Method validation data validity

Control All control points starting with the basic raw materials right through to the finished product must be identified. Descriptions of the specifications, test methods, reference standards, and methods validation data should be included. 

Table 4.42. Raw Data from Method Validation Tests...

In contrast to the requirements for enforcement methods, validation of a previously collaboratively tested method, which is used to generate data, should be validated for new laboratory conditions. Also, where published methods are submitted, validation is required, when applied to the relevant sample matrix and laboratory conditions. 

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. 

Finally, to avoid the parallel use of similar but not identical method validation studies to fulfil the registration requirements, e.g., of the EU, US Environmental Protection Agency (EPA) or Japanese authorities, an adaptation of different data requirements for residue analytical methods for post-registration control and monitoring purposes would help to save resources. 

Each individual method collection comprises a large number of methods, which often have different validation statuses. For instance, the most important Swedish multi-residue method (based on ethyl acetate extraction, GPC and GC) is validated for many pesticides by four laboratories, but other methods are presented with singlelaboratory validation data. Some methods in the Dutch and German manuals were tested in inter-laboratory method validation studies, but others by an independent laboratory or in a single laboratory only. 

Any validation and verification work performed must always be documented in such a way that the results can be checked and the scope of a method is clear. International standards, e.g., ISO 17025, contain separate sections regarding documentation, which should be observed. The NMKL procedure on method validation states that It is of particular importance that the report includes all raw data from the experimental work, or references to where such data can be found . In some circumstances this complete documentation is impractical. Even where it is practical, it is usually impossible to publish these results together with the methods. 

Obviously, a best or generally accepted documentation of performance data of validated multi-residue methods does not exist. Too many data are collected and then-detailed presentation may be confusing and impractical. Additionally, the validation of multi-residue methods is a continuous on-going process which started for many pesticides 20 years ago, when less comprehensive method requirements had to be fulfilled. For this reason, a complete and homogeneous documentation of method validation data cannot be achieved. 

Once you have confidence that your method is adequate from the preliminary work in the method tryout, you are ready to begin the method validation. The method validation provides additional data on accuracy and precision, and confirms that there are no problems due to interference. Method validation must be completed before beginning the analysis of the treated samples from the field. The validation should test the detector s response over the expected range of concentrations from the field. 

Guideline for Submitting Samples and Analytical Data for Methods Validation, February 1987, Center for Drugs and Biologies, Food and Drug Administration, Rockville, MD. 

If for some reason the method validation process is not a GLP study, or a component thereof, the laboratory should adhere to the same data recording and retention principles as described for method development. 

Frequency domain performance has been analyzed with goodness-of-fit tests such as the Chi-square, Kolmogorov-Smirnov, and Wilcoxon Rank Sum tests. The studies by Young and Alward (14) and Hartigan et. al. (J 3) demonstrate the use of these tests for pesticide runoff and large-scale river basin modeling efforts, respectively, in conjunction with the paired-data tests. James and Burges ( 1 6 ) discuss the use of the above statistics and some additional tests in both the calibration and verification phases of model validation. They also discuss methods of data analysis for detection of errors this last topic needs additional research in order to consider uncertainties in the data which provide both the model input and the output to which model predictions are compared. 

Table 1. Data elements required for analytical method validation [1, 8]...

Identify available information, including information from quality control charts, performance in proficiency testing rounds, literature and validation information on related methods and data concerning comparison with other methods. Use the available information and professional judgement to review each relevant validation issue and sign-off issues adequately addressed and documented. 

Method validation provides information concerning the method s performance capabilities and limitations, when applied under routine circumstances and when it is within statistical control, and can be used to set the QC limits. The warning and action limits are commonly set at twice and three times the within-laboratory reproducibility, respectively. When the method is used on a regular basis, periodic measurement of QC samples and the plotting of these data on QC charts is required to ensure that the method is still within statistical control. The frequency of QC checks should not normally be set at less than 5% of the sample throughput. When the method is new, it may be set much higher. Quality control charts are discussed in Chapter 6. 

This chapter deals with handling the data generated by analytical methods. The first section describes the key statistical parameters used to summarize and describe data sets. These parameters are important, as they are essential for many of the quality assurance activities described in this book. It is impossible to carry out effective method validation, evaluate measurement uncertainty, construct and interpret control charts or evaluate the data from proficiency testing schemes without some knowledge of basic statistics. This chapter also describes the use of control charts in monitoring the performance of measurements over a period of time. Finally, the concept of measurement uncertainty is introduced. The importance of evaluating uncertainty is explained and a systematic approach to evaluating uncertainty is described. 

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. 

There are four general steps to ensure this control and consequently to guarantee the data quality [32] quality control checks (QC), system suitability tests (SSTs), analytical methods validation (AMV), and analytical instrument qualification (AIQ) (see Figure 5). 

These components, rigorously interconnected, enable analytical chemists to produce accurate and reproducible data when unknown samples are analysed. Especially the two basal compartments are of crucial importance analytical methods validation and analytical instrument qualification. 

Sample preparation is often a parameter whose impact on the analysis is overlooked or underestimated. In reality, the composition of the sample matrix is often key to the quality of the data obtained at the end of the analysis. An important consideration for biopharmaceutical molecules is to minimize sample preparation because the impact of sample manipulations must be evaluated during methods validation. 

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