AI Article Synopsis

  • - Prostate cancer (PCa) is a common cancer in men, driven significantly by the activation of androgen receptor (AR) signaling, leading to the use of AR antagonists in treatment.
  • - A machine-learning model (MIEC-SVM) was created for virtual screening of new AR antagonists, resulting in the identification of 51 candidates, including a promising new compound called C2.
  • - C2 demonstrated strong anti-cancer effects by inhibiting AR activity and the growth of prostate cancer cells more effectively than existing treatments, suggesting it could be a valuable option for future therapies.

Article 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 (IC = 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11335958PMC
http://dx.doi.org/10.1038/s41401-024-01284-xDOI Listing

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