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

Acta Pharmacol Sin. 2024 Sep;45(9):1978-1991. doi: 10.1038/s41401-024-01284-x. Epub 2024 May 15.

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

MeSH terms

  • Androgen Receptor Antagonists* / chemistry
  • Androgen Receptor Antagonists* / pharmacology
  • Antineoplastic Agents / chemistry
  • Antineoplastic Agents / pharmacology
  • Cell Cycle / drug effects
  • Cell Line, Tumor
  • Cell Proliferation* / drug effects
  • Humans
  • Machine Learning
  • Male
  • Prostatic Neoplasms* / drug therapy
  • Prostatic Neoplasms* / pathology
  • Receptors, Androgen* / metabolism
  • Structure-Activity Relationship

Substances

  • Androgen Receptor Antagonists
  • Receptors, Androgen
  • Antineoplastic Agents
  • AR protein, human