Sadana Biomarkers and Biosensors

Detection and Binding to Biosensor Surfaces and Biomarkers Applications
1. Auflage 2014
ISBN: 978-0-444-53795-9
Verlag: Elsevier Science & Techn.
Format: EPUB
Kopierschutz: 6 - ePub Watermark

E-Book, Englisch, 702 Seiten

Biomarkers and Biosensors offers thorough coverage of biomarker/biosensor interaction, current research trends, and future developments in applications of drug discovery. This book is useful to researchers in this field as well as clinicians interested in new developments in early detection and diagnosis of disease or the mode of operation of biomarkers. Biomarkers and Biosensors also emphasizes kinetics, and clearly delineates how this influences the biomarker market. - Offers thorough coverage of the kinetics of biomarker interaction with the biosensor surface - Provides evidence-based approach to evaluate effectiveness - Provides pharmaceutical chemists the possibilities and methodology in assessing the effectiveness of new drugs - Provides the information needed for the selection of the best biomarker for a specific application

Dr. Ajit Sadana has recently retired from the University of Mississippi after 34 years. His expertise is in the area of kinetics of binding and dissociation of analytes on biosensor surfaces.

Autoren/Hrsg.

Sadana, Ajit

Sadana, Neeti

Inhaltsverzeichnis

1;Front
Cover;1
2;Biomarkers and
Biosensors;4
3;Copyright;5
4;Contents;6
5;Preface;10
6;Chapter 1 - Introduction;12
6.1;1.1 INTRODUCTION;12
6.2;1.2 BIOMARKER ECONOMICS AND MARKETS;24
6.3;1.3 CHAPTER CONTENTS;27
6.4;REFERENCES;28
7;Chapter 2 - Modeling and Theory;34
7.1;2.1 INTRODUCTION;34
7.2;2.2 THEORY;38
7.3;REFERENCES;50
8;Chapter 3 - Detection of Cancer Biomarkers on Biosensor Surfaces;54
8.1;3.1 INTRODUCTION;54
8.2;3.2 THEORY;60
8.3;3.3 RESULTS;63
8.4;3.4 CONCLUSIONS;107
8.5;REFERENCES;109
9;Chapter 4 - Detection of Cancer Biomarkers by Biosensors: Part II;120
9.1;4.1 THEORY;120
9.2;4.2 RESULTS;122
9.3;4.3 CONCLUSIONS;171
9.4;REFERENCES;172
10;Chapter 5 - Detection of Biomarkers for Myocardial Infarction;180
10.1;5.1 INTRODUCTION;180
10.2;5.2 THEORY;181
10.3;5.3 RESULTS;183
10.4;5.4 CONCLUSIONS;203
10.5;REFERENCES;205
11;Chapter 6 - A Fractal Analysis of the Binding and Dissociation Kinetics of Glucose to Different Biosensor Surfaces;208
11.1;6.1 INTRODUCTION;208
11.2;6.2 THEORY;212
11.3;6.3 RESULTS;214
11.4;6.4 CONCLUSIONS;245
11.5;REFERENCES;247
11.6;FURTHER READING;255
12;Chapter 7 - A Fractal Analysis of Binding and Dissociation of Glucose to Different Biosensor Surfaces;256
12.1;7.1 INTRODUCTION;256
12.2;7.2 THEORY;258
12.3;7.3 RESULTS;260
12.4;7.4 CONCLUSIONS;275
12.5;REFERENCES;276
13;Chapter 8 - Binding and Dissociation of Biomarkers for Systemic Lupus Erythematosus: A Fractal Analysis of the Kinetics;282
13.1;8.1 INTRODUCTION;282
13.2;8.2 THEORY;283
13.3;8.3 RESULTS;285
13.4;8.4 OTHER AUTOIMMUNE REACTIONS;301
13.5;8.5 CONCLUSIONS;377
13.6;REFERENCES;379
14;Chapter 9 - Binding and Dissociation of Biomarkers for Alzheimer's Disease on Biosensor Surfaces: A Fractal Analysis;388
14.1;9.1 INTRODUCTION;388
14.2;9.2 THEORY;391
14.3;9.3 RESULTS;392
14.4;9.4 CONCLUSIONS;490
14.5;REFERENCES;492
15;Chapter 10 - Detection of Biomarkers for Different Diseases on Biosensor Surfaces;498
15.1;10.1 INTRODUCTION;498
15.2;10.2 THEORY;499
15.3;10.3 RESULTS;500
15.4;10.4 CONCLUSIONS;524
15.5;REFERENCES;526
15.6;FURTHER READING;530
16;Chapter 11 - Detection of Biomarkers for Different Diseases on Biosensor Surfaces Part II;536
16.1;11.1 INTRODUCTION;536
16.2;11.2 THEORY;538
16.3;11.3 RESULTS;540
16.4;13.4 CONCLUSIONS;564
16.5;REFERENCES;565
17;Chapter 12 - A Fractal Analysis of Biomarkers for Different Diseases on Biosensor Surfaces;570
17.1;12.1 INTRODUCTION;570
17.2;12.2 THEORY;571
17.3;12.3 RESULTS;573
17.4;12.4 CONCLUSIONS;600
17.5;REFERENCES;602
18;Chapter 13 - A Fractal Analysis of the Detection of Biomarkers for Different Diseases on Biosensor Surfaces;608
18.1;13.1 INTRODUCTION;608
18.2;13.2 THEORY;608
18.3;13.3 RESULTS;610
18.4;13.4 CONCLUSIONS;653
18.5;REFERENCES;654
19;Chapter 14 - Biosensor Economics and Manufacturing;664
19.1;14.1 INTRODUCTION;664
19.2;14.2 BIOSENSOR COST;665
19.3;14.3 BIOSENSOR MARKETS;669
19.4;14.4 CONCLUSIONS;687
19.5;REFERENCES;689
20;Index;692

