Research Project 1 will develop in silico models that predict PD progression and inform clinical trial design

研究项目 1 将开发预测 PD 进展并为临床试验设计提供信息的计算机模型

• 批准号: 10242056
• 负责人: KARL D. KIEBURTZ
• 金额: $ 17.73万
• 依托单位: UNIVERSITY OF ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-30 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10242056
• 关键词: Adopted; Algorithms; Area; Biological; Clinical; Clinical Trials; Clinical Trials Design; Cognitive; Collaborations; Collection; Critical Pathways; Data; Databases; Deterioration; Development; Disease; Disease Progression; Drug Industry; Genetic; Health; Individual; Laboratories; Mathematics; Measurable; Modeling; Modernization; Motor; Neurology; Outcome; Parkinson Disease; Patients; Progressive Disease; Published Comment; Publishing; Research; Research Methodology; Research Personnel; Research Project Grants; Sample Size; Time; United States Food and Drug Administration; United States National Institutes of Health; Universities; Work; care outcomes; cohort; drug development; evidence base; experience; genetic signature; high risk; improved; in silico; machine learning method; models and simulation; neurogenetics; novel; novel therapeutics; predictive modeling; prognostic signature; simulation; therapeutic development; tool

SUMMARY Disease progression modeling uses mathematical functions and data-driven algorithms to quantitatively describe and predict the time course of progressive disease. In 2004, the United States Food and Drug Administration (FDA) launched its Critical Path Initiative, which aims to modernize drug development. In this initiative, disease progression modeling was promoted as a key opportunity for the advancement of novel therapeutics. Since that time, disease progression modeling has been widely adopted by the pharmaceutical industry and regulatory agencies. However, the application of disease progression modeling for PD is at a nascent stage. Direct evidence demonstrating that disease progression modeling and clinical trial simulations will improve PD research, care, and outcomes is limited. For example, the common notion that the identification of specific types of patients (e.g., those at high-risk for specific progression rates) will lead to clinical trials being conducted with greater efficiency cannot be assumed. This lack of evidence strongly supports the need for more research on modeling and simulation tools in relation to PD research, care and outcomes. The expected outcomes of the efforts, networks, and platforms developed from this research project are modeling and simulation tools, and rapidly published studies to form the evidence base supporting the use of such tools in PD research.

总结 疾病进展建模使用数学函数和数据驱动的算法来定量地 描述和预测疾病进展的时间进程。2004年，美国食品和药物管理局 美国食品和药物管理局（FDA）推出了其关键路径倡议，旨在实现药物开发的现代化。在这 在这项倡议中，疾病进展建模被认为是促进新的 治疗学从那时起，疾病进展建模已被制药公司广泛采用。 行业和监管机构。然而，疾病进展模型在PD中的应用处于一个新的阶段。 初生阶段直接证据表明，疾病进展建模和临床试验模拟 将改善PD研究，护理和结果是有限的。例如，通常认为， 识别特定类型的患者（例如，特定进展率的高风险患者）将导致 不能假定临床试验正在以更高的效率进行。证据的缺乏 支持需要更多的研究建模和仿真工具有关的PD研究，护理和 结果。从本研究项目中开发的努力、网络和平台的预期成果 是建模和模拟工具，并迅速发表研究，以形成支持使用的证据基础 在PD研究中使用这些工具。