Point mutations in the androgen receptor (AR) are significant drivers of resistance in prostate cancer (PCa), posing a great challenge to the development of effective treatment strategies. Building on our previous discovery of the suboptimal AR antagonist T1-12, we developed LT16, which contains an N-(4-(benzyloxy)phenyl)piperidine-1-sulfonamide scaffold through structural optimization and comprehensive screening against T878A-mutated AR. LT16 outperformed existing antiandrogens by fully antagonizing clinical AR mutations and effectively suppressing castration- and enzalutamide-resistant LNCaP cells proliferation in vitro. Mechanically, LT16 was found to disrupt AR nuclear translocation, hinder AR homodimerization, and suppress transcription of AR-regulated genes by competitive binding to the ligand binding pocket. Further in vivo experiments demonstrated that LT16 significantly reduced both regular- and enzalutamide-resistant LNCaP tumor volume and serum prostate-specific antigen levels in mice. These findings position LT16 as a promising and innovative therapeutic for advanced PCa, particularly in cases where resistance to current therapies is a concern.
Xin Chai, Xinyue Wang, Lvtao Cai et al.· Journal of Medicinal Chemist...· 2 citations
Targeting the intrinsically disordered N-terminal domain of the androgen receptor (AR-NTD) represents a promising strategy to overcome resistance in prostate cancer. However, its inherent lack of a stable tertiary structure and highly dynamic conformational ensemble pose formidable challenges for rational drug design. This study introduces an integrated computational workflow that combines enhanced sampling techniques and machine learning collective variables to identify druggable conformations of the AR-NTD and elucidate the binding mechanism of its modulator, EPI-002. We characterize nine metastable states of the Tau-5 region and reveal that ligand recognition is driven by π–π stacking and structured water-mediated hydrogen bonds. Leveraging these insights, we perform structure-based virtual screening based on the identified druggable conformations and identify K53, a rationally designed AR-NTD antagonist, which exhibits potent anti-proliferative activity in enzalutamide-resistant prostate cancer cells. K53 directly binds the AR-NTD, suppresses AR transcriptional activity, and demonstrates high selectivity for cancer cells. This work provides a rational design paradigm for targeting intrinsically disordered proteins and offers a therapeutic candidate for resistant prostate cancer. In this work, the authors develop a machine learning–based enhanced sampling workflow to target the intrinsically disordered AR-NTD, identifying druggable conformations and enabling transferable modeling of ligand binding for rational drug discovery.
Kai Zhu, Huating Wang, Jintu Zhang et al.· Nature Communications· 0 citations