Quantitation method validation

Full factorial designs can be used in quantitative method validation. With very few factors this is a feasible design. A simple way to display the experiment necessary for the validation is to display the assay runs in a table or matrix. For instance, suppose a method is run on two different machines and the goal is to assess intermediate precision components. We have the random effects of operator and day and a fixed... 

Methods and analytical results are often classified loosely as quantitative, semi-quantitative or qualitative (screening). These categories do not have well-defined or universally accepted boundaries. Since comparison of residue concentrations with legal limits requires exact quantitative results, the validation of quantitative methods is discussed here. 

Table 8 Summary of CSL parameters and criteria for single-laboratory validation of procedures involved in a quantitative method ... 

In summary, the CSL guidelines can be simply applied in each laboratory and contain very clear instructions. The validated procedures do not focus on the central analytical part only. Important secondary aspects of the whole procedure (sample processing, analyte stability, extraction efficiency) are also considered. For each parameter which is determined, different criteria for the evaluation of quantitative, semi-quantitative and screening methods are given. Here, it should be noted that compared with other guidelines the requirement for the precision of quantitative methods is very stringent (RSD < 10%). 

Verification implies that the laboratory investigates trueness and precision in particular. Elements which should be included in a full validation of an analytical method are specificity, calibration curve, precision between laboratories and/or precision within laboratories, trueness, measuring range, LOD, LOQ, robustness and sensitivity. The numbers of analyses required by the NMKL standard and the criteria for the adoption of quantitative methods are summarized in Table 10. 

Method validation is needed to demonstrate the acceptability of the analytical method. A recovery test on a chemical being determined should be performed in order to verify the reliability of the series of analyses. Recovery studies are usually conducted by spiking untreated sediment with the target chemical at the deteetion limit, quantitation limit and in the range of 10-50 times the detection limit. The method is considered acceptable when the recoveries typically are greater than 70%. When the recovery is less than 70%, an improvement in the analytical methods is needed. Where this is not possible for technical reasons, then lower recovery levels may be acceptable provided that method validation has demonstrated that reproducible recoveries are obtained at a lower level of recovery. Analysis is usually done in duplicate or more, and the coefficient of variation (CV) should be less than 10% to ensure that recoveries will be consistently within the range 70-110%. 

For method tryout, run a control sample and two fortifications from each site. One fortification should be done at the LOQ and the other at the highest expected residue level, perhaps 1000 x LOQ. If the recoveries are within the acceptable range of 70-120% and there are no interferences, proceed with the method validation. If interferences are present which prevent quantitation of the analyte, try additional cleanup steps with SPE or use a more selective detection method such as liquid chromatography/mass spectrometry (LC/MS). 

Whereas the components of (known) test mixtures can be attributed on the basis of APCI+/, spectra, it is quite doubtful that this is equally feasible for unknown (real-life) extracts. Data acquisition conditions of LC-APCI-MS need to be optimised for existing universal LC separation protocols. User-specific databases of reference spectra need to be generated, and knowledge about the fragmentation rules of APCI-MS needs to be developed for the identification of unknown additives in polymers. Method development requires validation by comparison with established analytical tools. Extension to a quantitative method appears feasible. Despite the current wide spread of LC-API-MS equipment, relatively few industrial users, such as ICI, Sumitomo, Ford, GE, Solvay and DSM, appear to be somehow committed to this technique for (routine) polymer/additive analysis. 

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. 

Before performing a validation method for a certain application, the scope of the method and its validation criteria should be defined first. The parameters to be investigated include compounds, matrices, types of formation, qualitative or quantitative method, detection or quantitation limit, linear range, precision and accuracy, types of equipment that will be used, and the location of the system. These steps of the validation method are illustrated in Fig. 1, which has been modified from Ref. [11],... 

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 fundamental unit in chemical measurement is the mole - amount of substance. A mole is the amount of a substance that contains as many atoms, molecules, ions or other elementary units as the number of atoms in 0.012 kg of carbon 12 (12C). It is the only dimensionless SI unit. In practical terms, it is almost impossible to isolate a mole of pure substance. Substances with a purity of better than 99.9% are rare one exception is silver, which can be obtained with a purity of 99.9995% which is referred to as five nines silver . Another problem is that it is not always possible to isolate all of the analyte from the sample matrix, and the performance of the chemical measurement may be matrix-dependent - a given response to a certain amount of a chemical in isolation may be different from the response to the same amount of the chemical when other chemicals are present. If it is possible to isolate quantitatively all of the analyte of interest from the accompanying sample matrix, then a pure chemical substance may be used for calibration. The extent to which the analyte can be recovered from the sample matrix will have been determined as part of the method validation process (see Chapter 4, Section 4.6.3). 