Leseproben

Chapter 2 Modeling and Theory
Abstract
In a biosensor-based assay, the molecule to be detected (analyte) is present in solution and the appropriate receptor is immobilized on a solid surface. The interaction between the analyte and the receptor on the solid biosensor surface is detected either by a change in the refractive index (in surface plasmon resonance (SPR) instruments) or by changes in the fluorometric intensity, ultraviolet light intensity, etc. The SPR biosensor protocol analyzes the binding (and dissociation where applicable) kinetic curves using classical saturation models involving analyte-receptor binding using 1:1, 1:2, etc. ratios, generally under diffusion-free conditions and assuming that the receptors are homogeneously distributed over the sensor surface. Though a careful analysis and experimental protocol may eliminate or minimize the influence of diffusional limitations; realistically speaking, it is more appropriate to include a heterogeneous distribution on the sensing surface. Heterogeneity on the sensing surface and in the biosensor systems itself may be due to other reasons, such as nonspecific binding, inherent irregularities on the sensing surface, mixture of receptors on the surface, and mixture of analytes in solution which includes the analyte of interest. Keywords
Anomalous diffusion; Binding rate coefficient; Biosensor surfaces; Dissociation rate coefficient; Dual-fractal analysis; Mautner model; Nanoscale sensors; Pfeifer’s fractal binding rate theory; Single-fractal analysis; Trapped diffusion; Triple-fractal analysis; Variable rate coefficient 2.1. Introduction
In a biosensor-based assay, the molecule to be detected (analyte) is present in solution and the appropriate receptor is immobilized on a solid surface. The interaction between the analyte and the receptor on the solid biosensor surface is detected either by a change in the refractive index (in surface plasmon resonance (SPR) instruments) or by changes in the fluorometric intensity, ultraviolet light intensity, etc. The SPR biosensor protocol analyzes the binding (and dissociation where applicable) kinetic curves using classical saturation models involving analyte–receptor binding using 1:1, 1:2, etc. ratios, generally under diffusion-free conditions and assuming that the receptors are homogeneously distributed over the sensor surface. Computer programs and software that come with the equipment provide values of the binding (and the dissociation) rate coefficients. Though a careful analysis and experimental protocol may eliminate or minimize the influence of diffusional limitations; realistically speaking, it is more appropriate to include a heterogeneous distribution on the sensing surface. Heterogeneity on the sensing surface and in the biosensor systems itself may be due to other reasons, such as nonspecific binding, inherent irregularities on the sensing surface, mixture of receptors on the surface, and mixture of analytes in solution which includes the analyte of interest. Two factors need to be addressed while analyzing the analyte–receptor binding and dissociation kinetics. The system by its design is heterogeneous. For example, as indicated above, the receptors immobilized on the biosensor surface may exhibit some heterogeneity, that is, surface roughness. No matter how careful one is in immobilizing the receptors on the biosensor surface, there will be some degree of heterogeneity on the surface. Henke et al. (2002) have used the atomic force microscopy technique to determine the effects of cleaning fused silica and glass on surface roughness. This is for biosensor use. Note that prior to the immobilization of receptors on the surface, the surface needs to be cleaned to remove contaminants, and to create surface attachment sites for example, for hydroxyl groups. For the analyte–receptor binding (and dissociation) to take place the analyte, by the diffusion process, must come within the “proximity” of the active site on the receptor. Mass transport limitations may be minimized or eliminated if the system is either properly designed or properly operated or both. In most cases, however, both diffusional effects and heterogeneity aspects will be present in biosensor systems, and their influence on binding and dissociation kinetics need to be determined. Ideally, one would like to determine the influence of each of these separately on the binding and dissociation kinetics. In the theoretical analysis to be presented below, (the Havlin, 1989; analysis) the effects of diffusion and heterogeneity are presented coupled together. One possible way of accounting for the presence of diffusional limitations and the heterogeneity that exists on the surface is by using fractals. Ideally, and as indicated