Method validation Applications

The use of reference samples for method calibration and development/validation occurred hand-in-hand with the development of all modern instrumental methods of analysis. In fact, the two developments are intimately linked with one another. As already noted, G-i and W-i (Fairbaim et al. 1951 Stevens i960) illustrate first instance of reference samples specifically developed for calibration purposes. Following that, the use of BCR-i as a reference sample throughout the lunar program (Science 1970) is a prime illustration of the quality assurance and method validation applications in large-scale inter-laboratory measurement programs. 

Rozet, E., Wascotte, V., Lecouturier, N., Preat, V., Dewe, W., Boulanger, B., Hubert, P. Improvement of the decision efficiency of the accuracy profile by means of a desirability function for analytical methods validation application to a diacetyl-monoxime colorimetric assay used for the... 

The information given above should make it possible in general to predict the usefulness of x-ray methods in problems involving films. In principle, these methods should be useful occasionally when more than one film is present. The problems in such cases are complex rather than complicated. If these methods are applicable to a duplex film, for example, there will be three characteristic lines to be counted, and absorption effects in three regions to be considered. The three counts should, however, contain enough information in many cases to permit the drawing of valid conclusions. 

Brunger AT. Assessment of phase accuracy by cross validation the free R value. Methods and applications. Acta Cryst 1993 049 24-36. 

Zhang, S., Golbraikh, A., Oloff, S., Kohn, H., Tropsha, A. A novel automated lazy learning QSAR (ALL-QSAR) approach method development, applications, and virmal screening of chemical databases using validated ALL-QSAR models. 

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). 

Although we speak generally of validated methods , only the performance of a method applied to a particular range of materials (matrices) is reported. The possibility of matrix interferences or the efficiency of cleanup steps may vary with matrix type. For that reason, methods should be validated in all matrix types, which differ significantly. In the context of the validation of enforcement methods by applicants, significant difference is not a well defined term. To avoid any dispute about completeness of validation, five material types had been selected for crops, which usually... 

Better guidance for applicants seems necessary, with the objective that information about extraction efficiency is routinely considered in method validation studies. 

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. 

In Europe, very different concepts of method validation are in use. The extent of validation depends upon legal requirements (e.g., for enforcement methods provided by the applicant), upon the required level of acceptance (e.g., for CEN methods) and upon national resources. Undoubtedly, the best method validation is performed with the help of inter-laboratory studies of performance, but such studies can be uneconomic, too slow to reach completion or restricted in scope. 

It should be clear that the Darwin equation with its special LoRENTZ-polariza-tion factor as reported by Warren ([97], Eq. (4.7)) is only valid for unpolarized laboratory sources and the rotation-crystal method. An application to different setup geometries, for example to synchrotron GIWAXS data of polymer thin films is not appropriate. 

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. 

The guidelines stress, however, that internal quality control is not foolproof even when properly executed. Obviously it is subject to errors of both kinds , i.e. runs that are in control will occasionally be rejected and runs that are out of control occasionally accepted. Of more importance, IQC cannot usually identify sporadic gross errors or short-term disturbances in the analytical system that affect the results for individual test materials. Moreover, inferences based on IQC results are applicable only to test materials that fall within the scope of the analytical method validation. Despite these limitations, which professional experience and diligence can alleviate to a degree, internal quality control is the principal recourse available for ensuring that only data of appropriate quality are released from a laboratory. When properly executed it is very successful. 

Use of unusual automated methods of analysis, although desirable for control testing, may lead to delay in regulatory methods validation because the FDA laboratories must assemble and validate the system before running samples. To avoid this delay, applicants may demonstrate the equivalency of the automated procedure to that of a manual method based on the same chemistry. ... 

This book is the result of a cooperation between a chemometrician and a statistician. Usually, both sides have quite a different approach to describing statistical methods and applications—the former having a more practical approach and the latter being more formally oriented. The compromise as reflected in this book is hopefully useful for chemometricians, but it may also be useful for scientists and practitioners working in other disciplines—even for statisticians. The principles of multivariate statistical methods are valid, independent of the subject where the data come from. Of course, the focus here is on methods typically used in chemometrics, including techniques that can deal with a large number of variables. Since this book is an introduction, it was necessary to make a selection of the methods and applications that are used nowadays in chemometrics. 

