With relevance spanning from disease diagnostics such as immunohistochemistry to immunoassays and therapeutics, antibody reagents play a critical role in the life sciences, clinical chemistry, and clinical medicine. Approaches such as immunohistochemistry (IHC), and immunoassays, all work by exploiting the principle that antibodies bind specifically to antigens of interest. IHC uses antibodies to detect antigens on cancerous tissue, and immunoassays use antibodies to identify biomarkers both for a myriad of disease diagnostics for cancer and infections diseases. Currently, antibody-based proteomic approaches such as immunohistochemistry and technologies that serve to diagnose and advance therapeutics are severely limited due to non-specific antibody binding, low specificity, and reproducibility issues. Selecting antibodies based on their antigen binding kinetic properties, such as their association and dissociation rate constants, kon and koff, can provide a quantitative metric that can further optimize and validate immunoreagent selection. These rate constants quantify the ability for an antibody to associate (bind) or dissociate (unbind) to a target analyte and determines inherent binding strength. Therefore a metric such as this has the power to eliminate problems seen by clinicians, researchers, and drug developers alike in regards to false positive, false negatives, and problems with reproducibility seen in antibody-based approaches and inform assay design. Consequently, scalable and efficient analytical tools for informed selection of reliable antibody reagents would have wide impact. In this work, I have developed a highly scalable, rapid, microfluidic screening assay, that is able to assess important but difficult to characterize interaction kinetics for antibodies and protein-protein interactions. This work includes a novel screening assay for quantitative characterization of binding kinetics for the development of new biomarker discovery, disease diagnostics, and novel therapies and advances antibody-based proteomics.