above, one would prefer to decouple the influence of diffusion and heterogeneity. Presumably, an approach other than fractal analysis is required to decouple these two effects. A characteristic feature of fractals is self-similarity at different levels of the scale. Fractals exhibit dilatational symmetry. Fractals are disordered systems, and the disorder is described by nonintegral dimensions (Pfeifer and Obert, 1989). Fractals have nonintegral dimensions, and are smaller than the dimension they are embedded in. In other words, the highest value that a fractal can have is three. In our case, an increase in the degree of heterogeneity on the biosensor surface would lead to an increase in the value of the fractal dimension. Another way of looking at the fractal dimension is its “space filling” capacity. The more the space a surface fills, the higher is its fractal dimension. The fractal dimension cannot have a negative value, and very low values of the fractal dimension on the surface indicate that the surface exists as a Cantor-like dust. Kopelman (1988) points out that surface-diffusion-controlled reactions that occur on clusters or islands are expected to exhibit anomalous and fractal-like kinetics. These kinetics exhibit anomalous reaction orders and time-dependent (e.g., binding) rate coefficients. As long as surface irregularities show scale invariance they can be characterized by a single number, the fractal dimension. Later on in this book we will characterize the surfaces of the biosensors used in different examples by a fractal dimension. More specifically, we will characterize the heterogeneity present on these biosensor surfaces by a fractal dimension. The fractal dimension is a global property, and it is insensitive to structural or morphological details (Pajkossy and Nyikos, 1989). Markel et al. (1991) point out that fractals are scale self-similar mathematical objects that possess nontrivial geometrical properties. Furthermore, these authors state that rough surfaces, disordered layers on surfaces, and porous objects all possess fractal structure. A consequence of the fractal nature is a power-law dependence of a correlation function (in our case the analyte–receptor on the biosensor surface) on a coordinate (e.g., time). Pfeifer (1987) shows that fractals may be used to track topographical features of a surface at different levels of scale. Lee and Lee (1995) point out that the fractal approach permits a predictive approach for transport (diffusion-related) and reaction processes occurring on catalytic surfaces. This approach may presumably be extended to diffusion-limited analyte–receptor reactions occurring on biosensor surfaces. The binding of an analyte in solution to a receptor attached to a solid (albeit flow cell or biosensor surface) is a good example of a low dimension reaction system in which the distribution tends to be “less random” (Kopelman, 1988), and a fractal analysis would provide novel physical insights into the diffusion-controlled reactions occurring at the surface. Also, when too many parameters are involved in a reaction, which is the case for these analyte–receptor reactions on a solid (e.g., biosensor surface), a fractal analysis provides a useful lumped parameter. It is appropriate to pay particular care to the design of such systems and to explore new avenues by which further insight or knowledge may be obtained on these biosensor systems. The fractal approach is not new and has been used previously in analyzing different phenomena on lipid membranes. Fatin-Rouge et al. (2004) have recently presented a summary of cases where the analysis of diffusion properties in random media has provoked significant theoretical and experimental interest. These cases include soils (Sahimi, 1993), gels (Starchev et al., 1997; Pluen et al., 1999), bacterial cytoplasm (Berland et al., 1995; Schwille et al., 1999), membranes (Saffman and Delbruck, 1975; Peters and Cherry, 1982; Ghosh and Webb, 1988), and channels (Wei et al., 2000). Coppens and Froment (1995) have analyzed the geometrical aspects of diffusion and the reaction occurring in a fractal catalyst pore. In this chapter, and in this book as a whole, we are extending the analysis to analyte–receptor binding (and dissociation) on biosensor surfaces. Fatin-Rouge et al. (2004) show that in most real systems disorder may exist over a finite range of distances. Harder et al. (1987) and Havlin (1989) point out that in this range the diffusion process cannot be characterized by the classical Fick's law. In this range, anomalous diffusion applies. Fatin-Rouge et al. (2004) emphasize that at larger distances than in the above window range, the effects of disorder on diffusion may be very small due to statistical effects, and may cancel each...