Method validation parameters

The general sample set for method validation parameters is the same for all matrices under consideration (except body fluids and tissues, see Section 4.2.5) ... 

Method validation is a term used for the suite of procedures to which an analytical method is subjected to provide objective evidence that the method, if used in the manner specified, will produce results that conform to the statement of the method validation parameters. Like many aspects quality assurance, method validation is of a relative nature. As with the concept of fitness for purpose, a method is validated for a particular use under particular circumstances. If those circumstances vary, then the method would need to be re-validated at least for the differences. Common sense should be used, and the analysts should use his or her skill and experience to decide what aspects of a method require validation and to what extent. The goal of satisfying client requirements is prominent in most published definitions of method validation, some of which are listed below ... 

Not all methods require each parameter detailed in table 8.2 to be established. For example, a method that only measures the active ingredient in a 100-mg cold cure as part of a quality control protocol is not concerned with limit of detection, the matrix is fixed, and the calibration range might only need to be established between 80 and 120 mg. An analysis that determines the presence or absence of the target analyte needs only to establish its selectivity, limit of detection, and ruggedness. Table 8.3 details some common analytical systems with their critical method validation parameters. 

Analytical system Critical method validation parameters Other validation parameters3... 

Figure 9.3. A diagram listing of common method validation parameters.

Table 2 Important method validation parameters for particular analytical requirements...

Anaiyticai requirements important method validation parameters other validation parameters... 

Principles and Characteristics Whereas parameters most relevant to method development are considered to be accuracy, system precision, linearity, range, LOD, LOQ, sensitivity and robustness, method validation parameters are mainly bias, specificity, recovery (and stability of the analyte), repeatability, intermediate precision, reproducibility and ruggedness. However, method development and validation are highly related. Also, validation characteristics are not independent they influence each other. Acceptance criteria for validation parameters should be based on the specification limits of the test procedure. Quantitation and detection limits need a statement of the precision at their concentration levels. Procedures used for validation of qualitative methods are generally less involved than those for quantitative analytical methods. According to Riley [82], who has discussed the various parameters for validation of quantitative analytical methods, the primary statistical parameters that validate an analytical method are accuracy and precision. 

Over 20 different methods have been proposed for predictions of secondary stmcture they can be categorized in two broad classes. The empirical statistical methods use parameters obtained from analyses of known sequences and tertiary stmctures. All such methods are based on the assumption that the local sequence in a short region of the polypeptide chain determines local stmcture as we have seen, this is not a universally valid assumption. The second group of methods is based on stereochemical criteria, such as compactness of form with a tightly packed hydrophobic core and a polar surface. Three frequently used methods are the empirical approaches of P.Y. Chou and G.D. Fasman and of J. Gamier, D.J. Osguthorpe and B. Robson (the GOR method), and third, the stereochemical method of V.l. him. 

Identification of sources of analytical bias in method development and method validation is another very important application of reference materials in geochemical laboratories. USGS applied simplex optimization in establishing the best measurement conditions when the ICP-AES method was introduced as a substitute for AAS in the rapid rock procedure for major oxide determinations (Leary et al. 1982). The optimized measurement parameters were then validated by analyzing a number of USGS rock reference samples for which reference values had been established first by classical analyses. Similar optimization of an ICP-AES procedure for a number of trace elements was validated by the analysis of U S G S manganese nodule P-i (Montaser et al. 1984). 

Analytical methods submitted by applicants are evaluated using harmonized criteria (see Section 2.5). The following presentation provides a brief overview of the validation parameters used in the registration of plant protection products and their a.i. These parameters are as follows ... 

Table 4 Validation parameters and criteria appUed for the assessment of enforcement analytical methods...

As mentioned above, the speciflcity, precision, recovery, and LOQ must be experimentally determined and reported for each method and for each relevant representative matrix. In Table 4 brief explanations are given to describe the validation parameters in... 

The sensitivity achieved (LOD) is not normally presented. It is recognized that different laboratories determine dissimilar values for this parameter and even within a laboratory the repeatability of the LOD is low. Most often, the lowest validated concentration gives an impression about the lowest levels that can be analyzed generally with acceptable results. A measure of selectivity is the intensity of blank results. This intensity is discussed by the participants of inter-laboratory validation studies. However, results are not reported and limits are not defined by CEN TC 275. The results of method validations of the several multi-residue/multi-matrix methods are not reported in the same way, but newer methods with limited scope generate analogous tables with validation results (as an example, see Table 7). 

