PO.ET09.03 · 实验与分子治疗
通过 AI 辅助分子建模合理设计强效雄激素受体降解剂并验证其在前列腺癌中的抗肿瘤疗效
Rational design of potent androgen receptor degraders via AI-assisted molecular modeling and validation of anti-tumor efficacy in prostate cancer
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
雄激素受体（AR）在前列腺癌进展中发挥核心作用，其持续的信号传导仍是去势抵抗性前列腺癌（CRPC）中的主要挑战。对 AR 的靶向蛋白降解提供了一种有前景的治疗策略，以克服限制现有 AR 拮抗剂疗效的治疗耐药。为合理设计新型 AR 降解剂，我们采用 AnHorn 专有的 AIMCADD（人工智能-生物信息-药物化学-计算机辅助药物设计）平台预测 AR 配体结合域（AR-LBD）与 CRBN E3 连接酶之间的三维结合构象。分子动力学（MD）模拟用于计算结合自由能并鉴定最稳定的 AR-CRBN 三元复合体。基于该复合体，通过分子对接获得 AR-LBD 与 CRBN 弹头的结合构象，随后使用基于深度神经网络的算法 AIMLinker 进行 AI 辅助的连接子生成，以构建降解剂候选物的虚拟库。进一步应用分子对接和动态模拟以评估三元复合体稳定性和结合亲和力，从中选取一部分排名靠前的候选物进行实验验证。先导候选物在 LNCaP、22Rv1 和 VCaP 细胞中有效诱导 AR 降解。值得注意的是，这些降解剂对 CRPC 患者中常见的临床相关 AR 突变体也保持活性，包括 L702H、T878A、H875Y、W742C 和 F877L。共同处理蛋白酶体抑制剂 MG132 可消除降解，证实其为泛素-蛋白酶体依赖性机制。通过药物亲和响应性靶点稳定性（DARTS）检测进一步验证了先导化合物与 AR 的直接结合。定量 RT-PCR 分析显示 AR 下游靶点 KLK3（PSA）显著下调，提示 AR 转录活性的丧失。在细胞活力检测中，最强效的化合物选择性抑制 AR 阳性前列腺癌细胞的增殖，而对正常细胞的细胞毒性极小。在异种移植动物模型中，每日一次给药显著降低了肿瘤体积和血浆中前列腺特异性抗原（PSA）水平，且无可观察到的全身毒性。总之，通过 AI 辅助的分子设计和计算筛选，我们开发出一种强效的 AR 降解剂，能有效诱导蛋白酶体依赖性 AR 降解、抑制 AR 转录活性，并在体内展现出稳健的抗肿瘤疗效。这些结果凸显了通过靶向 AR 降解治疗晚期前列腺癌的一种有前景的治疗方法。
查看英文原文 English abstract
The androgen receptor (AR) plays a central role in the progression of prostate cancer, and its sustained signaling remains a major challenge in castration-resistant prostate cancer (CRPC). Targeted protein degradation of the AR offers a promising therapeutic strategy to overcome therapeutic resistance that limits the efficacy of current AR antagonists. To rationally design novel AR degraders, we employed AnHorn's proprietary AIMCADD (Artificial Intelligence-bio-Informatic-MedChem-Computer-Aided Drug Design) platform to predict the three-dimensional binding conformations between the AR ligand-binding domain (AR-LBD) and CRBN E3 ligase. Molecular dynamics (MD) simulations were used to calculate binding free energies and identify the most stable AR-CRBN ternary complex. Based on this complex, the binding conformations of AR-LBD and CRBN warheads were obtained via molecular docking, followed by AI-assisted linker generation using AIMLinker, a deep neural network-based algorithm, to construct a virtual library of degrader candidates. Molecular docking and dynamic simulations were further applied to evaluate ternary complex stability and binding affinity, from which a subset of top-ranked candidates were selected for experimental validation. Lead candidates effectively induced AR degradation in LNCaP, 22Rv1, and VCaP cells. Notably, these degraders also retained activity against clinically relevant AR mutants frequently observed in CRPC patients, including L702H, T878A, H875Y, W742C, and F877L. The degradation was abolished by co-treatment with the proteasome inhibitor MG132, confirming a ubiquitin-proteasome-dependent mechanism. Direct binding of the lead compound to AR was further verified by drug affinity responsive target stability (DARTS) assay. Quantitative RT-PCR analysis revealed a marked downregulation of AR downstream targets, KLK3 (PSA), suggesting the loss of AR transcriptional activity. In cell viability assays, the most potent compound selectively suppressed proliferation of AR-positive prostate cancer cells, with minimal cytotoxicity in normal cells. In xenograft animal models, once daily administration significantly reduced tumor volume and prostate specific antigen (PSA) level in plasma without observable systemic toxicity. In summary, through AI-assisted molecular design and computational screening, we developed a potent AR degrader that efficiently induces proteasome-dependent AR degradation, suppresses AR transcriptional activity, and exhibits robust anti-tumor efficacy in vivo . These results highlight a promising therapeutic approach for advanced prostate cancer through targeted AR degradation.
利益披露 Disclosure
C. Chou, None..
Y. Lin, None..
C. Chou, None..
K. Chen, None..
S. Chen, None..
C. Lin, None.