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
Resistance-conferring mutations in the androgen receptor (AR) ligand-binding pocket (LBP) compromise the effectiveness of clinically approved orthosteric AR antagonists. Targeting the dimerization interface pocket (DIP) of AR presents a promising therapeutic approach. In this study, we report the design and optimization of N-(thiazol-2-yl) furanamide derivatives as novel AR DIP antagonists, among which C13 was the most promising candidate. C13 exhibited excellent AR antagonistic activity (IC50 = 0.010 μM), effectively blocked AR dimerization and nuclear translocation, and demonstrated potent efficacy in several castration-resistant prostate cancer (CRPC) cells. Notably, C13 showed superior efficacy against variant drug-resistant AR mutants, along with favorable metabolic stability, excellent pharmacokinetic properties, and low brain distribution. Furthermore, oral administration of C13 achieved 123.4% tumor growth inhibition in an LNCaP xenograft model without apparent toxicity. As a noncompetitive binder, C13 complements current LBP-targeting AR inhibitors and represents a promising therapy for drug-resistant PCa.
Jinbiao Liao, J. Liao, Yanzhen Yu et al.· Journal of Medicinal Chemist...· 1 citation
Photodynamic therapy (PDT) is a clinically approved therapeutic modality that has demonstrated significant potential for cancer treatment, and triplet photosensitizers (PSs) play a key role in its efficacy. Despite deep learning having emerged as a next-generation tool for material discovery, existing methods mainly target a limited subset of triplet PSs, such as thermally activated delayed fluorescence (TADF) materials, neglecting the critical intersystem crossing (ISC) between the high-lying singlet and triplet states (ΔESnTn). To overcome this limitation, we compiled a comprehensive dataset (∼1.90 × 109) of triplet PSs encompassing various ISC mechanisms. Then, we proposed a novel strategy that incorporates two models: a fragment-based model (Frag-MD) and a character-based model (MD), both integrating a conditional transformer, recurrent neural networks, and reinforcement learning. In silico experiments revealed that the Frag-MD model outperforms the MD model in generating larger conjugated motifs with higher average ring numbers and atom counts; while the MD model generates twice as many unique motifs and excels in novelty and diversity, as evaluated by conditional and MOSES metrics. Therefore, our approach is highly effective for modifying conjugated motifs and designing novel triplet PSs. Notably, the recently reported high-efficiency triplet PSs have been re-identified through ablation experiments using our proposed models, which target ΔESnTn and significantly outperform traditional baselines, achieving a prediction accuracy of 73% versus 4%. Our approach holds the potential to establish a new paradigm for discovering novel PSs applicable in PDT.
Kepeng Chen, Xiaoting Zhang, Jike Wang et al.· Chemical Science· 3 citations
ConspectusProstate cancer (PCa) is the most prevalent malignancy among men worldwide, with its pathogenesis and progression heavily reliant on the sustained activation of the androgen receptor (AR) signaling pathway. The AR, a transcription factor of nuclear receptor superfamily, serves as the most privileged therapeutic target in PCa, as evidenced by the clinical efficacy of first- and second-generation AR antagonists. Current clinically available AR antagonists exclusively target the ligand binding pocket (LBP), suppressing tumor proliferation through competitive inhibition of androgen binding and subsequent blockade of AR signaling transduction. However, their therapeutic utility is invariably limited by acquired resistance mechanisms, including point mutations that alter LBP specificity, AR gene amplification leading to receptor overexpression, and the emergence of constitutively active splice variants that bypass ligand-dependent activation. Thus, the development of novel AR antagonists featuring innovative mechanisms and structural scaffolds is imperative to overcome resistance to antiandrogen therapy. However, the AR exhibits significant structural flexibility, and the lack of antagonist-bound crystal structures has hindered structure-based rational drug design. In this Article, we summarize our advances in elucidating the molecular mechanisms underlying AR conformational regulation and highlight our progress in the structure-based design and development of novel AR antagonists. First, our molecular dynamic (MD) studies collectively elucidate the molecular mechanisms by which the AR ligand binding domain (LBD) regulates its functional states through dynamic conformational changes mediated by distinct allosteric pathways when bound to agonists or antagonists, providing atomic-level insights and structural basis for drug development. Then, we successfully identified structurally diverse lead compounds targeting the LBP through various integrated approaches combining MD simulations, structure-based virtual screening (SBVS), and systematic biological evaluation. These compounds exhibited potent activity against clinically relevant AR mutations F877L, W742C, T878A, and H875Y, demonstrating their potential to overcome mutations-driven resistance. Further, we explored non-LBP mediated strategies for AR antagonism, including: (1) targeting the allosteric binding sites on LBD; (2) identification of novel druggable binding sites; and (3) targeting alternative domains beyond the LBD. As a paradigm-shifting example, we proposed inhibition of AR LBD dimerization as a novel mechanism of action for LBP-targeting AR antagonists. Building upon this insight, we characterized a promising pocket at the dimer interface, designated the Dimerization Interface Pocket (DIP), and developed first-in-class antagonists specifically targeting this site, which exhibit exceptional therapeutic potential. Collectively, these multipronged strategies not only highlight the power of computation-driven approaches in drug discovery but also yield a diverse pipeline of resistance-targeting candidates, directly addressing the unmet clinical need in advanced PCa.
