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Analytical method validation precision

Traceability and MU both form parts of the purpose of an analytical method. Validation plays an important role here, in the sense that it confirms the fitness-for-purpose of a particular analytical method [4]. 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 [7]. Validation is 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 [2,11]. In Section 8.2.3, the classical method validation approach is described based on the evaluation of a number of method performance parameters. Summarized, the cri-teria-based validation process consists of precision and bias studies, a check for... [Pg.746]

Analytical methods validation is a critical component of the entire company validation program. A method is not declared acceptable until a collaborative crossover study is conducted between two development laboratories and at least one quality control laboratory to ensure proper precision, accuracy, and efficiency. In the new world of outsourcing, it is imperative that an analytical crossover study be conducted between the client and supplier before any work is begun on dosage form development. [Pg.226]

Test methods used in the laboratory are generally derived from pharmacopeias such as the US Pharmacopoeia, British Pharmacopoeia or European Pharmacopoeia. For test methods that are not from recognized pharmacopoeias, validation of the analytical methods is required. The validation includes testing for accuracy, specificity, ruggedness, robustness, precision, detection limit, quantitation limit and range. A discussion of analytical methods validation is presented in Section 9.6.5. [Pg.230]

Spectroscopic methods can provide fast, non-destructive analytical measurements that can replace conventional analytical methods in many cases. The non-destructive nature of optical measurements makes them very attractive for stability testing. In the future, spectroscopic methods will be increasingly used for pharmaceutical stability analysis. This chapter will focus on quantitative analysis of pharmaceutical products. The second section of the chapter will provide an overview of basic vibrational spectroscopy and modern spectroscopic technology. The third section of this chapter is an introduction to multivariate analysis (MVA) and chemometrics. MVA is essential for the quantitative analysis of NIR and in many cases Raman spectral data. Growth in MVA has been aided by the availability of high quality software and powerful personal computers. Section 11.4 is a review of the qualification of NIR and Raman spectrometers. The criteria for NIR and Raman equipment qualification are described in USP chapters <1119> and < 1120>. The relevant highlights of the new USP chapter on analytical instrument qualification <1058> are also covered. Section 11.5 is a discussion of method validation for quantitative analytical methods based on multivariate statistics. Based on the USP chapter for NIR <1119>, the discussion of method validation for chemometric-based methods is also appropriate for Raman spectroscopy. The criteria for these MVA-based methods are the same as traditional analytical methods accuracy, precision, linearity, specificity, and robustness however, the ways they are described and evaluated can be different. [Pg.224]

Method validation for NIR or Raman spectroscopic methods using chemometrics is outlined in USP chapter <1119>. The criteria for method validation are the same as other quantitative analytical methods accuracy, precision, intermediate precision, linearity, specificity, robustness. However, because these methods are statistical in nature and are based on a previously validated analytical method, the validation of MVA methods is somewhat different than traditional analytical methods. [Pg.236]

The process of method validation (i.e., evaluation of the assay) affects the quality of the quantitative data directly [9 A Guide to Analytical Method Validation, Waters Corporation]. Through method validation, it is assured that the method developed is acceptable. Issues involved in the validation of a mass spectrometry method for quantitative analysis are similar to those in any other analytical technique. The validation involves undertaking a series of studies to demonstrate the limit of detection G OD) limit of quantitation (LOQ) linear range specificity within-day precision and accuracy and day-to-day precision and accuracy, specificity, and robustness of the method. All of these parameters must be determined with those commonly accepted good laboratory practices criteria that are applicable in the vafidation of analytical methods. [Pg.491]

The specification development process is a data-driven activity that requires a validated analytical method. The levels of data needed include assay precision, replicate process results (process precision), and real-time stability profiles. A statistical analysis of these data is critical in setting a realistic specification. Most often, aggregation and fragmentation degradation mechanisms are common to protein and peptide therapeutics. Therefore, the SE-HPLC method provides a critical quality parameter that would need to be controlled by a specification limit. [Pg.535]

When an analytical method is being developed, the ultimate requirement is to be able to determine the analyte(s) of interest with adequate accuracy and precision at appropriate levels. There are many examples in the literature of methodology that allows this to be achieved being developed without the need to use complex experimental design simply by varying individual factors that are thought to affect the experimental outcome until the best performance has been obtained. This simple approach assumes that the optimum value of any factor remains the same however other factors are varied, i.e. there is no interaction between factors, but the analyst must be aware that this fundamental assumption is not always valid. [Pg.189]

Other features of an analytical method that should be borne in mind are its linear range, which should be as large as possible to allow samples containing a wide range of analyte concentrations to be analysed without further manipulation, and its precision and accuracy. Method development and validation require all of these parameters to be studied and assessed quantitatively. [Pg.269]

Option (Valid) presents a graph of relative standard deviation (c.o.v.) versus concentration, with the relative residuals superimposed. This gives a clear overview of the performance to be expected from a linear calibration Signal = A + B Concentration, both in terms of (relative) precision and of accuracy, because only a well-behaved analytical method will show most of the residuals to be inside a narrow trumpet -like curve this trumpet is wide at low concentrations and should narrow down to c.o.v. = 5% and rel. CL = 10%, or thereabouts, at medium to high concentrations. Residuals that are not randomly distributed about the horizontal axis point either to the presence of outliers, nonlinearity, or errors in the preparation of standards. [Pg.385]

In this article, an analytical method is defined as series of procedures from receipt of a sample to final determination of the residue. Validation is the process of verifying that a method is fit for purpose. Typically, validation follows completion of the development of a method. Validated analytical data are essential for monitoring of pesticide residues and control of legal residue limits. Analysts must provide information to demonstrate that a method intended for these purposes is capable of providing adequate specificity, accuracy and precision, at relevant analyte concentrations and in all matrices analyzed. [Pg.95]

Most often studies will be accepted by regulatory authorities even if they do not contain all information. For example, a summary, the scope, a separate notice regarding the residue definition or a schematic diagram of the analytical procedure are helpful and may avoid additional questions, but they are not essential. Also, detailed specification of standard glassware or chemicals commonly used in residue analysis is less important. Finally, data about extraction efficiency or analyte stability can be offered in separate studies or statements, which are also valid for other methods. However, each method must precisely describe at the minimum ... [Pg.101]

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. [Pg.121]

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). [Pg.121]

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. [Pg.121]

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. [Pg.129]

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. [Pg.319]

This analytical method provides very good precision and accuracy for the three parent herbicides over a 0.05-5.00 pgL range. Validation has been extended up to 20pgL-i. [Pg.378]

Accurate, precise and sensitive analytical methods are important to the collection of data needed for regulatory decisions about pesticide registration. This article describes the various components of analytical method development, validation and implementation that affect the collection of pesticide residue distribution data for regulatory assessment of environmental fate and water quality impacts. Included in this discussion are both the technical needs of analytical methods and the attributes of study design and sample collection needed to develop data that are useful for regulatory purposes. [Pg.603]

With respect to method application, once validation has been satisfactorily completed, there is little question that use of the analytical method in worker safety and re-entry studies falls under the full requirements of the GLP Standards. In addition, there should be an adequate level of quality control measurements taken in conjunction with the specimens so as to provide for a meaningful assessment of accuracy and precision, as well as verification of freedom from artifactual interferences. Along with these measurements there needs to be reasonably rigid data acceptance criteria in place (usually established during validation) which are consistently applied during the course of the specimen analytical phase of the study. [Pg.159]

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). [Pg.116]


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