Research Project
8898959
? DESCRIPTION (provided by applicant): Neonatal sepsis is a serious public health concern afflicting 0.7% of all live births and accounts for nearly 50% of all neonate mortalities. In neonatal sepsis early targeted treatment is life-saving, and considering the rate at which the disease develops, is perhaps the most crucial factor to improving outcomes. Unfortunately, given the time-consuming diagnostic process today, the underlying cause of this disease is typically identified too late. Current diagnostics do not facilitate an early initiation of a targeed first-line antimicrobial intervention- as they require culturing, which requires as long as 2-5 day for accurate identification of the infecting pathogen. Hence current treatment protocols employ empirically designed antibiotic 'cocktails' which are not only less effective, but likewise increas complications, and significantly increase the prevalence of drug resistant pathogens. This proposal will remove the 'culturing barrier' by demonstrating the feasibility of classifying the most clinically prevalent pathogens directly from phlebotomy samples in an automated manner. This diagnostic will be the first to leverage the innovative use of a new synthetic nucleic acid analogue which has ability to invade double- stranded DNA enabling the development of a rapid diagnostic assay with unparalleled sensitivity and accuracy. Our innovative diagnostic approach would enable a significantly faster, more sensitive, and more accurate method of identifying neonatal sepsis. The diagnostic we propose will have profound impact, both by improving outcomes by providing a means to develop a hypothesis driven first-line antimicrobial intervention, and likewise by reducing the use of excessive and unnecessary antimicrobials in infants. To succeed in this Phase I we have put together a top-notch team at HelixBind, including experts in assay development, application of synthetic nucleic acid analogues, in the design and implementation of sample-to- answer automated diagnostics, and in biomedical instrumentation development. Additionally we have enlisted key strategic advisors in clinical microbiology, surface chemistry, and nucleic acid analogues as well as successful entrepreneurs with experience in the development and commercialization of medical devices. Together, we will build upon our already impressive preliminary data and demonstrate the feasibility of our diagnostic. To validate our approach, we will establish an automated 'sample-to-answer' diagnostic employing synthetic nucleic acid analogues, capable of classifying the most prevalent pathogens directly from human whole-blood at clinically relevant load levels. Having achieved our Phase I goals, in Phase II we will develop a fully-integrated prototype, and address all issues such as sample injection, deployable instrumentation, and commercial format.
