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    Modeling the Contributions of Cross-Reactant Chemicals in Immunoassays

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    Immunoassay techniques often involve reagents that react with constituents of samples other than the target compound. The phenomenon is called cross-reactivity. This paper describes a study with three objectives aimed at overcoming some of the problems related to crossreactivity: (1) to investigate the effects of cross-reactivity on the accuracy and precision of analytical measurements; (2) to develop mathematical techniques to “deconvolute” analytical measurements that contain cross-reactions; (3) to define the minimum analytical and statistical experimental design required to implement the mathematical techniques developed.

    It was found that cross-reactivity affects method accuracy, but not necessarily precision; an additive bias occurs because what is measured is actually the sum of the reactions of the target analyte plus nontarget cross-reactions. The study was successful in developing a stochastic model by which the desired “deconvolution” could always be accomplished under certain experimental conditions. The model and estimation procedures are algorithmically simple and extendable to any number of cross-reactants. The minimum experimental design requires that (1) the sample mixture be qualitatively prescreened for the identity of potential cross-reactants, (2) associative rate constants be determined for the target analyte and cross-reactants, (3) the associative rate constants not be equal for the specific reagent used, and (4) the analytical runs produce a time series of measurements over the period during which binding occurs.


    immunoassay, cross-reactivity, modeling, simulation

    Author Information:

    Show, IT
    Independent consultant, Encinitas, CA

    Show, MB
    Independent consultant, Encinitas, CA

    Williams, LR
    U.S. EPA, Las Vegas, NV

    Committee/Subcommittee: E47.13

    DOI: 10.1520/STP16762S