Examples method validation precision

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 ... 

An example of the minimum requirement for potency assay of the drug substance and drug product is tabulated in Table 4. Note that the postponement of intermediate precision is aligned with previous discussion that the use of early phase analytical method resides mainly in one laboratory and is used only by a very limited number of analysts. Each individual company s phased method validation procedures and processes will vary, but the overall philosophy is the same. The extent of and expectations from early phase method validation are lower than the requirements in the later stages of development. The validation exercise becomes larger and more detailed and collects a larger body of data to ensure that the method is robust and appropriate for use at the commercial site. 

Before any method validation is started, the scope of validation must be fixed, comprising both the analytical system and the analytical requirement. A description of the analytical system includes the purpose and type of method, the type and concentration range of analyte(s) being measured, the types of material or matrices for which the method is applied, and a method protocol. On the basis of a good analysis lies a clear specification of the analytical requirement. The latter reflects the minimum fitness-for-purpose criteria or the different performance criteria the method must meet in order to solve the particular problem. For example, a minimum precision (RSD, see below) of 5% may be required or a limit of detection (LOD) of 0.1% (w/w) [2,4,15,58]. The established criteria for performance characteristics form the basis of the final acceptability of analytical data and of the validated method [58]. 

Fit the purpose calibration. It is common sense to check instrument performance each day, and GLP requirements simply formalize the performance and documentation of these checks. On the other hand, it is also important to use the right test (full calibration, verification, system suitability test, or instrument and method validation) to verify the performance and to avoid needlessly lengthy procedures. As already discussed (see Sections 13.2.3 and 13.3.1), it is not always necessary to perform a MS full calibration every day. For example, if a particular MS is used only to record complete full-scan mass spectra, a daily calibration or verification of the calibration of the m/z ratio scale is required. However, in the case where a MS is coupled with an LC and utilized primarily for the analysis of one or more analytes in the selected ion monitoring (SIM) mode, it does not always require a daily verification of the calibration. In this specific case it is quite common in LC-MS and LC-MS/MS applications to test only the following performance parameters (a) sensitivity, (b) system precision,... 

The term method transfer does not formally appear in the current FDA regulations or guidance documents. The ICH requirement of reproducibility , however, is intended to demonstrate the precision of analyses between laboratories. As a successful part of the total method validation, this ana-lyst-to-analyst comparison at different laboratory sites serves to prove the method validity. Also, this portion of validation can occur during the original validation experiments or at a future date. As an example, a method is developed in an analytical R D group to be eventually transferred to QC labs, production facilities, or contract laboratories worldwide. These reproducibility experiments would be performed as method-transfer exercises. 

We have developed a protocol which describes how data generated from experimental studies commonly undertaken for method validation purposes can be used in measurement uncertainty evaluation. This paper has illustrated the application of the protocol. In the example described, the uncertainty estimate for three analytes in different oil matrices was evaluated from three experimental studies, namely precision, recovery and ruggedness. These studies were required as part of the method validation, but planning the studies with uncertainty evaluation in mind allowed an uncertainty estimate to be calculated with little extra effort. A number of areas were identified where additional experimental work may be required to refine the estimates. However the necessary data could be generated by carrying out additional analyses alongside routine test samples. Again this would minimise the amount of laboratory effort required. 

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. 

Computational modeling is a powerful tool to predict toxicity of drugs and environmental toxins. However, all the in silico models, from the chemical structure-related QSAR method to the systemic PBPK models, would beneht from a second system to improve and validate their predictions. The accuracy of PBPK modeling, for example, depends on precise description of physiological mechanisms and kinetic parameters applied to the model. The PBPK method has primary limitations that it can only predict responses based on assumed mechanisms, without considerations on secondary and unexpected effects. Incomplete understanding of the biological mechanism and inappropriate simplification of the model can easily introduce errors into the PBPK predictions. In addition values of parameters required for the model are often unavailable, especially those for new drugs and environmental toxins. Thus a second validation system is critical to complement computational simulations and to provide a rational basis to improve mathematical models. 

Before the performance of method transfer activities involving protocols and acceptance criteria, it was customary for a receiving laboratory to repeat some or all of the validation experiments. This laboratory was thereby deemed to be qualified as described above. The choice of validation parameter(s) depends highly on the type of method being transferred. For example, content uniformity assays to determine consistency of product potency depend heavily on the method and system precision. As a second example, a determination of trace impurities in an API could not be reproduced between two sites if their instruments did not yield similar limits of detection and limits of quantitation. A detailed discussion on the rational choice of validation parameters that would need to be repeated by the receiving laboratory is beyond the scope of this chapter. The reader is referred to the method validation chapter by Crowther et al. for additional information on this subject. 

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. 

Methods are either valid or they are not, and it has been said that method validation is no more than the expression of the full measurement uncertainty of a method. However, historically, there are eight performance parameters that are considered to be capable of validation. These are given in Table 1. The hierarchy shows the order in which the validation parameters might be studied. For example, if the method does not actually analyze the required substance, then there is no point in worrying about precision or robustness. 

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. 

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