Deciphering the Drug Synergy in Pharmacological Rejuvenation of Mesenchymal Stromal Cells

解读间充质基质细胞药理再生中的药物协同作用

• 批准号: 2244760
• 负责人: Neil Lin
• 金额: $ 58.59万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2244760&HistoricalAwards=false
• 关键词: Deciphering; Drug; Synergy; Pharmacological; Rejuvenation

Cell rejuvenation can be achieved, in principle. The process involves interconnected biological processes. The major challenge is to understand which drugs can stimulate and coordinate these processes. The mechanisms of stimulation and regulation must also be discovered and described. Artificial intelligence (AI) will be utilized to identify effective drug combinations and understand their working mechanisms. The initial objective is to delay aging in lab-grown mesenchymal stromal cells (MSCs). Specifically, the research team will decipher the metabolic responses to combinations of rejuvenating drugs. To promote diversity in engineering, the research team will advise underrepresented undergraduates both in project-based courses and in research labs. This project integrates tissue engineering, drug screening, and machine learning. The aim is to develop a pharmacological approach for mitigating the in vitro senescence progression in MSCs. MSCs hold great promise for treating many currently uncurable diseases, but their therapeutic function decays rapidly during expansion. Identifying the appropriate pharmacologic factors that effectively mitigates senescence remains challenging. A “combination therapy” approach for reducing MSC senescence will be pursued and evaluated. This will entail deciphering the nonlinear and high-dimensional relationship between the major senescence signaling pathways (i.e., mTOR, EGF, FGF, PDGF, and insulin). The research team will first utilize AI to identify the combination of pharmacological factors that reduces senescence. Subsequently, transcriptome analyses of the rejuvenated MSCs will be performed to identify how the induced transcriptional response mitigates senescence. By performing a targeted drug screening that perturbs the AI-predicted factor combination, the research team will identify the underlying drug synergism that amplifies the efficacy of the “combination therapy”. The outcome sought it to identify an effective rejuvenation trajectory that can be reproducibly induced by perturbing the cell culture medium supplements.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.

原则上可以实现细胞再生。这个过程涉及相互关联的生物过程。主要的挑战是了解哪些药物可以刺激和协调这些过程。刺激和调节的机制也必须被发现和描述。人工智能（AI）将用于识别有效的药物组合并了解其工作机制。最初的目标是延缓实验室生长的间充质基质细胞（MSC）的衰老。 具体来说，研究小组将破译对恢复活力药物组合的代谢反应。为了促进工程的多样性，研究团队将在基于项目的课程和研究实验室中为代表性不足的本科生提供建议。该项目整合了组织工程、药物筛选和机器学习。目的是开发一种用于减轻MSC体外衰老进展的药理学方法。MSCs在治疗许多目前无法治愈的疾病方面有很大的希望，但它们的治疗功能在扩张过程中迅速衰减。确定有效缓解衰老的适当药理学因素仍然具有挑战性。一个“联合治疗”的方法，以减少MSC衰老将追求和评估。这将需要破译主要衰老信号通路之间的非线性和高维关系（即，mTOR、EGF、FGF、PDGF和胰岛素）。研究小组将首先利用人工智能来确定减少衰老的药理学因素的组合。随后，将对再生的MSC进行转录组分析，以确定诱导的转录反应如何减轻衰老。通过进行干扰AI预测因子组合的靶向药物筛选，研究团队将确定放大“联合疗法”疗效的潜在药物协同作用。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。