I-Corps: Translation Potential of a Cell Culture Platform to Model Dynamic Drug Concentrations In Vitro

I-Corps:细胞培养平台模拟体外动态药物浓度的转化潜力

基本信息

  • 批准号:
    2405765
  • 负责人:
  • 金额:
    $ 5万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2024
  • 资助国家:
    美国
  • 起止时间:
    2024-02-15 至 2025-01-31
  • 项目状态:
    未结题

项目摘要

The broader impact of this I-Corps project is the development of an automated cell culture platform to enhance the predictive accuracy of preclinical drug testing. This innovation promises to make the process of testing potential new medicines cheaper, faster, and more effective. By mimicking the changes in drug concentration that occur in the human body, the technology can better predict safety issues and treatment effectiveness earlier in drug development. This automated system improves research efficiency, resulting in lower drug development costs, saving both drug developers and patients money.This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. The project is based on the development of an automated instrument that can precisely adjust drug concentrations in a cell culture dish over time. The technology builds on prior studies showing that cells respond differently depending on the rate of exposure. This system will be used to replicate the drug exposure of cells in the human body during the course of drug treatment. Currently, the information gained by these experiments is impossible to attain without the use of poorly predictive animal studies or expensive and time-consuming organ-on-a-chip experiments. The system promises to accelerate pharmaceutical development through enhanced productivity and predictive accuracy of early-stage drug screening.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.
I-Corps项目的更广泛影响是开发自动化细胞培养平台,以提高临床前药物测试的预测准确性。这项创新有望使潜在新药的测试过程更便宜,更快,更有效。通过模拟人体内发生的药物浓度变化,该技术可以在药物开发的早期更好地预测安全性问题和治疗效果。该自动化系统提高了研究效率,降低了药物开发成本,为药物开发人员和患者节省了资金。该I-Corps项目利用体验式学习以及对行业生态系统的第一手调查来评估该技术的转化潜力。该项目基于开发一种自动化仪器,该仪器可以随着时间的推移精确调节细胞培养皿中的药物浓度。该技术建立在先前的研究基础上,这些研究表明,细胞的反应取决于暴露率。该系统将用于在药物治疗过程中复制人体细胞的药物暴露。目前,如果不使用预测性差的动物研究或昂贵且耗时的器官芯片实验,这些实验所获得的信息是不可能获得的。该系统承诺通过提高早期药物筛选的生产率和预测准确性来加速药物开发。该奖项反映了NSF的法定使命,并通过使用基金会的知识价值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

期刊论文数量(0)
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会议论文数量(0)
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Gregor Neuert其他文献

Automated Localization and Quantification of RNA Transcripts from RNA-Fish Image Data
  • DOI:
    10.1016/j.bpj.2020.11.708
  • 发表时间:
    2021-02-12
  • 期刊:
  • 影响因子:
  • 作者:
    Blythe G. Hospelhorn;Benjamin K. Kesler;Gregor Neuert
  • 通讯作者:
    Gregor Neuert
Diverse Time-Varying Cell Stimulations Identify Predictive Signal Transduction Models
  • DOI:
    10.1016/j.bpj.2020.11.1677
  • 发表时间:
    2021-02-12
  • 期刊:
  • 影响因子:
  • 作者:
    Hossein Jashnsaz;Zachary Fox;Jason J. Hughes;Guoliang Li;Brian Munsky;Gregor Neuert
  • 通讯作者:
    Gregor Neuert
Determining Long Noncoding RNA Mechanisms in Stem Cells using Automated Image Analysis and Single Molecule RNA-Fluorescent in Situ Hybridization
  • DOI:
    10.1016/j.bpj.2020.11.1024
  • 发表时间:
    2021-02-12
  • 期刊:
  • 影响因子:
  • 作者:
    Benjamin Kesler;Gregor Neuert
  • 通讯作者:
    Gregor Neuert
Understanding the Role of the Saga Complex in Gene Expression Heterogeneity and Transcriptional Memory
  • DOI:
    10.1016/j.bpj.2020.11.1668
  • 发表时间:
    2021-02-12
  • 期刊:
  • 影响因子:
  • 作者:
    Jason J. Hughes;Gregor Neuert
  • 通讯作者:
    Gregor Neuert
Information Processing In Single Yeast Cells: Homogeneous Signal Transduction Result In Heterogeneous Gene Expression
  • DOI:
    10.1016/j.bpj.2008.12.1518
  • 发表时间:
    2009-02-01
  • 期刊:
  • 影响因子:
  • 作者:
    Gregor Neuert;Alexander van Oudenaarden
  • 通讯作者:
    Alexander van Oudenaarden

Gregor Neuert的其他文献

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