Nevertheless, it is clear to us that the field of psychopathology is undergoing a transformation. There have been dramatic advances in quantitative methods that allow researchers to evaluate the basic premise behind our nosological system. Thus far the implicit assumption of the DSM was that psychiatric disorders are well represented by categorical diagnoses. This assumption is not necessarily true and may be valid only for certain mental disorders. We believe that all DSM entities must be tested using taxometrics (with CCK or non-CCK procedures). If all diagnoses are tested, we are likely to find that many of them are best conceptualized as continua. 

Finally, there are custom two-step quantitation methods such as chromatography or ELISA that require a capture step for isolating the protein and then a quantitation step based on a standard curve of the purified target protein. The preliminary capture step may also concentrate the protein for increased sensitivity. These techniques are typically not available in a commercial kit form and may require extensive method development. They are more labor intensive and complex than the colorimetric or absorbance-based assays. In addition, recovery of the protein from and reproducibility of the capture step complicate validation. Despite these disadvantages, the custom two-step quantitation methods are essential in situations requiring protein specificity. 

Method validation is the process of proving that an analytical method is acceptable for its intended purpose. Many organizations provide a framework for performing such validations (ASTM, 2004). In general, methods for product specifications and regulatory submission must include studies on specificity, linearity, accuracy, precision, range, detection limit, and quantitation limit. 

Further discussion of method validation can be found in Chapter 7. However, it should be noted from Table 11 that it is frequently desirable to perform validation experiments beyond ICH requirements. While ICH addresses specificity, accuracy, precision, detection limit, quantitation limit, linearity, and range, we have found it useful to additionally examine stability of solutions, reporting threshold, robustness (as detailed above), filtration, relative response factors (RRF), system suitability tests, and where applicable method comparison tests. 

Analytical data generated in a testing laboratory are generally used for development, release, stability, or pharmacokinetic studies. Regardless of what the data are required for, the analytical method must be able to provide reliable data. Method validation (Chapter 7) is the demonstration that an analytical procedure is suitable for its intended use. During the validation, data are collected to show that the method meets requirements for accuracy, precision, specificity, detection limit, quantitation limit, linearity, range, and robustness. These characteristics are those recommended by the ICH and will be discussed first. 

Needham, S. and Ye, B., Method Validation for the Simultaneous HPLC/MS/MS Quantitation of Midazolam and 1-Hydroxymidazolam in Human Plasma, American Society for Mass Spectrometry 2002 Conference Abstract, Orlando, EL, USA, 2002. 

Jemal, M., Schuster, A., and Whigan, D. B. (2003). Liquid chromatography/tandem mass spectrometry methods for quantitation of mevalonic acid in human plasma and urine method validation, demonstration of using a surrogate analyte, and demonstration of unacceptable matrix effect in spite of use of a stable isotope analog internal standard. Rapid Commun. Mass Spectrom. 17, 1723-1734. 

Although the standard MSP is an appropriate approach for promoter methylation status screening, our results clearly showed that subsequent evaluation of the differential promoter methylation level should be conducted by quantitative MSP (qMSP) to quantify the degree of these alterations to make clear correlations to the severity of the psychiatric disorders (H.M. Abdolmaleky and S. Thiagalingam, unpublished). In addition, it could be further validated using a complementary quantitative method such as immunoprecipitation (IP) of the methylated DNA to confirm the results of the qMSP (34). 

Otilonium bromide Powder and tablet Non-destructive quantitation method for QC of three production steps (blend, cores and coated tablets). Validated method following ICH guidelines. Relative errors lower than 1 % 147... 

Resorcinol Water solution Non-destructive quantitation method. Precision lower than 0.15% in a temperature range of 9-35 °C. Method validated using ICH-adapted guidelines 151... 

ECVAM is the leading international center for alternative test method validation. Hartung et al. (29) summarized the modular steps necessary to accomplish stage 3 (test validation). The seven modular steps are (I) test definition, (2) within-laboratory variability, (3) transferability, (4) between-laboratory variability, (5) predictive capacity, (6) applicability domain, and (7) performance standards (29). Steps 2-4 evaluate the test s reliability steps 5 and 6 evaluate the relevance of the test. Successful completion of all seven steps is necessary to proceed to stage 4 (independent assessment or peer review). This modular approach allows flexibility for the validation process where information on the test method can be gathered either prospectively or retrospectively. The approach is applicable not only to in vitro test methods but also to in silico approaches (e.g., computer-based approaches such as quantitative structure-activity relationships or QSAR) and pattern-based systems (e.g., genomics and proteomics). 

Figure 5.1 shows the various characteristics and stages in a method validation program. For most quantitative methods of analysis, the method characteristics that require evaluation are accuracy, sensitivity, selectivity, precision and method limitations. Each of these characteristics have contributions from various effects, all of which require consideration within a method validation study. 

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. 

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