Analytical method validation linearity

Apart from the qualification dossiers provided by vendors there seems, at present, to be very little information published on the performance of an operational qualification for capillary electrophoresis (CE) instruments other than a chapter in Analytical Method Validation and Instrument Performance. The chapter, written by Nichole E. Baryla of Eli Lilly Canada, Inc, discusses the various functions (injection, separation, and detection) within the instrument and provides guidance on the type of tests, including suggested acceptance criteria, that may be performed to ensure the correct working of the instrument. These include injection reproducibility and linearity, temperature and voltage stability, detector accuracy, linearity, and noise. 

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. 

A subset of the dynamic range is the linear dynamic range. The linear dynamic range is that portion of the range for which the appropriate response function is a linear one. Historically, obtaining a linear calibration model has been the desired outcome of an analytical method development activity and most guidance for analytical method validations include an assessment of linearity (along with the... 

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. 

Because physicochemical cause-and-effect models are the basis of all measurements, statistics are used to optimize, validate, and calibrate the analytical method, and then interpolate the obtained measurements the models tend to be very simple (i.e., linear) in the concentration interval used. 

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. 

For non-compendial procedures, the performance parameters that should be determined in validation studies include specificity/selectivity, linearity, accuracy, precision (repeatability and intermediate precision), detection limit (DL), quantitation limit (QL), range, ruggedness, and robustness [6]. Other method validation information, such as the stability of analytical sample preparations, degradation/ stress studies, legible reproductions of representative instrumental output, identification and characterization of possible impurities, should be included [7], The parameters that are required to be validated depend on the type of analyses, so therefore different test methods require different validation schemes. 

Method validation is the process of proving that an analytical method is acceptable for its intended purpose. Many organizations provide a framework for performing such validations (ASTM, 2004). In general, methods for product specifications and regulatory submission must include studies on specificity, linearity, accuracy, precision, range, detection limit, and quantitation limit. 

Analytical data generated in a testing laboratory are generally used for development, release, stability, or pharmacokinetic studies. Regardless of what the data are required for, the analytical method must be able to provide reliable data. Method validation (Chapter 7) is the demonstration that an analytical procedure is suitable for its intended use. During the validation, data are collected to show that the method meets requirements for accuracy, precision, specificity, detection limit, quantitation limit, linearity, range, and robustness. These characteristics are those recommended by the ICH and will be discussed first. 

Sensitivity is a measure of the rate of change of response with concentration. It is dependent on the method of detection and the nature of the analytes. The majority of analytical methods are designed around a linear response within a certain concentration range. However, it is always necessary to validate this linearity and the slope of the linearity plot determines the sensitivity of the method. 

The purpose of an analytical method is the deliverance of a qualitative and/or quantitative result with an acceptable uncertainty level. Therefore, theoretically, validation boils down to measuring uncertainty . In practice, method validation is done by evaluating a series of method performance characteristics, such as precision, trueness, selectivity/specificity, linearity, operating range, recovery, LOD, limit of quantification (LOQ), sensitivity, ruggedness/robustness, and applicability. Calibration and traceability have been mentioned also as performance characteristics of a method [2, 4]. To these performance parameters, MU can be added, although MU is a key indicator for both fitness for purpose of a method and constant reliability of analytical results achieved in a laboratory (IQC). MU is a comprehensive parameter covering all sources of error and thus more than method validation alone. 

Extent of Validation Depends on Type of Method On the one hand, the extent of validation and the choice of performance parameters to be evaluated depend on the status and experience of the analytical method. On the other hand, the validation plan is determined by the analytical requirement(s) as defined on the basis of customer needs or as laid down in regulations. When the method has been fully validated according to an international protocol [63,68] before, the laboratory does not need to conduct extensive in-house validation studies. It must only verify that it can achieve the same performance characteristics as outlined in the collaborative study. As a minimum, precision, bias, linearity, and ruggedness studies should be undertaken. Similar limited vahdation is required in cases where it concerns a fully validated method which is apphed to a new matrix, a well-established but noncol-laboratively studied method, and a scientifically pubhshed method with characteristics given. More profound validation is needed for methods pubhshed as such in the literature, without any characteristic given, and for methods developed in-house [84]. 

Typical parameters that are generally considered most important for validation of analytical methods are specificity, selectivity, precision, accuracy, extraction recovery, calibration curve, linearity, working range, detection limit, quantification limit, sensitivity, and robustness. 

The working range of an analytical method is the interval between the upper and lower concentrations of the analyte in the sample for which it has been demonstrated that the method has acceptable precision, accuracy and linearity. This interval is normally derived from linearity studies and depends on the intended application of the method. However, validating over a range wider than actually needed provides confidence that the routine standard levels are well removed from nonlinear response concentrations, and allows quantitation of crude samples in support of process development. The range is normally expressed in the same units as the test results obtained by the analytical method. 

The preparation and execution should follow a validation protocol, in which the scope of the method and its validation criteria should first be defined (46). The scope of the analytical method should be clearly understood since this will govern the validation characteristics that need to be evaluated. For example, if the method is to be used for qualitative trace residue analysis, there is no need to examine and validate its linearity over the full dynamic range of the equipment. The scope of the method should also include the different types of equipment and the locations where the method will be run. In this way, experiments can be limited to what is really necessary. For example, if the method is intended for use in one specific laboratory, there is no need to include other laboratories and different equipment in the validation experiments. 

Any analytical method should be validated. The U.S. Food and Drug Administration has proposed guidelines on submitting samples and analytical data for validation (78). In LC one should check at least specificity, selectivity, accuracy, precision, linearity, reproducibility, limit of detection, limit of quantitation, and robustness. 

---