G蛋白结合受体数字双胞胎用于精密和个性化医学
Tabitha Boeringer1, Mia Pardo1, Carter J Craig1
1Receptor Biology Lab - Department of Drug Discovery, Division of Basic Sciences, H. Lee Moffitt Cancer Center, Tampa, FL 33612, United States.
Computational and structural biotechnology journal
|December 8, 2025
概括
数字双胞胎正在通过创建虚拟患者模型来改变G蛋白结合受体 (GPCR) 疗法的精密医学. 这种方法整合了多个omics数据来个性化各种疾病的治疗方法,改善患者的治疗结果.
科学领域:
- 综合生物学和计算医学.
- 药理学和药物发现
- 基因组学,蛋白质组学和多组学.
背景情况:
- 精准医学是利用个人的遗传,环境和生活方式数据来定制医疗保健.
- G蛋白结合受体 (GPCRs) 是许多疾病的关键治疗点.
- 数字双胞胎技术为个性化GPCR向治疗提供了一种新的方法.
研究的目的:
- 探索数字双胞胎技术在推进GPCRs精密医学方面的潜力.
- 概念化数字双胞胎对个性化基于受体的疗法的影响.
- 突出数字双胞胎,定量系统药理学 (QSP) 和人工智能 (AI) 的协同作用.
主要方法:
- 整合基因组,蛋白质组和实时生理数据,构建患者特定的虚拟模型.
- 精准医学从药物遗传学发展到多组学.
- 通过QSP和AI增强的数字双胞胎应用程序的概念化,用于模拟GPCR信号和预测药物反应.
主要成果:
- 数字双胞胎可以为心血管,瘤和神经系统疾病中的关键GPCRs (GLP1R,CXCR4,DRD2) 提供个性化的治疗应用.
- QSP和AI可以增强数字双胞胎,以加速药物发现和优化患者分层.
- 数字双胞胎有望对心力衰竭,癌症,代谢,神经退行和神经精神疾病的个性化治疗产生重大影响.
结论:
- 数字双胞胎为精确的GPCR药物提供了一个强大的平台,将分子见解与临床应用联系起来,以改善结果.
- 数字双胞胎,QSP和AI的结合将彻底改变GPCR向疗法.
- 解决伦理 (数据隐私,访问,验证) 和技术 (数据集成,可扩展性) 挑战对于广泛的临床实施至关重要.
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