Below an outline of the 10 validation parameters is given and a short definition of each are presented in Table 1. To aid in the extraction of information from measurement data the Data Sheet S2 in Supplementary Material can be used.
Table 1. Short description of the validation parameters for which SOPs are presented. Robustness or ruggedness is the ability of a method to remain unaffected by small variations in method parameters. If the instructions from the manufacturer of a commercially available assay does not contain any information indicative of a robustness assessment the manufacturer should be contacted and asked to provide this information since it is likely that such data is available given that the method development was sound.
In case of an in-house method, the robustness should be investigated as a part of the method development and the results should be reflected in the assay protocol before other validation parameters are investigated. The reason for this is that a validation is linked to an assay protocol and changes in the latter might demand a new validation to be performed.
Identify critical parameters in the procedure, e. Perform the assay with systematic changes in these parameters, one at the time, using the same set samples at each occasion. If the measured concentrations do not depend on the changes, adjust the protocol by adding appropriate intervals, e. If the changes systematically alter the measured concentrations, lower the magnitude of the changes until no dependence is observed.
Incorporate the results into the protocol. Note: if many critical steps are identified the number of experiments can be reduced using dedicated software, e. There are three different types of precisions depending on the stipulated conditions and these are repeatability r , intermediate precision Rw , and reproducibility R. Repeatability is the variability observed when as many factors as possible, e.
For intermediate precision, all factors except laboratory are allowed to vary and for clarity the factors changed should be stated in the validation report. Repeatability is sometimes called within-run or within-day precision while intermediate precision is also known as between-run or between day repeatability.
Precision is difficult to quantify and it is therefore the inversely related imprecision that is commonly reported. Collect samples with known high and low concentrations of the measurand. Pool samples if necessary. At day 1—5 measure 5 replicates on each sample. Note: the days need not to be consecutive, only different. Five samples with different levels have been suggested as a general rule to cover a wide measuring range 7.
However, it can be argued that if the levels are chosen with care, for example, one above and one below the decision limit, two samples might be enough.
In addition, it is not always possible to obtain samples covering a wide range, e. If large volumes of the samples are available, more aliquots than the ones needed for the precision measurements can be prepared for use as internal quality control samples when the method has been put in service.
Other experimental schemes than the one suggested under points 2—3 in the procedure are possible, e. The latter scheme will allow for more different factors to be explored, which will give a better estimate of the variability. At the same time, it is very impractical and expensive if the method is, e. The quantity in which the trueness is measured is called bias b , which is the systematic difference between the test result and the accepted reference value.
If there exists an external quality control QC program, but no CRM, the bias b QC is calculated as the mean value of the deviations from the assigned QC values using formula 2. Note: the bias might be concentration-dependent and therefore b QC should preferably be calculated using a longitudinal QC sample. Once the bias is determined, it can be used to compensate the measured concentration resulting in a method without systematic effects 8.
If the bias is constant over the measurement interval the bias is simply subtracted from the measured value and if the bias is proportional to the measured concentration the correction is done by multiplication of a factor determined from bias evaluations at different concentrations. Alternatively, the calibrators can be assigned new values to compensate for the bias. The total bias is the sum of two components originating from the method and the laboratory, respectively.
When a CRM is available, manufacturers are obliged to calibrate their method against materials traceable to the CRM and then the total bias should in principle be equal to the laboratory bias. The intermediate precision provides information about the dispersion characteristics of the results within a laboratory with no regard to the true value of a measurand in a sample. Therefore, in the absence of a CRM, the measurements rather deliver relative concentrations as opposed to absolute ones that can be achieved if the calibrators were traceable to a CRM.
However, if different methods can be used for quantifying the same analyte and if a universal cutoff value is warranted there is a need for a CRM that can be used by the kit manufacturers to calibrate their methods against, in order to minimize the bias.
This will also enable calculating absolute concentrations but the uncertainty in the results must then include not only the uncertainty from the method but also the uncertainty of the assigned value for the CRM. Note: this way of calculating the u c assumes that the bias has been adjusted for as outlined in the trueness section above. Note: the results from the precision measurements can be used as an estimate of the uncertainty, e. Calculate the expanded uncertainty U using formula 4.
Note: the coverage factor for a given confidence interval is dependent on the degrees of freedom. Details on this and coverage factors for other confidence intervals can be found elsewhere 8. At least for the LLOQ, there is more than one definition and these can be classified as either determined based on the signals from the instrument or the calculated concentrations from samples. For the former, a number of blank samples are analyzed and the average and SD of the signal are calculated Run 16 blank samples immunodepleted matrix or sample diluent.
Calculate the mean and SD of the signal. Determine the concentration based on a signal of 10 SDs above the mean of the blank. To determine the concentration based on a signal the inverse of the calibration function must be used. The two most common models used in immunochemical calibrations are the four and five parametric logistic models. The four parametric function and its inverse are:. The parameters A — E should be available from the software used for data acquisition and analysis.
Analyze, in duplicates, samples with very low and very high concentrations of the measurand. Dilution linearity is performed to demonstrate that a sample with a spiked concentration above the ULOQ can be diluted to a concentration within the working range and still give a reliable result.
In other words, it determines to which extent the dose—response of the analyte is linear in a particular diluent within the range of the standard curve. Thereby dilution of samples should not affect the accuracy and precision. At the same time, the presence of a hook effect, i. Spike three samples undiluted with calibrator stock solution, as high as possible. Note: if possible, spike undiluted samples with to fold the concentration at ULOQ using the calibrator stock solution. Biological samples can also be diluted less than the prescribed concentration, if an assay allows to.
Make serial dilutions of the spiked samples, using sample diluent in small vials until the theoretical concentration is below LLOQ. Analyze the serial dilutions in duplicates and compensate for the dilution factor.
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Quantitative Immunoassay. Immunoassay Automation. Immunoassay Automation: A Practical Guide describes automation of immunoassay from the practical viewpoint of the clinical laboratory.
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