Xin Chai, Tingjun Hou, Dan Li· Accounts of Chemical Researc...· 0 citations
Accurate prediction of metalloprotein-ligand interactions is critical for metalloprotein-targeted drug discovery. Conventional docking tools and existing deep learning (DL) models fail to reliably capture metal-ligand interactions, hampering the discovery of potent metalloprotein inhibitors. Here, we propose MetalloDock, the first DL-based docking framework specially designed for metalloprotein targets. By innovatively integrating an autoregressive spatial decoding engine with a physics-constrained geometric generation paradigm, MetalloDock can precisely reconstruct metal coordination geometries and accurately capture metal-ligand interactions, which enhance both the accuracy of metalloprotein-ligand docking and binding affinity prediction. Extensive evaluations on our custom-built benchmark data set demonstrate that MetalloDock outperforms existing methods, including AlphaFold3, in docking success rate and virtual screening performance for metalloprotein targets. In real-world applications, MetalloDock successfully identified multiple novel hit compounds in a virtual screening campaign targeting the prostate-specific membrane antigen. Additionally, it enabled rational drug design for acidic polymerase endonuclease, leading to the discovery of potent inhibitors. These results highlight the broad applicability of MetalloDock in accelerating metalloprotein-targeted drug discovery and provide a standardized framework for future evaluation of metalloprotein-specific docking algorithms.
Hui Zhang, Xujun Zhang, Qun Su et al.· Journal of the American Chem...· 5 citations
Glucocorticoids (GCs) are limited by severe side effects, driving the development of selective glucocorticoid receptor modulators (SGRMs) with improved therapeutic profiles. We previously development the SGRM lead B53, which suffered from poor metabolic stability. In this study, structure-guided optimization of B53 yielded 43 novel sulfonamide derivatives. Among them, D8, which contained 2-oxo-tetrahydroquinoline by carbonyl migration form B53, manifests an excellent SGRM with remarkable transrepression potency (IC50NF-κB = 0.9 nM) superior to dexamethasone (IC50 NF-κB = 5.0 nM). Besides, D8 exhibits a significantly higher specificity for GR over AR, MR, and PR and exhibited less adverse effects on osteoprotegerin. Furthermore, D8 demonstrated improved metabolic stability and optimized binding mode within the GR LBD. In vivo, oral administration of D8 significantly alleviated dermatitis and autoimmune hepatitis in mouse models, underscoring its therapeutic potential and validating our design strategy.
Xiaodong Bao, Yuxin Zhou, Zhaoxu Yang et al.· Journal of Medicinal Chemist...· 1 citation
The discovery of CAP-Gly domain-containing linker protein 1(CLIP1)-Leukocyte tyrosine kinase (LTK) as an oncogenic fusion reveals a unique dependency not only on LTK kinase activity but also on CLIP1-mediated multimerization, a noncatalytic function that drives oncogenic signaling. While this fusion is currently targeted with anaplastic lymphoma kinase inhibitors, their exclusive focus on kinase inhibition leaves the scaffolding function intact, necessitating a complete protein clearance strategy. Here, we report the AI-guided development of a first-in-class proteolysis-targeting chimera (PROTAC) designed to selectively degrade the CLIP1-LTK fusion protein. By integrating deep learning models for ternary complex prediction with structure-based molecular optimization, we designed DCL05, an orally bioavailable degrader of CLIP1-LTK fusion protein, achieving picomolar degradation potency (DC50 = 40 pM) and robust antitumor activity. DCL05 consistently outperformed existing kinase inhibitors across a broad spectrum of LTK resistance-associated mutations, both in vitro and in vivo. Collectively, our study explores resistance-associated contexts of LTK and establishes a structure-guided PROTAC development pipeline, providing a promising therapeutic strategy for overcoming acquired resistance in kinase-driven cancers.
Shicheng Chen, Haiting Duan, S. Zhong et al.· Proceedings of the National...· 0 citations