Department of Agronomy and Plant Breeding, Faculty of Agriculture, University of Zanjan, Zanjan, P.O. BOX. 4537138791, Iran.
Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, House of Biomedicine II, 6 avenue du Swing, Belvaux L-4367, Luxembourg.
Med Chem. 2023;19(6):594-618. doi: 10.2174/1573406419666230103142021.
The nuclear transcription factor PPARγ, which can modulate cell growth via proliferation and apoptosis-related mechanisms, is a promising target in cancer therapy. This study aims to focus on PPARγ as the target and use virtual screening to find hits.
A set of 5,677 flavonoid compounds were filtered by subjecting them to descriptor-based drug-likeness and ADMET strategies to discover drug-like compounds. The candidates' modes of binding to PPARγ were then evaluated using docking and MD simulation. PharmMapper was used to identify the potential targets of selected hits. The pharmacological network was constructed based on the GO and KEGG pathway analysis.
In primary screening, 3,057 compounds met various drug-likeness criteria and docked well as partial agonists in the PPARγ-LBD. Five compounds (euchrenone b, kaempferol-7-Orhamnoside, vincetoxicoside B, morusin, and karanjin) were selected with the use of ADMET profiles for further MD simulation investigation. Based on the PharmMapper findings, 52 proteins were then submitted to GO and KEGG enrichment analysis. As expected by GO and KEGG pathway enrichment studies, core targets were enriched in the PI3K-Akt signaling pathway (p < 0.01), indicating that certain chemicals may be involved in cancer processes.
Our results suggested that the selected compounds might have sufficient drug-likeness, pharmacokinetics, and in silico bioactivity by acting as PPARγ partial agonists. Although much work remains to illuminate extensive cancer therapeutic/ chemopreventive efficacy of flavonoids in vivo, in silico methodology of our cheminformatics research may be able to provide additional data regarding the efficacy and safety of potential candidates for therapeutic targets.
核转录因子 PPARγ 可以通过增殖和凋亡相关机制调节细胞生长，是癌症治疗的有前途的靶点。本研究旨在以 PPARγ 为靶点，利用虚拟筛选寻找命中化合物。
通过基于描述符的药物相似性和 ADMET 策略对 5677 种黄酮类化合物进行筛选，以发现类药性化合物。然后使用对接和 MD 模拟评估候选物与 PPARγ 的结合模式。使用 PharmMapper 识别所选命中物的潜在靶标。根据 GO 和 KEGG 通路分析构建药理学网络。
在初步筛选中，有 3057 种化合物符合各种药物相似性标准，并在 PPARγ-LBD 中作为部分激动剂良好对接。使用 ADMET 谱进一步进行 MD 模拟研究，选择了 5 种化合物（euchrenone b、kaempferol-7-Orhamnoside、vincetoxicoside B、morusin 和 karanjin）。根据 PharmMapper 的发现，然后将 52 种蛋白质提交给 GO 和 KEGG 富集分析。如 GO 和 KEGG 通路富集研究预期的那样，核心靶标富集在 PI3K-Akt 信号通路（p < 0.01）中，表明某些化学物质可能参与癌症过程。
我们的结果表明，所选化合物可能通过作为 PPARγ 部分激动剂具有足够的药物相似性、药代动力学和计算机生物活性。尽管还有大量工作需要阐明黄酮类化合物在体内的广泛癌症治疗/化学预防功效，但我们化学生信研究的计算方法可能能够为治疗靶点的潜在候选药物的功效和安全性提供额外数据。