Komit6 for Levnedsmidler (NMKL)]. The standard presents a universal validation approach for chemical analytical methods in the food sector. This includes methods for the main constituents and also for trace components. Therefore, the NMKL procedure focuses on primary validation parameters, such as specificity, calibration, trueness, precision, LOD or LOQ and does not refer to special requirements of pesticide residue analysis. 

Once the determinative or confirmatory method has been developed to take full advantage of the chemical properties of the analyte molecule, a study is necessary to prove that the method is valid. Criteria for method validation are outlined in guidelines from the US FDA, US EPA, and EU. A summary of the differences in regulatory requirements for method validation is provided in Table 3. The parameters addressed by all of the regulatory guidelines include accuracy, precision, sensitivity, specificity, and practicability. 

For non-compendial procedures, the performance parameters that should be determined in validation studies include specificity/selectivity, linearity, accuracy, precision (repeatability and intermediate precision), detection limit (DL), quantitation limit (QL), range, ruggedness, and robustness [6]. Other method validation information, such as the stability of analytical sample preparations, degradation/ stress studies, legible reproductions of representative instrumental output, identification and characterization of possible impurities, should be included [7], The parameters that are required to be validated depend on the type of analyses, so therefore different test methods require different validation schemes. 

Method validation is defined in the international standard, ISO/IEC 17025 as, the confirmation by examination and provision of objective evidence that the particular requirements for a specific intended use are fulfilled. This means that a validated method, if used correctly, will produce results that will be suitable for the person making decisions based on them. This requires a detailed understanding of why the results are required and the quality of the result needed, i.e. its uncertainty. This is what determines the values that have to be achieved for the performance parameters. Method validation is a planned set of experiments to determine these values. The method performance parameters that are typically studied during method validation are selectivity, precision, bias, linearity working range, limit of detection, limit of quantitation, calibration and ruggedness. The validation process is illustrated in Figure 4.2. 

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. 

There is no relevance to the order of the items in the list. In fact there is no agreed order in which to evaluate the characteristics. Method validation is like putting together a jigsaw puzzle . The more pieces that are in place, the clearer the picture becomes. The difference is that in validation all of the pieces are not always required. Table 4.6 shows when a study of the parameter is required for four different situations. However, it is important that validation is a planned activity or otherwise it can become very labour-intensive and inefficient. Some familiarity with the statistical terms introduced in Chapter 6 is essential before starting out to plan method validation. 

Associated with method validation, but not part of it, are two properties of results that have been previously mentioned. These parameters are measurement uncertainty and metrological traceability. Measurement uncertainty is covered in Chapter 6 and metrological traceability in Chapter 5. If considered at the planning stage of method validation, the information obtained during validation is a valuable input into measurement uncertainty evaluation. Traceability depends on the method s operating procedures and the materials being used. 

Precision estimates are key method performance parameters and are also required in order to carry out other aspects of method validation, such as bias and ruggedness studies. Precision is also a component of measurement uncertainty, as detailed in Chapter 6. The statistics that are applied refer to random variation and therefore it is important that the measurements are made to comply with this requirement, e.g. if change of precision with concentration is being investigated, the samples should be measured in a random order. 

The approaches described above give approximate values for the LoD and LoQ. This is sufficient if the analyte levels in test samples are well above the LoD and LoQ. If the detection limits are critical, they should be evaluated by using a more rigorous approach [1, 2, 14]. In addition, the LoD and LoQ sometimes vary with the type of sample and minor variations in measurement conditions. When these parameters are of importance, it is necessary to assess the expected level of change during method validation and build a protocol for checking the parameters, at appropriate intervals, when the method is in routine use. 

Method validation is carried out to provide objective evidence that a method is suitable for a given application. A formal assessment of the validation information against the measurement requirements specification and other important method performance parameters is therefore required. Although validation is described as a sequential process, in reality it can involve more than one iteration to optimize some performance parameters, e.g. if a performance parameter is outside the required limits, method improvement followed by revalidation is needed. 

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