SBIR-STTR Award

With the advancements in high throughput technologies and a growing volume of clinical data that is becoming available through public initiatives and within biopharmaceutical companies, there is an immediate and imperative need for developing computational tools to analyze and understand this data and with the ultimate aim to predict the whole body response to environmental and genetic changes. To develop a comprehensive platform for modeling human metabolism, an integrated dynamic and steady state framework is required for modeling metabolism at the whole-body and intracellular level. To understand and characterize such complex biological systems, computational models have been developed that fall generally within two categories, top-down or bottom-up models. While these approaches, by themselves, do not provide an accurate representation of complex dynamic biological systems, in this proposal, we seek to integrate the top-down and bottom-up approach to simulate the dynamic human physiological conditions by development of a hybrid dynamic/steady state-framework. We have developed an integrated kinetic FBA modeling framework that allows for the integration of clinical data with intracellular metabolic network analysis. This approach utilizes kinetic rate equation parameters and input nutrient concentrations to calculate dynamic intracellular metabolite concentrations. Using the software technology platform SimPhenyTM, we have reconstructed metabolic networks for hepatocyte, adipocyte and myocyte. In addition, the development of an integrated model of hepatocyte and adipocyte was used to characterize in distinct physiological conditions and resulted in the generation of testable hypotheses that can be investigated experimentally. The assessment of the scientific and computational feasibility of developing an integrated kinetic FBA approach and application to a multi-cell model framework will be an important step towards a comprehensive metabolic modeling platform in human disease research and drug development.

Public Health Relevance:
With the advancements in high throughput technologies and a growing volume of clinical data that is becoming available through public initiatives and within biopharmaceutical companies, there is an immediate and imperative need for developing computational tools to analyze and understand this data and with the ultimate aim to predict the whole body response to environmental and genetic changes. In this proposal, we seek to simulate the dynamic human physiological conditions by development of a hybrid dynamic/steady state-framework. The assessment of the scientific and computational feasibility of developing an integrated kinetic Flux Balance Analysis (FBA) approach and application to a multi-cell model framework will be an important step towards a comprehensive metabolic modeling platform in human disease research and drug development.

Public Health Relevance:
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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.

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0-6 Weeks Old; 1-Propanaminium, 3-Carboxy-2-Hydroxy-N,N,N-Trimethyl-, Inner Salt, (R)-; Address; Adopted; Agreement; Algorithms; American; Amino Acid Channel; Amino Acid Transport Systems; Amino Acid Transporter; Area; Assay; Bioassay; Biochemical; Biologic Assays; Biologic Sciences; Biological Assay; Biological Sciences; Blood; Body Tissues; California; Cancers; Carnitine; Cell Function; Cell Process; Cell Physiology; Cells; Cellular Function; Cellular Physiology; Cellular Process; Characteristics; Clinical; Clinical Data; Clinical Protocols; Collaborations; Complex; Computational Algorithm; Computer Programs; Computer Simulation; Computer Software; Computerized Models; Contractor; Costs And Benefits; Data; Data Set; Dataset; Detection; Development; Diagnosis; Diagnostic; Diagnostic Tests; Disease; Disorder; Drug Toxicity; Equipment; Family; Future; Generalized Growth; Generations; Genetic; Genetic Diseases, Inborn; Genome; Goals; Growth; Hosp; Hepatic Cells; Hepatic Parenchymal Cell; Hepatocyte; Hereditary Metabolic Disorder; Hospitals; Human; Human, General; Inborn Errors Of Metabolism; Inborn Genetic Diseases; Incidence; Individual; Infant; Infant, Newborn; Inherited Disorder; Intermediary Metabolism; Investigators; Knowledge; L-Phenylalanine; Laboratories; Life Sciences; Liver Cells; Metbl; Malignant Neoplasms; Malignant Tumor; Man (Taxonomy); Man, Modern; Mass Spectrum; Mass Spectrum Analysis; Mathematical Model Simulation; Mathematical Models And Simulations; Measurement; Measures; Medical Genetics; Medical Center; Medicine; Metabolic; Metabolic Diseases; Metabolic Disorder; Metabolic Pathway; Metabolic Processes; Metabolism; Metabolism, Inborn Errors; Methods; Modeling; Models, Computer; Monitor; Neonatal Screening; Newborn Infant; Newborn Infant Screening; Newborns; Outcome; Outcome Measure; Pathway Interactions; Patients; Phase; Phenylalanine; Phenylalanine, L-Isomer; Photometry/Spectrum Analysis, Mass; Physiologic; Physiological; Process; Programs (Pt); Programs [publication Type]; Prospective Studies; Proteomics; Public Health; R01 Mechanism; R01 Program; Rpg; Research; Research Grants; Research Personnel; Research Project Grants; Research Projects; Research Projects, R-Series; Research Resources; Researchers; Resources; Reticuloendothelial System, Blood; Running; Sbir; Sbirs (R43/44); Sampling; Science Of Medicine; Scientist; Screening Procedure; Simulation, Computer Based; Small Business Innovation Research; Small Business Innovation Research Grant; Software; Spectrometry, Mass; Spectroscopy, Mass; Spectrum Analyses, Mass; Spectrum Analysis, Mass; Spottings; Subcellular Process; System; System, Loinc Axis 4; Systems Analyses; Systems Analysis; Technology; Testing; Thesaurismosis; Tissue Growth; Tissues; Toxicology; Translating; Translatings; United States; Update; Validation; Vendor; Work; Base; Biomarker; Cancer Diagnosis; Clinical Diagnosis; Clinical Practice; Clinical Relevance; Clinically Relevant; College; Computational Modeling; Computational Models; Computational Simulation; Computer Based Models; Computer Program/Software; Computerized Modeling; Computerized Simulation; Data Acquisition; Disease/Disorder; Drug Discovery; High Throughput Technology; Improved; In Silico; Inborn Error; Inborn Metabolism Disorder; Language Translation; Malignancy; Measurement Of Metabolism; Meetings; Metabolism Disorder; Metabolomics; Neonate; Neoplasm/Cancer; Newborn Human (0-6 Weeks); Newborn Screening; Novel; Ontogeny; Pathway; Programs; Prospective; Public Health Medicine (Field); Public Health Relevance; Safety Testing; Screening; Screenings; Success; Tandem Mass Spectrometry; Tool; Transcriptomics; Virtual Simulation