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A potent new-scaffold androgen receptor antagonist discovered on the basis of a MIEC-SVM model
Xin-Yue Wang1, Xin Chai1, Lu-Hu Shan2
1College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
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.
Insights
Researchers developed a machine learning model to find new prostate cancer drugs. They discovered Compound C2, a novel androgen receptor antagonist with significant anti-cancer effects, offering a promising new therapeutic scaffold.
Area of Science:
- Oncology
- Medicinal Chemistry
- Computational Biology
Background:
- Prostate cancer (PCa) is a leading global malignancy in men.
- Androgen receptor (AR) signaling is a key driver in PCa development and progression.
- AR antagonists are established therapies, but novel agents are needed.
Purpose of the Study:
- To identify novel androgen receptor (AR) antagonists using a machine learning approach.
- To evaluate the efficacy and specificity of newly discovered AR antagonists in preclinical models.
Main Methods:
- Development and application of a machine-learning MIEC-SVM model for virtual screening.
- Selection and bioactivity evaluation of 51 candidate compounds.
- In vitro assessment of AR transcriptional function, nuclear translocation, cell cycle progression, and antiproliferative/antimetastatic activity using LNCaP cells.
Main Results:
- Identification of a novel-scaffold AR antagonist, designated C2, with bioactivity comparable to Enzalutamide (Enz).
- C2 demonstrated potent inhibition of AR transcriptional function (IC50 = 0.63 μM) and nuclear translocation.
- Significant antiproliferative and antimetastatic effects were observed in LNCaP cells, with C2 outperforming Enz in cell cycle blockade at lower doses and showing superior AR specificity.
Conclusions:
- The MIEC-SVM model is effective for discovering novel AR antagonists.
- Compound C2 represents a promising new scaffold for developing targeted AR therapeutics for prostate cancer.
- Further investigation of C2 is warranted for its potential in advanced prostate cancer treatment.
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