Method for Prediction of Efficacy of Genetic-Based Prediction Models of Personali

基于遗传的个人预测模型的功效预测方法

• 批准号: 7726391
• 负责人: Peter J. Tonellato
• 金额: $ 33.9万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-01 至 2011-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7726391
• 关键词: Algorithms; Classification; Clinical; Clinical Research; Clinical Trials; Collection; Computerized Medical Record; Data; Devices; Dose; Environment; Evaluation; Expenditure; Genetic; Genetic screening method; Genotype; Healthcare; Individual; Knowledge; Label; Malignant Neoplasms; Medical; Medical Records; Medicine; Methodology; Methods; Modeling; Patients; Pharmacogenetics; Predictive Value; Preventive; Process; Publishing; Research; Research Infrastructure; Review Literature; Risk; Series; Technology; Testing; Therapeutic; Translating; Translations; Validation; Warfarin; Work; base; biomedical informatics; clinical efficacy; design; genetic analysis; improved; research study; simulation; tool; trial comparing

DESCRIPTION (provided by applicant): The collection of newly discovered clinically valuable genetic tests is far outpacing our ability to use clinical trials to determine their clinical efficacy and determine which of the collection of tests and associated algorithms are best suited for any given clinical setting. For example, a review of the literature demonstrates that at least 35 published algorithms for the prediction of therapeutic warfarin dosing have been published in the past ten years, nine of which include genotype data (unpublished review). Expenditures to determine the most clinical useful of these algorithms include a total of 19 clinical trials (clinicaltrials.gov, accessed Feb, 2008) Compounding this complexity and adding to the delay of the successful medical use of the rapidly expanding collection of clinically valuable genetic discoveries is the lack of clinical and biomedical informatic methods, tools and infrastructure required to facilitate the successful translation of the discoveries to practical clinical use. Efforts to translate important biomedical informatics methods, tools and processes required to implement important new genetic discoveries in the clinical setting are severely hindered by regulatory, technical and validation barriers not easily resolved in the current clinical-research or clinical enterprise environments. This proposal will test an environment and methodology that creates clinical avatars with data statistically consistent with actual patient electronic medical records. Once created, these clinical avatar medical records will be used to conduct insilico experiments to compare genetic- based algorithms and their predicted value in the clinical setting. We will develop and test the methodology and create the applications by conducting a series of clinical avatar simulations and subsequent analysis of genetic-based warfarin dosing prediction. Once developed, we will conduct a series of insilico clinical trials comparing various clinical and genetic factors to demonstrate the efficacy of the warfarin dosing algorithms. This work is representative of similar projects that may be designed to test other personalized medicine devices such as genetic tests used to quantify risk to cancers, pharmacogenetic problems, and other FDA labeled "IVDMIA" devices (e.g. Genetic Health's Oncotype DX(R)) projected to improve predictive and preventive personalized medicine.

描述（由申请人提供）：新发现的有临床价值的基因检测的收集远远超过了我们使用临床试验来确定其临床疗效的能力，并确定哪些测试集合和相关算法最适合任何给定的临床环境。例如，一篇文献综述表明，在过去十年中，至少有35篇已发表的用于预测治疗性华法林剂量的算法，其中9篇包括基因型数据（未发表的综述）。用于确定这些算法最具临床用途的支出包括总共19项临床试验（clinicaltrials.gov， 2008年2月访问）。由于缺乏促进将这些发现成功转化为实际临床使用所需的临床和生物医学信息学方法、工具和基础设施，使这种复杂性更加复杂，并使迅速扩大的临床有价值的基因发现的收集在医疗上的成功应用更加延迟。将重要的生物医学信息学方法、工具和流程转化为在临床环境中实施重要的新基因发现所需要的努力，受到当前临床研究或临床企业环境中难以解决的监管、技术和验证障碍的严重阻碍。该提案将测试一种环境和方法，该环境和方法创建具有统计数据的临床化身，与实际患者的电子医疗记录一致。一旦创建，这些临床化身医疗记录将用于进行计算机实验，以比较基于遗传的算法及其在临床环境中的预测值。我们将开发和测试方法，并通过进行一系列临床化身模拟和随后的基于基因的华法林剂量预测分析来创建应用程序。一旦开发出来，我们将进行一系列的计算机临床试验，比较各种临床和遗传因素，以证明华法林给药算法的有效性。这项工作是类似项目的代表，这些项目可能被设计用于测试其他个性化医疗设备，如用于量化癌症风险的基因测试，药物遗传学问题，以及其他FDA标记的“IVDMIA”设备（例如遗传健康的Oncotype DX(R)），旨在改善预测性和预防性个性化医疗。