Discovery of novel VEGFR2 inhibitors against non-small cell lung cancer based on fingerprint-enhanced graph attention convolutional network.

Autor: Wang Z; Department of Pharmacy, Honghui Hospital, Xi'an Jiaotong University, Xi'an, 710054, China. zixiaowang1112@foxmail.com., Sun L; Department of Pharmacy, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China., Xu Y; State Key Laboratory of Natural Medicines, Jiangsu Key Laboratory of Drug Discovery for Metabolic Diseases, Center of Drug Discovery, China Pharmaceutical University, Nanjing, 210009, China., Huang J; Department of Pharmacy, Honghui Hospital, Xi'an Jiaotong University, Xi'an, 710054, China., Yang F; Department of Pharmacy, Honghui Hospital, Xi'an Jiaotong University, Xi'an, 710054, China., Chang Y; Department of Pharmacy, Honghui Hospital, Xi'an Jiaotong University, Xi'an, 710054, China. changyu0552@163.com.
Jazyk: angličtina
Zdroj: Journal of translational medicine [J Transl Med] 2024 Dec 03; Vol. 22 (1), pp. 1097. Date of Electronic Publication: 2024 Dec 03.
DOI: 10.1186/s12967-024-05893-2
Abstrakt: Despite the proven inhibitory effects of drugs targeting vascular endothelial growth factor receptor 2 (VEGFR2) on solid tumors, including non-small cell lung cancer (NSCLC), the development of anti-NSCLC drugs solely targeting VEGFR2 still faces risks such as off-target effects and limited efficacy. This study aims to develop a novel fingerprint-enhanced graph attention convolutional network (FnGATGCN) model for predicting the activity of anti-NSCLC drugs. Employing a multimodal fusion strategy, the model integrates a feature extraction layer that comprises molecular graph feature extraction and molecular fingerprint feature extraction. The performance evaluation results indicate that the model exhibits high accuracy and stability in predicting activity. Moreover, we explored the relationship between molecular features and biological activity through visualization analysis, thus improving the interpretability of the approach. Utilizing this model, we screened the ZINC database and conducted high-precision molecular docking, leading to the identification of 11 potential active molecules. Subsequently, molecular dynamics simulations and free energy calculations were performed. The results demonstrate that all 11 aforementioned molecules can stably bind to VEGFR2 under dynamic conditions. Among the short-listed compounds, the top six exhibited satisfactory inhibitory activity against VEGFR2 and A549 cells. Especially, compound Z-3 displayed VEGFR2 inhibitory with IC 50 values of 0.88 μM, and anti-proliferative activity against A549 cells with IC 50 values of 4.23 ± 0.45 μM. This approach combines the advantages of target-based and phenotype-based screening, facilitating the rapid and efficient identification of candidate compounds with dual activity against VEGFR2 and A549 cell lines. It provides new insights and methods for the development of anti-NSCLC drugs. Furthermore, further biological activity tests revealed that Z1-Z3 and Z6 manifested relatively strong antiproliferative activities against NCI-H23 and NCI-H460, and relatively low toxicity towards GES-1. The hit compounds were promising candidates for the further development of novel VEGFR2 inhibitors against NSCLC.
Competing Interests: Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: All authors of this article agree to publish. Competing interests: The authors declare no competing interests.
(© 2024. The Author(s).)
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje