Many human diseases-if not all human diseases-and human health appear to be linked to our genetics. Major government and private research efforts are focused on investigating this linkage. Unfortunately, one of our best tools for this critical work-i.e., microarrays-holds us back, as most provide only generally qualitative results, limiting their usefulness. One key example is single nucleotide polymorphism (SNP) detection. Current SNP analysis is not quantitative because of imperfect molecular recognition (cross-hybridization) and pseudoequilibrium analyses performed with microarrays, limiting their use (e.g., to preliminary screening, as with the Affymetrix SNPChip). The microarray techniques attempt to compensate via excessive redundancy, leading to massive quantities of inaccurate/irreproducible data. The need to sort through these copious amounts of data extends analysis time, leads to improper conclusions, initiates scientific controversy, and may misdirect diagnostic and pharmaceutical research. Fortunately, these outcomes can be improved upon-which is the primary goal of the multi-phase STTR Fast-Track project proposed here. One key task that requires next-generation tools for real-time, reliable, quantitative SNPs analysis-including new data-management/analysis capabilities and simple yet powerful chemistries-is heteroplasmy (semiquantitative mitochondrial DNA SNP assays). Heteroplasmy could fulfill a major unmet need for reliable/costefficient quantitative tests involving cancer, diabetes, Alzheimer's disease, hypertension and a variety of neuromuscular, neurodegenerative, and metabolic diseases. Our goal is to develop, prototype, and commercialize real-time and quantitative heteroplasmy research tools based on technology licensed from the U of Utah and developed by Sigma founders. Preliminary data using our proprietary """"""""competitive displacement analysis"""""""" (CDA)-the key innovation-strongly indicates the potential for successful development and rapid commercialization under this Fast-Track project. Specifically, we propose to develop our new method of performing real-time SNP microarray analysis (via CDA) by monitoring non-linear binding kinetics of known competitors in the presence of unlabeled targets. Our Phase I Aims are to 1) Demonstrate relevant binding kinetics;2) Prove/validate feasibility of using CDA for heteroplasmy-based SNP detection in a model system;and 3) Develop CDA-based analytical approaches for multi-component model systems, based on synthetic targets, competitors, and lower-affinity species (background). Meeting the key Phase I milestones will allow us to pursue Phase II Aims: 4) Characterize CDA for heteroplasmy analysis on A3243G mutation locus;5) Scale up CDA to interrogate multiple mutations;and 6) Complete comparative validation of CDA versus reference methods. Phase II success will provide the data needed to attract """"""""Phase III"""""""" industry and financial partners. This will facilitate rapid product introduction into a key niche in a multi-billion-dollar research/diagnostics industry that benefits human health worldwide.
Many of the diseases that kill the majority of Americans and millions worldwide are linked to our genetic makeups. Unfortunately, although we have made important progress in the mapping of common human genetic variations, we still need much better real-time, quantitative tools for making the best possible use of that information in terms of disease research, diagnostics, and therapeutics. The overall goal of this Fast-Track STTR project is to develop, validate, and commercialize Sigma?s proprietary ?competitive displacement analysis? (CDA) technology for next-generation heteroplasmy testing to meet the needs of human health researchers and to address a key niche in a multi-billion-dollar industry.
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