Modelling of multi-component immunoassay kinetics — A new node-based method for simulation of complex assays

Algorithms for immunochromatographic assay: review and impact on future application

Lateral flow immunoassay (LFIA) is a critical choice for applications of point-of-care testing (POCT) in clinical and laboratory environments because of its excellent features and versatility. To obtain authentic values of analyte concentrations and reliable detection results, the relevant research has featured the application of a diversity of methods of mathematical analysis to technical analysis to allow for use with a small quantity of data. Accordingly, a number of signal and image processing strategies have also emerged for the application of gold immunochromatographic and fluorescent strips to improve sensitivity and overcome the limitations of correlative hardware systems. Instead of traditional methods to solve the problem, researchers nowadays are interested in machine learning and its more powerful variant, deep learning technology, for LFIA detection. This review emphasizes different models for the POCT of accurate labels as well as signal processing strategies that use artificial intelligence and machine learning. We focus on the analytical mechanism, procedural flow, and the results of the assay, and conclude by summarizing the advantages and limitations of each algorithm. We also discuss the potential for application of and directions of future research on LFIA technology when combined with Artificial Intelligence and deep learning.

Improving Lateral Flow Assay Performance Using Computational Modeling

The performance, field utility, and low cost of lateral flow assays (LFAs) have driven a tremendous shift in global health care practices by enabling diagnostic testing in previously unserved settings. This success has motivated the continued improvement of LFAs through increasingly sophisticated materials and reagents. However, our mechanistic understanding of the underlying processes that drive the informed design of these systems has not received commensurate attention. Here, we review the principles underpinning LFAs and the historical evolution of theory to predict their performance. As this theory is integrated into computational models and becomes testable, the criteria for quantifying performance and validating predictive power are critical. The integration of computational design with LFA development offers a promising and coherent framework to choose from an increasing number of novel materials, techniques, and reagents to deliver the low-cost, high-fidelity assays of the future.

Mathematical Modeling of Bioassays

The high affinity and specificity of biological receptors determine the demand for and the intensive development of analytical systems based on use of these receptors. Therefore, theoretical concepts of the mechanisms of these systems, quantitative parameters of their reactions, and relationships between their characteristics and ligand-receptor interactions have become extremely important. Many mathematical models describing different bioassay formats have been proposed. However, there is almost no information on the comparative characteristics of these models, their assumptions, and predictive insights. In this review we suggested a set of criteria to classify various bioassays and reviewed classical and contemporary publications on these bioassays with special emphasis on immunochemical analysis systems as the most common and in-demand techniques. The possibilities of analytical and numerical modeling are discussed, as well as estimations of the minimum concentrations that may be detected in bioassays and recommendations for the choice of assay conditions.

Mathematical Model of Serodiagnostic Immunochromatographic Assay

This article describes the mathematical model for an immunochromatographic assay for the detection of specific immunoglobulins against a target antigen (antibodies) in blood/serum (serodiagnosis). The model utilizes an analytical (non-numerical) approach and allows the calculation of the kinetics of immune complexes' formation in a continuous-flow system using commonly available software, such as Microsoft Excel. The developed model could identify the nature of the influence of immunochemical interaction constants and reagent concentrations on the kinetics of the formation of the detected target complex. On the basis of the model, recommendations are developed to decrease the detection limit for an immunochromatographic assay of specific immunoglobulins.

Theoretical limitations of quantification for noncompetitive sandwich immunoassays

Immunoassays exploit the highly selective interaction between antibodies and antigens to provide a vital method for biomolecule detection at low concentrations. Developers and practitioners of immunoassays have long known that non-specific binding often restricts immunoassay limits of quantification (LOQs). Aside from non-specific binding, most efforts by analytical chemists to reduce the LOQ for these techniques have focused on improving the signal amplification methods and minimizing the limitations of the detection system. However, with detection technology now capable of sensing single-fluorescence molecules, this approach is unlikely to lead to dramatic improvements in the future. Here, fundamental interactions based on the law of mass action are analytically connected to signal generation, replacing the four- and five-parameter fittings commercially used to approximate sigmoidal immunoassay curves and allowing quantitative consideration of non-specific binding and statistical limitations in order to understand the ultimate detection capabilities of immunoassays. The restrictions imposed on limits of quantification by instrumental noise, non-specific binding, and counting statistics are discussed based on equilibrium relations for a sandwich immunoassay. Understanding the maximal capabilities of immunoassays for each of these regimes can greatly assist in the development and evaluation of immunoassay platforms. While many studies suggest that single molecule detection is possible through immunoassay techniques, here, it is demonstrated that the fundamental limit of quantification (precision of 10 % or better) for an immunoassay is approximately 131 molecules and this limit is based on fundamental and unavoidable statistical limitations.

Rapid multiple immunoenzyme assay of mycotoxins

Mycotoxins are low molecular weight fungal metabolites that pose a threat as toxic contaminants of food products, thereby necessitating their effective monitoring and control. Microplate ELISA can be used for this purpose, but this method is characteristically time consuming, with a duration extending to several hours. This report proposes a variant of the ELISA method for the detection and quantification of three mycotoxins, ochratoxin A, aflatoxin B1 and zearalenone, in the kinetic regime. The main requirement for the proposed kinetic protocol was to provide a rapid method that combined sensitivity and accuracy. The use of biotin with an extended spacer together with a streptavidin-polyperoxidase conjugate provided high signal levels, despite these interactions occurring under non-equilibrium conditions. Duration of the individual mycotoxin assays was 20 min, whereas the analysis of all three mycotoxins in parallel reached a maximum duration of 25 min. Recovery of at least 95% mycotoxins in water-organic extracts was shown. The developed assays were successfully validated using poultry processing products and corn samples spiked with known quantities of mycotoxins. The detection limits for aflatoxin B1, ochratoxin A and zearalenone in these substances were 0.24, 1.2 and 3 ng/g, respectively.