Immunoassay Kinetics at Continuous Surfaces

Place, John F.; Sutherland, Ranald M.; Riley, Andrew M.; Mangan, Ciaran

doi:10.1007/978-1-4612-0483-1_8

Cited by 18 publications

(12 citation statements)

References 27 publications

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“…It is of interest to analyze the important variables that influence these assays. External diffusion limitations play a role in the analysis of immunoassays (Giaver, 1976;Eddowes, 1987;Bluestein et al, 1991;Place et al, 1991) and will signficantly affect the performance of hybridization assays. The influence of diffusion in immunoassays has been analyzed to some extent (Stenberg et al, 1982;Nygren and Stenberg, 1985;Stenberg and Nygren, 1982;Sadana and Sii, 1992a,b).…”

Section: Introductionmentioning

confidence: 99%

Single- and Dual-Fractal Analysis of Hybridization Binding Kinetics: Biosensor Applications

Sadana

Vo‐Dinh

1998

Biotechnol. Prog.

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The diffusion-limited hybridization kinetics of analyte in solution to a receptor immobilized on a biosensor or immunosensor surface is analyzed within a fractal framework. The data may be analyzed by a single- or a dual-fractal analysis. This was indicated by the regression analysis provided by Sigmaplot. It is of interest to note that the binding rate coefficient and the fractal dimension both exhibit changes in the same direction for both the single-fractal and the dual-fractal analysis examples presented. For example, for a single-fractal analysis and for the hybridization of 10 nM 16CFl (oligonucleotide) to 16B immobilized via sulfosuccinimidyl-6-(biotinamido)hexanoate and streptavidin using chemical and thermal regeneration (Abel, A. P.; Weller, M. G.; Duveneck, G. L.; Ehrat, M. Widmer, H. M. Anal. Chem. 1996, 68, 2905-2912), an increase in the fractal dimension, Df from 1.211 (chemical regeneration) to 1.394 (thermal regeneration), leads to an increase in the binding rate coefficient, k, from 86.53 (chemical regeneration) to 100.0 (thermal regeneration). An increase in the degree of heterogeneity on the biosensor surface leads to an increase in the binding rate coefficient. When a dual-fractal analysis was utilized, an increase in the fractal dimension value from Df1 to Df2 leads to an increase in the binding rate coefficient value from k1 to k2. The fractional order of dependence of the binding rate coefficient, k1, on (a) the analyte (rRNA) concentration in solution and (b) on the fractal dimension, Df1, for the hybridization kinetics to detect Listeria species (Fliss, R.; St-Laurent, M.; Emond, E.; Simard, R. E.; Lemieux, R.; Ettriki, A.; Pandian, S. Appl. Microbiol. Biotechnol. 1995, 43, 717-724.) further reinforces the fractal nature of the system. The binding rate coefficient(s) expressions developed as a function of the analyte concentration in solution and the fractal dimension are of particular value since they provide a means to better control of biosensor or immunosensor performance.

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Section: Introductionmentioning

confidence: 99%

Single- and Dual-Fractal Analysis of Hybridization Binding Kinetics: Biosensor Applications

Sadana

Vo‐Dinh

1998

Biotechnol. Prog.

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“…16 Convective-driven immunosorbent assays function through analyte complexation at antibody-functionalized addresses, 46 typically under nonequilibrium conditions. 47 Depending on binding kinetics, this process may not necessarily be transport limited and results in constantly changing address areas and analyte affinity, and depleted protein concentrations ( C o ) at downstream addresses. These phenomena result in situations not likely to satisfy stipulations 2, 3, and 4 ( i.e ., mass transport-limited, constant C o , and invariant address area) and additional considerations must be made.…”

