Model informed Precision Medicine of Chinese Herbal Medicines Formulas- A Multi-scale Mechanistic Intelligent Model

IF 6.1 1区 医学 Q1 PHARMACOLOGY & PHARMACY
Yuanyuan Qian, Xiting Wang, Lulu Cai, Jiangxue Han, Zhu Huang, Yahui Lou, Bingyue Zhang, Yanjie Wang, Xiaoning Sun, Yan Zhang, Aisong Zhu
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引用次数: 0

Abstract

Recent trends suggest that Chinese herbal medicine formulas (CHM formulas) are promising treatments for complex diseases. To characterize the precise syndromes, precise diseases and precise targets of the precise targets between complex diseases and CHM formulas, we developed an artificial intelligence-based quantitative predictive algorithm (DeepTCM). DeepTCM has gone through multilevel model calibration and validation against a comprehensive set of herb and disease data so that it accurately captures the complex cellular signaling, molecular and theoretical levels of traditional Chinese medicine (TCM). As an example, our model simulated the optimal CHM formulas for the treatment of coronary heart disease (CHD) with depression, and through model sensitivity analysis, we calculated the balanced scoring of the formulas. Furthermore, we constructed a biological knowledge graph representing interactions by associating herb-target and gene-disease interactions. Finally, we experimentally confirmed the therapeutic effect and pharmacological mechanism of a novel model-predicted intervention in humans and mice. This novel multiscale model opened up a new avenue to combine “disease syndrome” and "macro micro" system modeling to facilitate translational research in CHM formulas.

Abstract Image

中药配方精准医学模型--多尺度机理智能模型
最近的趋势表明,中药方剂(CHM 方剂)是治疗复杂疾病的有前途的方法。为了描述复杂疾病与中药方剂之间的精确综合征、精确疾病和精确靶点,我们开发了一种基于人工智能的定量预测算法(DeepTCM)。DeepTCM 经过了多层次的模型校准,并通过全面的草药和疾病数据集进行了验证,从而准确捕捉到了中药复杂的细胞信号、分子和理论层面。例如,我们的模型模拟了治疗冠心病合并抑郁症的最佳中药方剂,并通过模型敏感性分析计算出了方剂的平衡评分。此外,我们还通过关联草药-目标和基因-疾病之间的相互作用,构建了代表相互作用的生物知识图谱。最后,我们通过实验证实了新型模型预测干预措施在人类和小鼠中的治疗效果和药理机制。这种新型多尺度模型为结合 "疾病综合征 "和 "宏观微观 "系统建模,促进中药配方的转化研究开辟了一条新途径。
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来源期刊
Journal of Pharmaceutical Analysis
Journal of Pharmaceutical Analysis Chemistry-Electrochemistry
CiteScore
16.20
自引率
2.30%
发文量
674
审稿时长
22 weeks
期刊介绍: The Journal of Pharmaceutical Analysis (JPA), established in 2011, serves as the official publication of Xi'an Jiaotong University. JPA is a monthly, peer-reviewed, open-access journal dedicated to disseminating noteworthy original research articles, review papers, short communications, news, research highlights, and editorials in the realm of Pharmacy Analysis. Encompassing a wide spectrum of topics, including Pharmaceutical Analysis, Analytical Techniques and Methods, Pharmacology, Metabolism, Drug Delivery, Cellular Imaging & Analysis, Natural Products, and Biosensing, JPA provides a comprehensive platform for scholarly discourse and innovation in the field.
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