Workshop on Drug Repurposing for Future Pandemics

未来大流行药物再利用研讨会

基本信息

  • 批准号:
    2033384
  • 负责人:
  • 金额:
    $ 3万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2020
  • 资助国家:
    美国
  • 起止时间:
    2020-06-15 至 2020-11-30
  • 项目状态:
    已结题

项目摘要

Developing a drug to treat a new disease, moving the drug forward through clinical trials, and obtaining approval is a long, expensive process with a high risk of failure. An enticing alternative approach to finding a cure for a disease is drug repurposing, which reuses existing drugs, including some that failed in initial trials. Because most repurposed drugs have already passed the early phases of development and clinical testing, they get to market in 6.5 years and cost $300 million on average, less than half the time, and one-quarter of the cost needed to develop a new drug from scratch. While drug repurposing is not new, the speed at which it needs to be implemented during a pandemic is unprecedented. This is the principal open challenge that will be discussed in the workshop. This workshop will provide a forum for researchers who do active and state-of-the-art research in various aspects of drug repurposing. Participants will discuss ways to expedite drug repurposing by compressing years of work into months or even weeks through automation, data science and machine learning, novel data sources, and new biotechnology platforms. The workshop will facilitate community building by bringing together at least two distinct audiences: computational experts who develop algorithms and knowledge experts who use the algorithms as well as other tools for drug repurposing. The workshop results, including the website with linked resources, video recordings, and a perspective paper, will be disseminated broadly via public repositories. The perspective publication will be a roadmap intended for a broad audience with specific recommendations for rapid drug repurposing in future pandemics. Workshop speakers and panelists will be researchers with a wide variety of demographic backgrounds and experiences to promote diversity along several axes (gender, seniority, and affiliation).Historically, successful drug repurposing is based on serendipitous and opportunistic discoveries. However, this approach is unable to produce science-based diagnostic tools at the pandemic scale. This major problem motivates the workshop, which will be held in 2020 as a virtual two-day symposium with open registration and several hundred attendees. The workshop participants will present progress, discuss the challenges of current repurposing research, and recommend innovative ways to address these challenges and realize the full potential of drug repurposing. The workshop will cover topics related to all steps of drug repurposing, including preclinical research, clinical research, regulatory review, and post-market and safety monitoring. Bearing in mind the opportunities and challenges identified at the workshop, the organizers will put forward recommendations on how to compress current repurposing timescales into much shorter ready-to-use solutions to fight emerging pathogens. The organizers anticipate presenting a framework, context, and ultimately guidelines for accelerating the use of data-driven solutions in drug repurposing for the benefit of patients in the US.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
开发一种治疗新病的药物,推动药物通过临床试验,并获得批准,是一个漫长、昂贵的过程,失败的风险很高。寻找疾病治愈方法的一个诱人的替代方法是药物再利用,它重复使用现有的药物,包括一些在初步试验中失败的药物。由于大多数重新调整用途的药物已经通过了早期的开发和临床测试,它们需要6.5年才能上市,平均成本为3亿美元,不到从头开始开发新药所需时间的一半,以及所需成本的四分之一。虽然药物再利用并不新鲜,但在大流行期间需要实施的速度是史无前例的。这是研讨会将讨论的主要公开挑战。这个研讨会将为在药物再利用的各个方面进行积极和最先进研究的研究人员提供一个论坛。与会者将讨论如何通过自动化、数据科学和机器学习、新的数据源和新的生物技术平台将数年的工作压缩到数月甚至数周,从而加快药物再利用的速度。讲习班将通过将至少两个不同的受众聚集在一起来促进社区建设:开发算法的计算专家和使用算法以及用于药物再利用的其他工具的知识专家。讲习班的成果,包括带有链接资源的网站、录像和一份观点文件,将通过公共资料库广泛传播。这份前瞻性出版物将是一份面向广大受众的路线图,为未来大流行中的快速药物再利用提出具体建议。研讨会的演讲者和小组成员将是具有广泛的人口统计学背景和经验的研究人员,以促进沿着几个轴(性别、资历和从属关系)的多样性。从历史上看,成功的药物再利用是基于偶然和机会主义的发现。然而,这种方法无法产生大流行规模的以科学为基础的诊断工具。这一主要问题推动了研讨会的举办,研讨会将于2020年举行,作为一个虚拟的为期两天的研讨会,开放注册,有数百人参加。研讨会参与者将介绍进展,讨论当前再利用研究的挑战,并推荐应对这些挑战和实现药物再利用的全部潜力的创新方法。研讨会将涵盖与药物再利用的所有步骤相关的主题,包括临床前研究、临床研究、监管审查以及上市后和安全监测。考虑到研讨会上确定的机遇和挑战,组织者将就如何将当前重新调整用途的时间表压缩为更短的现成解决方案来对抗新出现的病原体提出建议。组织者预计将提出一个框架、背景以及最终的指导方针,以加速使用数据驱动的解决方案,以造福美国患者。该奖项反映了NSF的法定使命,并通过使用基金会的智力优势和更广泛的影响审查标准进行评估,被认为值得支持。

