Validation Performance characteristics

The following is a hypothetical example of a validation protocol. For purposes of this example we will assume that SOP 123 is the procedure used to measure mass (in /ig) using a calibration curve from a standard solution (all samples are pipetted to 1 pL hence, we are actually measuring concentration, but volume is considered constant at 1 /iL, so the term mass will be used for the measurement). Validation performance characteristics are briefly defined in the context of a protocol. These terms are discussed in more detail in the following section on terms and definitions. Square brackets [ ] delineate general content of a section or parenthetic remarks. 

Fig. 4 evidently shows the calibration data to provide valid performance characteristics. The actual behavior of measurements will be correctly described by the calibration. The obtained standard deviation of the method applies to the whole operating range, as long as we can ensure that the variances are homogeneous. 

Validation and Application. VaUdated CFD examples are emerging (30) as are examples of limitations and misappHcations (31). ReaUsm depends on the adequacy of the physical and chemical representations, the scale of resolution for the appHcation, numerical accuracy of the solution algorithms, and skills appHed in execution. Data are available on performance characteristics of industrial furnaces and gas turbines systems operating with turbulent diffusion flames have been studied for simple two-dimensional geometries and selected conditions (32). Turbulent diffusion flames are produced when fuel and air are injected separately into the reactor. Second-order and infinitely fast reactions coupled with mixing have been analyzed with the k—Z model to describe the macromixing process. 

The ROTOBERTY internal recycle laboratory reactor was designed to produce experimental results that can be used for developing reaction kinetics and to test catalysts. These results are valid at the conditions of large-scale plant operations. Since internal flow rates contacting the catalyst are known, heat and mass transfer rates can be calculated between the catalyst and the recycling fluid. With these known, their influence on catalyst performance can be evaluated in the experiments as well as in production units. Operating conditions, some construction features, and performance characteristics are given next. 

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. ... 

To avoid any subjectivity in the judgement of performance characteristics presented by applicants, the permitted ranges of several parameters are fixed (e.g., recovery, repeatability, highest intensity of blank signals compared with the LOQ). However, in other cases professional judgement of the referee is required to assess validation results. Some of these aspects are discussed below. 

The Guidance Document uses a very pragmatic definition for these performance characteristics. The lowest successful validated level is defined as fhe LOQ. Specificify... 

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. 

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 ... 

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). 

Selectivity and specificity are important performance characteristics of analytical procedures, especially in connection with validation processes. Nevertheless, both terms are used mostly verbal and a quantification is avoided, as a rule (IUPAC see Vessman et al. [2001]). Moreover, the concepts of selectivity and specificity are used interchangeably and synonymously. Occasionally, specificity is regarded as an intensification of selectivity, viz. the ultimate of selectivity (den Boef and Hulanicki [1983] Persson and Vessman [1998, 2001] Prichard et al. [2001]). 

Typical performance characteristics that should be considered in the validation are precision, accuracy, limit of detection, limit of quantitation, selectivity, range, linearity, robustness, ruggedness... 

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. 

For aerobic gravity sewers, procedure 4 is the ultimate calibration of the sewer process model. This is based on procedures 1 to 3 using information from upstream and downstream wastewater samples and by including local sewer systems and flow characteristics, temperature and DO concentration values of the wastewater in the sewer. Example 7.2 outlines the results of calibration and validation performed on a 5 km intercepting sewer line. 

In general, the strengths of the ELISA are its selectivity and specificity, whereas its weaknesses are related to precision of measurement. Each assay varies — depending on the antibody, enzyme, enzyme conjugate, and measurement, as well as on assay format. Each should be validated so that its unique performance characteristics are known. 

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. 

ISO defines validation as Conformation by examination and provision of objective evidence that the particular requirements for a specified intended use are fulfilled. This is decided by using a number of performance characteristics. These are specificity, linearity, range, accuracy, precision, detection limit (DL), quantitation limit (QL), and robusmess. System suitability testing (SST) is an integral part of many analytical procedures. Definitions of these terms based on the recommendations of the ICH Guideline Q2 (Rl) are given in Table... 

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... 

Research in analytical chemistry is clearly an area where automation has a significant role to play. It is important that research data is fuUy validated and as accurate as possible. While it is not always possible to automate entire processes, the use of automated carousels to feed samples into a reaction system is an obvious area to improve the quality and rate of generation of data. It wiU also allow the researchers to quickly validate their proposed methodology on real-world samples and optimize the performance characteristics. This naturally requires a very close relationship between the researchers and the ultimate end-users of the analytical product. Given a good return for the investment, I am sure that the initial investment to automate the research activity will be justified and forthcoming. 

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. 

New biomarkers will be useful in hepatotoxicity risk assessment if the data quality and validity can be established. The FDA defines a valid biomarker as one that can be measured in an analytical test system with well-established performance characteristics and has an established scientific framework or body of evidence that elucidates the significance of the test results [160]. Although there is no formerly agreed upon path, biomarker validation should include appropriate end-points for study (i.e., toxicology, histopathology, bioanalytical chemistry, etc.) and dose- and time-dependent measurements. An assessment of species, sex and strain susceptibility is also important to evaluate across species differences. More specific considerations for validation of gene and protein expression technologies are reviewed by Corvi et al. and Rifai et al. [144, 147]. 

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. 

Validation of an analytical procedure is the process by which it is established, by laboratory studies, that the performance characteristics of the procedure meet the Ill... 

Typical analytical performance characteristics that should be considered in the validation of the types of procedures described in this chapter are listed below. Each validation characteristic is defined to ensure consistency in usage of terminology and interpretation ... 

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