Examples method validation accuracy

Execution of the method validation protocol should be carefully planned to optimize the resources and time required to complete the full validation study. For example, in the validation of an assay method, linearity and accuracy may be validated at the same time as both experiments can use the same standard solutions. A normal validation protocol should contain the following contents at a minimum ... 

Different levels of validation are usually defined. For example, methods proposed to be used worldwide in medically related applications must demonstrate high-quality data related to both precision and accuracy, whereas analytical methods used for research purposes require less stringent validation.1 High degrees of validation involve several laboratories and the assay of a large number of samples this tends to be very expensive and impractical for locally or occasionally used methods. 

This chapter discusses regulatory issues in HPLC laboratories with a focus on procedures and requirements for system qualification, calibration, method validation, and system suitability testing. Examples in a cGMP pharmaceutical environment are used to illustrate the various tools and systems used to ensure the degree of HPLC data accuracy necessary to achieve the delicate balance of regulatory compliance and laboratory productivity. 

Acceptance criteria for accuracy and precision of standards and QCs must be determined during method validation, and are analogous to acceptance criteria for chromatographic methods. IAs may not be as inherently precise as chemical methods, because IAs measure a reaction rather than a physicochemical property of the analyte. In cases where internal standards are not used for recovery correction, two to three replicate assays may be conducted on a single sample to improve precision. Despite all of the available mathematical transformations, it is important to remember that this is not a linear system and caution must be used as the concentrations approach either the upper or lower end of the standard curve. For example, variability becomes too large to be acceptable as the B/B0 value goes beyond <0.1 or >0.9 for most limited reagent assays. 

Analysis 5. Compute performance statistics Use appropriate statistical methods to compute these values. For example, EXCEL program for computation of accuracy/precision summary statistics for prestudy methods validation (http //www. aapspharmaceutica.com/ inside/sections/biotec/ applications/lba.asp)... 

Your instructor will select one experiment for teams to perform validation studies. An example is a gas chromatography experiment such as Experiment 32, but for one analyte. A flow injection analysis (FIA) experiment, such as Experiment 37, would be a good choice as well, since multiple measurements can be made rapidly. The team will determine linearity, accuracy, precision, sensitivity, range, limit of detection, limit of quantitation, and robustness (repeatability) of the method. In addition, a control chart will be prepared over at least one laboratory period. The instructor will have available a reference standard to use for accuracy studies. Plan for two laboratory periods for the completed study. A report of the method will be prepared and documented. Before beginning the experiment, you should review method validation in Chapter 4. 

The availability of an accurate, precise, and specific bioanalytical technique for the quantification of active drug moieties in plasma, hlood, or other hiological fluids is an essential prerequisite for the evaluation of the relationship between dose, concentration, and effect of hiotech drugs. In analogy to small molecules, these analytical techniques have to he validated and have to meet prespecified criteria regarding accuracy, precision, selectivity, sensitivity, reproducihihty, and stahihty, for example, those recommended hy the US Food and Drug Administration [10-12]. Additional requirements for bioanalytical method validation for macromolecules have recently been published [11]. 

Eurachem guide [285], which discusses when, why, and how methods should be validated. However, for the pharmaceutical industry, the main reference source is the ICH Guidelines [286], which provides recommendations on the various characteristics to be tested for the most common types of analytical procedures developed in a pharmaceutical laboratory. The main characteristics of any analytical method to be tested are specificity, linearity, accuracy, precision, solution stability, limits of detection and quantification, and robustness. Specific aspects should be considered for a CE method including method transfer between instrument manufacturers, reagent purity and source, electrolyte stability, capillary treatment and variations in new capillaries, and buffer depletion. Fabre and Altria [284] discuss CE method validation in more detail and include a number of examples of validated CE methods for pharmaceutical analysis. Included in Table 4.3 are a number of validated pharmaceutical assay methods. 

Although several reports have appeared on the validation of LC methods for specific pharmaceuticals, one particularly comprehensive study discussed system suitability, peak purity, system resolution, system selectivity, and stability-indicating properties. The example used in this work was the method developed for pipecuronium bromide. A final comprehensive method validation review has also shown the importance of each facet specificity, accuracy, precision, sensitivity, and robustness. 

Calibration curve data should be assessed to determine the appropriate mathematical regression that describes the instrument s response over the range of thecalibration curve (Section8.5). The report should include the back-calculated concentration values, accuracies, slopes, y-intercepts and correlation coefficients (R) and the coefficients of determination (R ) (Equation[8.18] in Section 8.3.1) for aU curves used in the validation. The value should be > 0.98 for each calibration curve. The R value (if used) must be > 0.99 for each calibration curve. An example table used to summarize the calibration curve statistics for each run used for method validation is shown as Table 10.2. 

The purpose of method validation is to demonstrate that an analytical method is suitable for its intended purpose and, for a quantative method, provides a reasonable estimate of the true value of the sample tested. Appropriate performance characteristics, such as accuracy and precision, must be demonstrated before making decisions based on test data. Method validation involves assessing method performance against predefined criteria, established based on the sample specifications and the type of measurement to be performed, for example, assay, identification, or limit test. A rigorous assessment of method performance versus predefined criteria provides assurance that the method will consistently provide a fit for purpose performance. Method characteristics to be evaluated during method validation are described by several guidelines [1,2] some of which are shown in Tables 3.1 and 3.2. 

Numerous examples of standard methods have been presented and discussed in the preceding six chapters. What we have yet to consider, however, is what constitutes a standard method. In this chapter we consider how a standard method is developed, including optimizing the experimental procedure, verifying that the method produces acceptable precision and accuracy in the hands of a single analyst, and validating the method for general use. 

The methods used for testing at various stages in the manufacturing process must be validated to show that they are fit for their intended application. For example, a method may be capable of measuring an analyte to a high of degree of accuracy and... 

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. 

Part—I has three chapters that exclusively deal with General Aspects of pharmaceutical analysis. Chapter 1 focuses on the pharmaceutical chemicals and their respective purity and management. Critical information with regard to description of the finished product, sampling procedures, bioavailability, identification tests, physical constants and miscellaneous characteristics, such as ash values, loss on drying, clarity and color of solution, specific tests, limit tests of metallic and non-metallic impurities, limits of moisture content, volatile and non-volatile matter and lastly residue on ignition have also been dealt with. Each section provides adequate procedural details supported by ample typical examples from the Official Compendia. Chapter 2 embraces the theory and technique of quantitative analysis with specific emphasis on volumetric analysis, volumetric apparatus, their specifications, standardization and utility. It also includes biomedical analytical chemistry, colorimetric assays, theory and assay of biochemicals, such as urea, bilirubin, cholesterol and enzymatic assays, such as alkaline phosphatase, lactate dehydrogenase, salient features of radioimmunoassay and automated methods of chemical analysis. Chapter 3 provides special emphasis on errors in pharmaceutical analysis and their statistical validation. The first aspect is related to errors in pharmaceutical analysis and embodies classification of errors, accuracy, precision and makes... 

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