项目成果

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Marinka Zitnik其他文献

Digital twins as global learning health and disease models for preventive and personalized medicine
  • DOI:
    10.1186/s13073-025-01435-7
  • 发表时间:
    2025-02-07
  • 期刊:
  • 影响因子:
    11.200
  • 作者:
    Xinxiu Li;Joseph Loscalzo;A. K. M. Firoj Mahmud;Dina Mansour Aly;Andrey Rzhetsky;Marinka Zitnik;Mikael Benson
  • 通讯作者:
    Mikael Benson
Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases
用于罕见遗传疾病患者表型驱动诊断的小样本学习
  • DOI:
    10.1038/s41746-025-01749-1
  • 发表时间:
    2025-06-20
  • 期刊:
  • 影响因子:
    15.100
  • 作者:
    Emily Alsentzer;Michelle M. Li;Shilpa N. Kobren;Ayush Noori;Isaac S. Kohane;Marinka Zitnik
  • 通讯作者:
    Marinka Zitnik
AI-enabled drug discovery reaches clinical milestone
人工智能驱动的药物发现达到临床里程碑
  • DOI:
    10.1038/s41591-025-03832-2
  • 发表时间:
    2025-07-01
  • 期刊:
  • 影响因子:
    50.000
  • 作者:
    Marinka Zitnik
  • 通讯作者:
    Marinka Zitnik
Scientific discovery in the age of artificial intelligence
人工智能时代的科学发现
  • DOI:
    10.1038/s41586-023-06221-2
  • 发表时间:
    2023-08-02
  • 期刊:
  • 影响因子:
    48.500
  • 作者:
    Hanchen Wang;Tianfan Fu;Yuanqi Du;Wenhao Gao;Kexin Huang;Ziming Liu;Payal Chandak;Shengchao Liu;Peter Van Katwyk;Andreea Deac;Anima Anandkumar;Karianne Bergen;Carla P. Gomes;Shirley Ho;Pushmeet Kohli;Joan Lasenby;Jure Leskovec;Tie-Yan Liu;Arjun Manrai;Debora Marks;Bharath Ramsundar;Le Song;Jimeng Sun;Jian Tang;Petar Veličković;Max Welling;Linfeng Zhang;Connor W. Coley;Yoshua Bengio;Marinka Zitnik
  • 通讯作者:
    Marinka Zitnik
Efficient generation of protein pockets with PocketGen
使用 PocketGen 高效生成蛋白质口袋
  • DOI:
    10.1038/s42256-024-00920-9
  • 发表时间:
    2024-11-15
  • 期刊:
  • 影响因子:
    23.900
  • 作者:
    Zaixi Zhang;Wan Xiang Shen;Qi Liu;Marinka Zitnik
  • 通讯作者:
    Marinka Zitnik

Marinka Zitnik的其他文献

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{{ truncateString('Marinka Zitnik', 18)}}的其他基金

CAREER: Geometric Deep Learning to Facilitate Algorithmic and Scientific Advances in Therapeutics
职业:几何深度学习促进治疗学的算法和科学进步
  • 批准号:
    2339524
  • 财政年份:
    2024
  • 资助金额:
    $ 3万
  • 项目类别:
    Continuing Grant
RAPID:Collaborative Research: Computational Drug Repurposing for COVID-19
RAPID:合作研究:针对 COVID-19 的计算药物再利用
  • 批准号:
    2030459
  • 财政年份:
    2020
  • 资助金额:
    $ 3万
  • 项目类别:
    Standard Grant

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