Section: Discussionmentioning

confidence: 99%

Calibrant-Free Analyte Quantitation via a Variable Velocity Flow Cell

et al. 2016

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In this paper, we describe a novel method for analyte quantitation that does not rely on calibrants, internal standards, or calibration curves but, rather, leverages the relationship between disparate and predictable surface-directed analyte flux to an array of sensing addresses and a measured resultant signal. To reduce this concept to practice, we fabricated two flow cells such that the mean linear fluid velocity, U, was varied systematically over an array of electrodes positioned along the flow axis. This resulted in a predictable variation of the address-directed flux of a redox analyte, ferrocenedimethanol (FDM). The resultant limiting currents measured at a series of these electrodes, and accurately described by a convective-diffusive transport model, provided a means to calculate an “unknown” concentration without the use of calibrants, internal standards, or a calibration curve. Furthermore, the experiment and concentration calculation only takes minutes to perform. Deviation in calculated FDM concentrations from true values was minimized to less than 0.5% when empirically derived values of U were employed.

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“…Pisarchick et al (1992) have indicated that the details of the association of antibodies (or antigens) with antigencoated (or antibody-coated) surfaces are of tremendous significance for the development and characterization of immunodiagnostic devices, as well as for biosensors. Furthermore, external diffusional limitations play a role in the analysis of such assays (Bluestein et al, 1991;Eddowes, 1987Eddowes, /1988Place et al, 1991;Giaver, 1976). The influence of diffusion in such systems has been analyzed to some extent (Place et al, 1991;Stenberg et al, 1986;Nygren and Stenberg, 1985;Stenberg and Nygren, 1982;Sadana and Sii, 1992a,b;Sadana and Madagula, 1993).…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, external diffusional limitations play a role in the analysis of such assays (Bluestein et al, 1991;Eddowes, 1987Eddowes, /1988Place et al, 1991;Giaver, 1976). The influence of diffusion in such systems has been analyzed to some extent (Place et al, 1991;Stenberg et al, 1986;Nygren and Stenberg, 1985;Stenberg and Nygren, 1982;Sadana and Sii, 1992a,b;Sadana and Madagula, 1993). Kopelman (1988) indicates that surface diffusioncontrolled reactions that occur on clusters or islands are expected to exhibit anomalous and fractal-like kinetics.…”

Section: Introductionmentioning

confidence: 99%

Antigen‐Antibody Binding Kinetics for Biosensors: The Fractal Dimension and the Binding Rate Coefficient

Sadana¹

1995

Biotechnology Progress

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The diffusion-limited binding kinetics of antigen (or antibody or substrate) in solution to antibody (or antigen or enzyme) immobilized on a biosensor surface is analyzed within a fractal framework. The change in the fractal dimension, Df, is in the same direction as that in the forward binding rate coefficient, k1. An increase or decrease in the fractal dimension, Df leads to an increase or decrease in the forward binding rate coefficient, k1, respectively, irrespective of whether the antigen or the antibody is immobilized on the biosensor surface. For example, an increase in the concentration of IgG (MRC OX-19) (antigen) in solution from 1 to 10 micrograms/mL bound to polyclonal sheep anti-mouse IgG (antibody) immobilized on immunomagnetic beads leads to decreases in Df from 1.93 to 0.517 and in k1 from 2.72 to 0.064, respectively. The different examples analyzed and presented together indicate one means by which the forward binding rate coefficient, k1, may be controlled, that is, by changing the fractal dimension or the disorder on the surface. The versatility of the analysis is indicated by applying it to different examples for both types of systems, wherein either the antigen or antibody is immobilized on the surface. The analysis should assist in improving the stability, sensitivity, and response time of biosensors.

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Immunoassay Kinetics at Continuous Surfaces

Cited by 18 publications

References 27 publications

Single- and Dual-Fractal Analysis of Hybridization Binding Kinetics: Biosensor Applications

Single- and Dual-Fractal Analysis of Hybridization Binding Kinetics: Biosensor Applications

Calibrant-Free Analyte Quantitation via a Variable Velocity Flow Cell

Antigen‐Antibody Binding Kinetics for Biosensors: The Fractal Dimension and the Binding Rate Coefficient

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