Feng Chunlai, Cheng Jiaxi, Sun Mengqiu, Qiao Chunxue, Feng Qiuqi, Fang Naying, Ge Yingying, Rui Mengjie
School of Pharmacy, Jiangsu University, Zhenjiang, China.
Front Pharmacol. 2025 Jan 7;15:1522787. doi: 10.3389/fphar.2024.1522787. eCollection 2024.
The L. (PVL) and Hand.-Mazz. (TH) herb pair, which is commonly used in traditional Chinese medicine (TCM), has been applied for the treatment of breast cancer. Although its efficacy is validated, the synergistic anti-breast cancer compound combinations within this herb pair and their underlying mechanisms of action remain unclear.
This study aimed to identify and validate synergistic anti-breast cancer compound combinations within the PVL-TH pair using large-scale biomedical data, artificial intelligence and experimental methods. The first step was to investigate the anti-breast cancer effects of various PVL and TH extracts using cellular assays to identify the most effective superior extracts. These superior extracts were subjected to liquid chromatography-mass spectrometry (LC-MS) analysis to identify their constituent compounds. A deep learning-based prediction model, DeepMDS, was applied to predict synergistic anti-breast cancer multi-compound combinations. These predicted combinations were experimentally validated for their anti-breast cancer effects at actual content ratios found in the extracts. Preliminary bioinformatics analyses were conducted to explore the mechanisms of action of these superior combinations. We also compared the anti-breast cancer effects of superior extracts from different geographical origins and analyzed the contents of compounds to assess their representation of the anti-tumor effect of the corresponding TCM.
The results revealed that LC-MS analysis identified 27 and 21 compounds in the superior extracts (50% ethanol extracts) of PVL and TH, respectively. Based on these compounds, DeepMDS model predicted synergistic anti-breast cancer compound combinations such as F973 (caffeic acid, rosmarinic acid, p-coumaric acid, and esculetin), T271 (chlorogenic acid, cichoric acid, and caffeic acid), and T1685 (chlorogenic acid, rosmarinic acid, and scopoletin) from single PVL, single TH and PVL-TH herb pair, respectively. These combinations, at their actual concentrations in extracts, demonstrated superior anti-breast cancer activity compared to the corresponding extracts. The bioinformatics analysis revealed that these compounds could regulate tumor-related pathways synergistically, inhibiting tumor cell growth, inducing cell apoptosis, and blocking cell cycle progression. Furthermore, the concentration ratio and total content of compounds in F973 and T271 were closely associated with their anti-breast cancer effects in extracts from various geographical origins. The compound combination T1685 could represent the synergistic anti-breast cancer effects of the PVL-TH pair.
This study provides insights into exploring the representative synergistic anti-breast cancer compound combinations within the complex TCM.
中药中常用的露蜂房（PVL）和山慈菇（TH）药对已被应用于乳腺癌的治疗。尽管其疗效得到了验证，但该药对中协同抗乳腺癌的化合物组合及其潜在作用机制仍不清楚。
本研究旨在利用大规模生物医学数据、人工智能和实验方法，识别并验证PVL-TH药对中协同抗乳腺癌的化合物组合。第一步是使用细胞实验研究各种PVL和TH提取物的抗乳腺癌作用，以确定最有效的优质提取物。对这些优质提取物进行液相色谱-质谱（LC-MS）分析，以鉴定其成分化合物。应用基于深度学习的预测模型DeepMDS预测协同抗乳腺癌的多化合物组合。在提取物中实际发现的含量比例下，对这些预测组合的抗乳腺癌作用进行实验验证。进行初步的生物信息学分析，以探索这些优质组合的作用机制。我们还比较了不同地理来源的优质提取物的抗乳腺癌作用，并分析了化合物含量，以评估它们对相应中药抗肿瘤作用的代表性。
结果显示，LC-MS分析分别在PVL和TH的优质提取物（50%乙醇提取物）中鉴定出27种和21种化合物。基于这些化合物，DeepMDS模型分别从单一PVL、单一TH和PVL-TH药对中预测出协同抗乳腺癌的化合物组合，如F973（咖啡酸、迷迭香酸、对香豆酸和七叶亭）、T271（绿原酸、菊苣酸和咖啡酸）和T1685（绿原酸、迷迭香酸和东莨菪素）。这些组合在提取物中的实际浓度下，与相应提取物相比，表现出更强的抗乳腺癌活性。生物信息学分析表明，这些化合物可协同调节肿瘤相关通路，抑制肿瘤细胞生长、诱导细胞凋亡并阻断细胞周期进程。此外，F973和T271中化合物的浓度比和总含量与它们在不同地理来源提取物中的抗乳腺癌作用密切相关。化合物组合T1685可代表PVL-TH药对的协同抗乳腺癌作用。
本研究为探索复杂中药中具有代表性的协同抗乳腺癌化合物组合提供了思路。
