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通过增强人工智能和高通量筛选选出的靶向雄激素受体变构结合位点的类药物分子,作为抗前列腺癌的抗肿瘤药物。

Drug-like molecules targeting androgen receptor's allosteric binding sites selected by augmented AI and high-throughput screening as antitumor agents against prostate cancer.

作者信息

Bruzzese Francesca, Iannelli Federica, Roca Maria Serena, Addi Laura, Grumetti Laura, Ahmed Mostafa, Budillon Alfredo, Gumede Njabulo Joyfull

机构信息

Experimental Animal Unit, Istituto Nazionale per lo Studio e la Cura dei Tumori, "Fondazione Pascale"-IRCCS, Naples, Italy.

Experimental Pharmacology Unit, Istituto Nazionale per lo Studio e la Cura dei Tumori, "Fondazione Pascale"-IRCCS, Naples, Italy.

出版信息

Biomed Pharmacother. 2025 Sep;190:118403. doi: 10.1016/j.biopha.2025.118403. Epub 2025 Aug 2.

Abstract

Despite therapeutic advancements, metastatic and nonmetastatic castration-resistant prostate cancer (mCRPC and nmCRPC) remain incurable due to drug resistance, partly due to Androgen receptor (AR) gene abnormalities and splice variants. An Augmented Artificial Intelligence (AI) -driven virtual drug screening using AtomNet® technology explored an 8 million small molecule library targeting AR allosteric sites to address resistance from AR mutations and AR-V7 splice variant. Screening identified compounds effectively reducing cancer cell growth, with ten actives in hormone-resistant cells. Dose-response assays identified compounds 1, 8, and 77 as most efficacious, exhibiting pIC values from < 5.86-4.82. Docking and molecular dynamics (MD) simulations indicated that compound 77 targets both the allosteric site within the AF2 domain and the ligand binding domain of AR. Compounds 1, 8, and 77 were most active in 2D and 3D spheroid assay, with compound 77 showing significant activity in AR+ cells and modest effects in AR-null cells. These compounds surpassed treatments like enzalutamide and galeterone in inhibiting colony and sphere formation, indicating new therapeutic potential. These effects, partly due to AR inhibition, suggest additional mechanisms in inhibiting cancer growth and disrupting models, as seen in PC3 cells. Moreover, the docking analysis indicated that compounds 1, 8, and 77 exhibit binding affinity with the Glucocorticoid receptor (GR). Furthermore, immunofluorescence assay on PC3 cells, demonstrated that compound 77 inhibits the nuclear translocation of GR following dexamethasone treatment, which is used to induce GR nuclear translocation. Furthermore, these findings revealed that compound 77 significantly reduces the transcription of GR and its downstream gene FKBP5, a classical GR target gene. These results substantiate our hypothesis that compound 77, particularly engages in off-target interactions, thereby potentially disrupting cancer growth.

摘要

尽管治疗方法有所进步,但转移性和非转移性去势抵抗性前列腺癌(mCRPC和nmCRPC)由于耐药性仍然无法治愈,部分原因是雄激素受体(AR)基因异常和剪接变体。使用AtomNet®技术的增强型人工智能(AI)驱动的虚拟药物筛选探索了一个针对AR变构位点的800万小分子文库,以解决AR突变和AR-V7剪接变体引起的耐药性问题。筛选鉴定出能有效降低癌细胞生长的化合物,其中有10种活性化合物对激素抵抗细胞有效。剂量反应试验确定化合物1、8和77最为有效,其pIC值在<5.86至4.82之间。对接和分子动力学(MD)模拟表明,化合物77靶向AF2结构域内的变构位点和AR的配体结合结构域。化合物1、8和77在二维和三维球体试验中活性最高,化合物77在AR+细胞中显示出显著活性,而在AR缺失细胞中作用较小。这些化合物在抑制集落和球体形成方面超过了恩杂鲁胺和加列酮等治疗药物,显示出新的治疗潜力。这些作用部分归因于AR抑制,表明在抑制癌症生长和破坏模型方面存在其他机制,如在PC3细胞中所见。此外,对接分析表明,化合物1、8和77与糖皮质激素受体(GR)具有结合亲和力。此外,对PC3细胞的免疫荧光分析表明,化合物77抑制了地塞米松处理后GR的核转位,地塞米松用于诱导GR核转位。此外,这些发现表明化合物77显著降低了GR及其下游基因FKBP5(一个经典的GR靶基因)的转录。这些结果证实了我们的假设,即化合物77特别参与脱靶相互作用,从而有可能破坏癌症生长。

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