Article

A potent new-scaffold androgen receptor antagonist discovered on the basis of a MIEC-SVM model

Xin-yue Wang1, Xin Chai1, Lu-hu Shan2, Xiao-hong Xu2, Lei Xu3, Ting-jun Hou1,4, Hui-yong Sun5, Dan Li1,6
1 College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
2 Institute of Cancer Research and Basic Medical Sciences of Chinese Academy of Sciences, Cancer Hospital of University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou 310022, China
3 Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China
4 State Key Lab of CAD&CG, Zhejiang University, Hangzhou 310058, China
5 Department of Medicinal Chemistry, China Pharmaceutical University, Nanjing 210009, China
6 Jinhua Institute of Zhejiang University, Jinhua 321000, China
Correspondence to: Hui-yong Sun: huiyongsun@cpu.edu.cn, Dan Li: lidancps@zju.edu.cn,
DOI: 10.1038/s41401-024-01284-x
Received: 10 January 2024
Accepted: 3 April 2024
Advance online: 15 May 2024

Abstract

Prostate cancer (PCa) is the second most prevalent malignancy among men worldwide. The aberrant activation of androgen receptor (AR) signaling has been recognized as a crucial oncogenic driver for PCa and AR antagonists are widely used in PCa therapy. To develop novel AR antagonist, a machine-learning MIEC-SVM model was established for the virtual screening and 51 candidates were selected and submitted for bioactivity evaluation. To our surprise, a new-scaffold AR antagonist C2 with comparable bioactivity with Enz was identified at the initial round of screening. C2 showed pronounced inhibition on the transcriptional function (IC50 = 0.63 μM) and nuclear translocation of AR and significant antiproliferative and antimetastatic activity on PCa cell line of LNCaP. In addition, C2 exhibited a stronger ability to block the cell cycle of LNCaP than Enz at lower dose and superior AR specificity. Our study highlights the success of MIEC-SVM in discovering AR antagonists, and compound C2 presents a promising new scaffold for the development of AR-targeted therapeutics.

Keywords: prostate cancer; androgen receptor antagonist; virtual screening; MIEC-SVM model; machine learning

Article Options

Download Citation

Cited times in Scopus