The incidence in the United States of metabolic disease resulting from inborn errors of metabolism (IEM) is estimated to be up to 1 in 3500 infants, and the impact on families where diseases are undetected in newborns can be devastating. Although the benefits of newborn screening for such diseases has been demonstrated, technical challenges are limiting their broader application. Two specific challenges have been identified by the American College of Medical Genetics, that could significantly improve newborn screening, are i) the discovery of new biomarker tests for IEM diseases for which tests are currently nonexistent and ii) the improvement of biomarker screening for current tests that have high false-positive rates. To address these two challenges, we propose to leverage the full range of metabolite measurements that are currently available from high-throughput data acquisition methods and predict biomarker signatures that are superior to single biomarker screens using our proprietary computational in silico metabolic modeling platform. Classical development of new screens has been data-driven, requiring hundreds of thousands of patient data points for a statistical analysis. This top-down approach has led to the two shortcomings mentioned. Our computational platform offers a mechanistically-based calculation of biomarkers using a bottom-up pathway-based approach to reconstruct the full metabolic content of human cells and then determine the functional and physiological impacts of IEM diseases. Using this approach, we can directly calculate multiple candidate metabolite biomarkers in human biofluids that change with a given IEM disease and predict entire disease biomarker signatures. In our Phase I effort, we developed the computational models and methods needed to predict biomarker signatures for a subset of IEM diseases and produced extremely promising results (approximately 90% accuracy in predicting known biomarkers for the collected set of diseases). We now propose in a Phase II effort, to i) expand the in silico model we currently have of the human hepatocyte metabolism to increase its scope and application to IEM diseases, ii.) advance and validate the biomarker signature computational algorithm to increase its accuracy with focused enhancements, and iii.) generate new biomarker signatures for targeted IEM diseases and utilize retrospective and prospective data to confirm the new biomarker signatures. These validated biomarker signatures will then be commercialized through partnerships with commercial laboratories currently performing newborn screening and/or with vendors of the measurement equipment. Success in generating new biomarker signatures for diagnostic screens is supported by our team of scientists who have been working in the field of metabolic modeling for over a decade, as well as our scientific, clinical, and commercial contractors. The developed biomarker platform of this Phase II program also has significant implications in the areas of identification and validation of biomarkers for cancer (and resulting products for use as diagnostics, therapy selection, and monitoring aids), toxicology and safety testing, and drug discovery

Public Health Relevance

Newborn inborn errors of metabolism screening is commonly performed in all states and despite efforts to standardize and expand the scope of important diseases in these screens, there remain diseases that have inadequate tests or no existing test altogether. Building off of our promising Phase I results, we proposed to utilize our computational biomarker identification platform to mechanistically discover new and improved tests for these diseases that can be directly used under current practices for diagnostic screening.

National Institute of Health (NIH)
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Small Business Innovation Research Grants (SBIR) - Phase II (R44)
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Special Emphasis Panel (ZRG1-IMST-H (14))
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Sechi, Salvatore
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Gt Life Sciences, Inc.
San Diego
United States
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