Biomarker and Diagnostic Discovery for Inborn Errors of Metabolism

先天性代谢缺陷的生物标志物和诊断发现

• 批准号: 8049047
• 负责人: IMAN FAMILI
• 金额: $ 100.55万
• 依托单位: GT LIFE SCIENCES, INC.
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2013-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8049047
• 关键词: Address; Adopted; Agreement; Algorithms; American; Amino Acid Transporter; Area; Biochemical; Biological Assay; Biological Markers; Biological Sciences; Blood; California; Carnitine; Cell physiology; Cells; Characteristics; Clinical; Clinical Data; Clinical Protocols; Collaborations; Complex; Computational algorithm; Computer Simulation; Computer software; Computing Methodologies; Contractor; Costs and Benefits; Data; Data Set; Detection; Development; Diagnosis; Diagnostic; Diagnostic tests; Disease; Drug toxicity; Equipment; Family; Future; Generations; Genetic; Genome; Goals; Growth; Hepatocyte; Hospitals; Human; Inborn Errors of Metabolism; Inborn Genetic Diseases; Incidence; Individual; Infant; Knowledge; Laboratories; Malignant Neoplasms; Mass Spectrum Analysis; Measurement; Measures; Medical Genetics; Medical center; Medicine; Metabolic; Metabolic Diseases; Metabolic Pathway; Metabolism; Methods; Modeling; Monitor; Neonatal Screening; Newborn Infant; Outcome; Outcome Measure; Pathway interactions; Patients; Phase; Phenylalanine; Phenylketonurias; Physiological; Process; Prospective Studies; Proteomics; Public Health; Research; Research Personnel; Research Project Grants; Resources; Retrospective Studies; Running; Sampling; Scientist; Screening procedure; Small Business Innovation Research Grant; Spottings; System; Systems Analysis; Technology; Testing; Tissues; Toxicology; Translating; United States; Update; Validation; Vendor; Work; base; cancer diagnosis; clinical Diagnosis; clinical practice; clinically relevant; college; data acquisition; drug discovery; high throughput technology; improved; meetings; metabolomics; neonate; novel; programs; prospective; safety testing; success; tandem mass spectrometry; tool; transcriptomics

DESCRIPTION (provided by applicant): 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

描述（由申请人提供）：在美国，由先天性代谢缺陷（IEM）引起的代谢性疾病的发病率估计高达1/3500婴儿，对新生儿未发现疾病的家庭的影响可能是毁灭性的。虽然新生儿筛查这些疾病的好处已经得到证明，但技术挑战限制了其更广泛的应用。美国医学遗传学学院已经确定了两个具体的挑战，可以显着改善新生儿筛查，是i）发现新的生物标志物检测IEM疾病的测试是目前不存在的，ii）改善生物标志物筛查的当前测试具有高假阳性率。为了解决这两个挑战，我们建议利用目前可从高通量数据采集方法获得的全方位代谢物测量，并使用我们专有的计算机模拟代谢建模平台预测上级单一生物标志物筛选的生物标志物特征。新屏幕的经典开发一直是数据驱动的，需要数十万个患者数据点进行统计分析。这种自上而下的方法导致了上述两个缺点。我们的计算平台提供了一种基于机械的生物标志物计算，使用自下而上的基于路径的方法来重建人体细胞的全部代谢内容，然后确定IEM疾病的功能和生理影响。使用这种方法，我们可以直接计算人类生物流体中随给定IEM疾病而变化的多种候选代谢物生物标志物，并预测整个疾病生物标志物特征。在我们的第一阶段工作中，我们开发了预测IEM疾病子集的生物标志物特征所需的计算模型和方法，并产生了非常有希望的结果（预测收集的疾病集的已知生物标志物的准确率约为90%）。我们现在在第二阶段的努力中提出，i）扩展我们目前拥有的人类肝细胞代谢的计算机模型，以增加其对IEM疾病的范围和应用，ii）推进和验证生物标志物签名计算算法以通过集中增强来增加其准确性，以及iii.）生成针对目标IEM疾病的新生物标志物特征，并利用回顾性和前瞻性数据来确认新生物标志物特征。这些经过验证的生物标志物特征将通过与目前进行新生儿筛查的商业实验室和/或测量设备供应商的合作伙伴关系进行商业化。我们在代谢建模领域工作了十多年的科学家团队，以及我们的科学，临床和商业承包商，都支持我们成功地为诊断筛选生成新的生物标志物特征。该II期项目开发的生物标志物平台在癌症生物标志物的鉴定和验证（以及用作诊断、治疗选择和监测辅助工具的所得产品）、毒理学和安全性测试以及药物发现领域也具有重要意义。