. As a minimum, precision, bias, linearity, and ruggedness studies should be undertaken. Similar limited vahdation is required in cases where it concerns a fully validated method which is apphed to a new matrix, a well-established but noncol-laboratively studied method, and a scientifically pubhshed method with characteristics given. More profound validation is needed for methods pubhshed as such in the literature, without any characteristic given, and for methods developed in-house [84]. 

On the one hand, even if an in-house vahdated method shows good performance and reliable accuracy, such a method cannot be adopted as a standard method. In-house validated methods need to be compared between at least eight laboratories in a collaborative trial. On the other hand, a collaborative study should not be conducted with an unoptimized method [58]. Interlaboratory studies are restricted to precision and trueness while other important performance characteristics such as specificity and LOD are not addressed [105]. For these reasons, single-laboratory validation and interlaboratory validation studies do not exclude each other but must be seen as two necessary and complementary stages in a process, presented in Figure... 

The validation requirements are discussed as they apply to both the sample preparation and sample analysis aspects of a dissolution method. The focus of the discussion in this chapter is on the validation considerations that are unique to a dissolution method. Validation is the assessment of the performance of a defined test method. The result of any successful validation exercise is a comprehensive set of data that will support the suitability of the test method for its intended use. To this end, execution of a validation exercise without a clearly defined plan can lead to many difficulties, including an incomplete or flawed set of validation data. Planning for the validation exercise must include the following determination of what performance characteristics to assess (i.e., strategy), how to assess each characteristic (i.e., experimental), and what minimum standard of performance is expected (i.e., criteria). The preparation of a validation protocol is highly recommended to clearly define the experiments and associated criteria. Validation of a test method must include experiments to assess both the sample preparation (i.e., sample dissolution) and the sample analysis. ICH Q2A [1] provides guidance for the validation characteristics of the dissolution test and is summarized in Table 4.1. 

Performance characteristics of residue methods are often only determined for major food animal species. The Joint FAO/IAEA Expert Consultation on Validation of Analytical Methods for Food Control (17) has discussed the issue of the availability of suitable analytical methods for determining compliance of residues in tissues of the so-called minor species with established MRLs. The Consultation has concluded that if metabolism and related pharmacokinetic data are similar in minor species to those in major species, only the recovery of the analytes in the minor species needs to be determined. If the recovery remains stable, there is no need to study the method performance any further. If the recovery is not stable the full set of performance data should be determined. 

The method s performance characteristics should be based on the intended use of the method. For example, if the method will be used for qualitative trace-level analysis, there is no need to test and validate the method s linearity over the full dynamic range of the equipment. Initial parameters should be chosen according to the analyst s best judgment. Finally, parameters should be agreed upon between the lab generating the data and the client using the data. 

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