Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China.
Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China; Tianjin University of Traditional Chinese Medicine, Tianjin, China.
Pharmacol Res. 2020 Oct;160:105077. doi: 10.1016/j.phrs.2020.105077. Epub 2020 Jul 17.
'Polypharmacology' is usually used to describe the network-wide effect of a single compound, but traditional Chinese medicine (TCM) has a polypharmacological effect naturally based on the 'multi-components, multi-targets and multi-pathways' principle. It is a challenge to investigate the polypharmacology mechanism of TCM with multiple components. In this study, we used XiaoErFuPi (XEFP) granules as an example to describe an unsupervised learning strategy for polypharmacology research of TCM and to explore the mechanism of XEFP polypharmacology against multifactorial disease function dyspepsia (FD). Unsupervised clustering of compounds based on similarity evaluation of cellular function fingerprints showed that compounds of TCM without similar targets and chemical structure could also exert similar therapeutic effects on the same disease, as different targets participate in the same pathway closely associated with the pathological process. In this study, we proposed an unsupervised machine learning strategy for exploring the polypharmacology-based mechanism of TCM, utilizing hierarchical clustering based on cellular functional similarity, to establish a connection from the chemical clustering module to cellular function. Meanwhile, FDA-approved drugs against FD were used as references for the mechanism of action (MoA) of FD. First, according to the compound-compound network built by the similarity of cellular function of XEFP compounds and FDA-approved FD drugs, the possible therapeutic function of TCM may represent a known mechanism of FDA-approved drugs. Then, as unsupervised learning, hierarchical clustering of TCM compounds based on cellular function fingerprint similarity could help to classify the compounds into several modules with similar therapeutic functions to investigate the polypharmacology effect of TCM. Furthermore, the integration of quantitative omics data of TCM and approved drugs (from LINCS datasets) provides more quantitative evidence for TCM therapeutic function consistency with approved drugs. A spasmolytic activity experiment was launched to confirm vanillic acid activity to repress smooth muscle contraction; vanillic acid was also predicted to be active compound of XEFP, supporting the accuracy of our strategy. In summary, the approach proposed in this study provides a new unsupervised learning strategy for polypharmacological research investigating TCM by establishing a connection between the compound functional module and drug-activated cellular processes shared with FDA-approved drugs, which may elucidate the unique mechanism of traditional medicine using FDA-approved drugs as references, facilitate the discovery of potential active compounds of TCM and provide new insights into complex diseases.
“多药理学”通常用于描述单个化合物的全网效应，但基于“多成分、多靶点、多途径”原则，中药（TCM）具有多药理学效应。研究具有多种成分的 TCM 的多药理学机制是一项挑战。在这项研究中，我们以小儿肺脾颗粒（XEFP）颗粒为例，描述了一种用于 TCM 多药理学研究的无监督学习策略，并探讨了 XEFP 多药理学治疗多因素疾病功能性消化不良（FD）的机制。基于细胞功能指纹相似性评估的化合物无监督聚类表明，没有相似靶点和化学结构的 TCM 化合物也可以对同一疾病产生相似的治疗效果，因为不同的靶点参与与病理过程密切相关的同一途径。在这项研究中，我们提出了一种用于探索 TCM 基于多药理学机制的无监督机器学习策略，利用基于细胞功能相似性的层次聚类，从化学聚类模块到细胞功能建立联系。同时，将 FDA 批准的治疗 FD 的药物作为 FD 作用机制（MoA）的参考。首先，根据 XEFP 化合物和 FDA 批准的 FD 药物的细胞功能相似性构建的化合物-化合物网络，TCM 的可能治疗功能可能代表 FDA 批准药物的已知机制。然后，作为无监督学习，基于细胞功能指纹相似性的 TCM 化合物层次聚类可以帮助将化合物分类为几个具有相似治疗功能的模块，以研究 TCM 的多药理学作用。此外，TCM 和批准药物的定量组学数据（来自 LINCS 数据集）的整合为 TCM 治疗功能与批准药物的一致性提供了更多定量证据。开展了一项解痉活性实验，以确认香草酸抑制平滑肌收缩的活性；香草酸也被预测为 XEFP 的活性化合物，支持我们策略的准确性。总之，本研究提出的方法通过建立化合物功能模块与 FDA 批准药物共享的药物激活细胞过程之间的联系，为 TCM 的多药理学研究提供了一种新的无监督学习策略，这可能利用 FDA 批准药物阐明传统医学的独特机制，促进 TCM 潜在活性化合物的发现，并为复杂疾病提供新的见解。
