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修订。该过程涉及互连的生物过程。主要的挑战是了解哪些药物可以刺激和协调这些过程。还必须发现和描述刺激和调节的机制。人工智能（AI）将用于确定有效的药物组合并了解其工作机制。最初的目标是延迟实验室生长的间充质基质细胞（MSC）的衰老。具体而言，研究小组将破译对复兴药物组合的代谢反应。为了促进工程学的多样性，研究小组将在基于项目的课程和研究实验室中的代表性不足的本科生建议。该项目集成了组织工程，药物筛查和机器学习。目的是开发一种药理方法来减轻MSC中体外感应进展。 MSC对治疗许多目前无法保育的疾病的良好有望，但是在扩张期间，其治疗功能迅速衰减。确定有效缓解感受的适当药物因素仍然受到挑战。将采用和评估一种减少MSC感应的“组合疗法”方法。这将需要破译主要的感应信号通路（即MTOR，EGF，FGF，PDGF和胰岛素）之间的非线性和高维关系。研究团队将首先利用AI来确定降低感受的药物因素的组合。随后，将对改革的MSC进行转录组分析，以确定诱导的转录响应如何减轻敏感性。通过进行有针对性的药物筛查，以使AI预测的因子组合抗衡，研究小组将确定基本的药物协同作用，从而扩大“联合疗法”的效率。结果感觉到它是为了确定可以通过扰动细胞培养基补充剂而可重复引起的有效修订轨迹。该奖项反映了NSF的法定任务，并通过使用该基金会的知识分子和更广泛的影响来审查标准，被认为是通过评估来获得的支持。