Performing a thorough method validation can be a tedious process, but the reliability of the data generated with the method is linked directly to the application of quality assurance and quality control protocols, which must be followed assiduously (Table 6.5). 

Briefly, to assure quality assurance and quality control, samples are analyzed using standard analytical procedures. A continuing program of analytical laboratory quality control verifies data quality and involves participation in interlaboratory crosschecks, and replicate sampling and analysis. When applicable, it is advisable, even insisted upon by the EPA, that analytical labs be certified to complete the analysis requested. However, in many cases, time constraints often do not allow for sufficient method validation. Many researchers have experienced the consequences of invalid methods and realized that the amount of time and resources required to solve problems discovered later exceeds what would have been expended initially if the validation studies had been performed properly. 

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. 

Reproducibility represents the precision of the method between two or more laboratories and it is typically assessed during method transfer between laboratories, but may be assessed during method validation when more than one laboratory will be performing the method. Reproducibility would also be reported as the SD or RSD value of the mean results between laboratories. These data are not part of the marketing authorization application. 

Once an application has been accepted for evaluation, the Pharmaceutical Chemistry Evaluation Section, Toxicology Section and Clinical Evaluation Units evaluate the Module 3, 4 and 5 data, respectively. For applications relating to products of biological origin, a second copy of the Module 3 data is also evaluated by the TGA Laboratories (TGAL) Branch, which evaluates aspects such as laboratory methodology, method validation and shelf-life. 

Within the context that an appropriate method is one that fulfills the basic criteria of scientific acceptability and supports a variety of complex label claims. I am sure that the list of gaps is likely to be long. The Commission may have to embark upon a significant program of research, technology development and method validation before standard tests for all applications will be (1) up to the task set for them within the proposed legislation and (2) acceptable to the industries they affect. 

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). 

The final stage of a method validation study involves the testing of the method for miscellaneous limitations that need to be determined before the method can be passed on for its intended application. Limitations that are commonly included in the validation study are ... 

Polarographic methods of analysis of the derivatives of formaldehyde and crotonaldehyde without chromatography were developed. Analysis by HPLC was later considered so as to achieve greater resolution between the individual aldehydes and potential interferences. Methods validated using HPLC analysis include acetaldehyde and furfural. Tests indicated that HPLC analysis may be applicable to the Girard-T derivatives of other aldehydes, such as formaldehyde, propionaldehyde, and benzaldehyde. 

The CISs are rapidly becoming more popular and reliable as their field of application broadens. This is mainly due to the production of surface images by multipoint scanning and mapping. Hyperspectral imaging has proven its potential for qualitative analysis of pharmaceutical products and can be used when spatial information becomes relevant for an analytical application. Even if online applications and regulatory method validation require further development, the power of CIS in quality control and PAT needs no further demonstration, whatever the wavelength domain or method of spectra collection. 

A detailed method validation report may not be necessary until submission of the final market application. However, summary reports should be available to facilitate efficient data retrieval and fulfill requests from regulatory agencies for the information when required. 

The ISO definition of validation is confirmation by examination and provision of objective evidence that the particular requirements of a specified intended use are fulfilled [15]. Method validation is needed to confirm the fitness for purpose of a particular analytical method, that is, to demonstrate that a defined method protocol, applicable to a specified type of test material and to a defined concentration rate of the analyte —the whole is called the analytical system — is fit for a particular analytical purpose [4]. This analytical purpose reflects the achievement of analytical results with an acceptable standard of accuracy. An analytical result must always be accompanied by an uncertainty statement, which determines the interpretation of the result (Figure 6). In other words, the interpretation and use of any measurement fully depend on the uncertainty (at a stated level of confidence) associated with it [8]. Validation is thus the tool used to demonstrate that a specific analytical method actually measures what it is intended to measure and thus is suitable for its intended purpose [11,55,56]. 

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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