Immunoassay matrix effects

J.H. Skerritt and B.E. A. Rani, Detection and removal of sample matrix effects in agrochemical immunoassays, in Immunoassays for Residue Analysis, ed. R.C. Beier and L.H. Stanker, American Chemical Society, Washington, DC, pp. 29 3 (1996). 

The analytical response generated by an immunoassay is caused by the interaction of the analyte with the antibody. Although immunoassays have greater specificity than many other analytical procedures, they are also subject to significant interference problems. Interference is defined as any alteration in the assay signal different from the signal produced by the assay under standard conditions. Specific (cross-reactivity) and nonspecific (matrix) interferences may be major sources of immunoassay error and should be controlled to the greatest extent possible. Because of their different impacts on analyses, different approaches to minimize matrix effects and antibody cross-reactivity will be discussed separately. 

Figure 2 An illustration of matrix effects on immunoassay performance. Calibration curves of atrazine were run in buffer ( ), in skim milk (O) and in whole milk (A). Reprinted from M. Franek, V. Kolarand S. A. Evemin,Analyh ca Chimica Acta, 311,349-356, Copyright 1995, with permission from Excerpta Medica Inc...

Two general approaches have been used to overcome matrix effects (1) partial purification of the analyte prior to analysis by immunoassay ( cleanup methods) and (2) the use of a matrix blank when preparing the calibration curve. Both options are widely used, but each has its individual limitations. 

Because of the possibility that the herbicide alachlor could adulterate food if either poultry or livestock consumed contaminated materials, Lehotay and Miller evaluated three commercial immunoassays in milk and urine samples from a cow dosed with alachlor. They found that milk samples needed to be diluted with appropriate solvents (1 2, v/v) to eliminate the matrix effect. One assay kit (selected based on cost) was also evaluated for use with eggs and liver samples from chickens. Egg and liver samples were blended with acetonitrile, filtered, and diluted with water. Linear calibration curves prepared from fortified egg and liver samples were identical... 

Experiments with aldicarb sulfone in ground beef involved simple extraction with acetonitrile during tissue homogenization and resulted in a definite immunoassay response at the tolerance level of 10 qg kg A moderate, but rather consistent, matrix effect was observed. A more severe matrix effect was observed in bovine milk, blood, and urine. For the liquid matrices, sample dilution was not a satisfactory strategy, because the assay variability increased at lower concentrations, negating any benefit of reducing the matrix effect. This work clearly demonsttated that matrix effects are... 

Using a simple solvent extraction procedure to minimize matrix effects, a diclofop-methyl immunoassay was developed for milk, a number of edible plant products, and other matrices. Gas chromatography (GC) and liquid scintillation counting (LSC) of a C-labeled analyte were used as reference methods to compare with enzyme immunoassay (EIA) results. The methods were well correlated, with comparison of EIA... 

A sulfathiazole immunoassay was utilized to determine residues present in raw milk. The LOD was found to be 12 pg kg (based on 80% Bo) however, comparison of the calibration curve from an aqueous solution with a raw milk calibration curve indicated a significant matrix effect. 

It may be important to consider the variability of the matrix due to the physiological nature of the sample. In the case of LC-M/MS-based procedures, appropriate steps should be taken to ensure the lack of matrix effects throughout application of the method, especially if the nature of the matrix changes from the matrix used during method validation. For Microbiological and immunoassay, if separation is used prior to assay for study samples but not for standards, it is important to establish recovery and use it in determining results. In this case, possible approaches to assess efficiency and reproducibility of recovery are ... 

As with any analytical technique, generation of a reproducible standard curve with minimal error is critical. An assay calibration consists of several steps during which the value of the primary standard is transferred to the calibrators used in the final assay [22]. Immunoassay optimization is usually difficult due to protein heterogeneity and matrix effects and these factors, heterogeneity and matrix effects, will also affect MIP based assays [22]. 

Matrix effects have traditionally been dealt with by standard addition, i.e., addition of known amounts of standard analyte in the sample and extrapolating the result to zero added standard. However, this method is diflicult to apply for immimoassay because of the non-linearity of the concentration-response relationship. An alternative approach is presented as a modality to optimize the standards and buffer, so that it reproduces as closely as possible the effects of a specific matrix on immunoassay performances [42]. 

Obviously, the universal goal of any measurement technique is to obtain reproducible results regardless whether the samples come from different sources with different matrix effects, are run by different operators, in different laboratories, on different occasions and using different lots of reagents. This is usually accomplished by characterization of the newly developed assay in terms of sensitivity, selectivity, robustness and correctness (i.e., accuracy and precision). Evaluation of sensitivity and precision does not normally constitute a problem for a newly developed immunoassay, and accuracy can be attained by comparison with the results obtained by a reference ( standard ) method. However, the evolution of assay standardization from this point on is much more difflcult. The following section deals with application-specific problems related to validation and standardization of immimoassay. 

Each immunoassay should also be characterized in terms of cross-reactivity and possible matrix effects before analysis. In particular, the influence of the compounds frequently present in water samples (see Table 9.4 [144])... 

Evaluation, characterization, and testing of a particular analytical method is necessary to ensure the intended use of the method is met. In general, this process requires the determination of intra-and interlaboratory studies for precision and bias, method detection limits, matrix effects, interferences, limits of reliable measurements and ruggedness of the method. Before the EPA commits time and resources for an in-depth evaluation study, the developer must meet certain developmental criteria or justify why they were not met. The developer must also clearly define all necessary reagents as well as the underlying basis of the immunoassay. 

When appropriate, a dynamic (in-use) method evaluation of the assay will be performed following completion of the EMSL-LV single laboratory evaluation or confirmation. This type of evaluation is intended for immunoassays that are well-characterized and mature (i.e., a method where the developer has extensive performance data regarding matrix effects, cleanup, cross-reactivity, confirmatory analyses, and any other pertinent information). The data obtained during the dynamic evaluation will actually be used in a monitoring program. A dynamic evaluation can occur only where there is an immediate and urgent need for an analytical method. 

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