To Kenneth K W, Cho William C
School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China.
Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong SAR, China.
Pharmaceutics. 2023 Aug 21;15(8):2166. doi: 10.3390/pharmaceutics15082166.
Immune checkpoint inhibitors (ICI) have achieved unprecedented clinical success in cancer treatment. However, drug resistance to ICI therapy is a major hurdle that prevents cancer patients from responding to the treatment or having durable disease control. Drug repurposing refers to the application of clinically approved drugs, with characterized pharmacological properties and known adverse effect profiles, to new indications. It has also emerged as a promising strategy to overcome drug resistance. In this review, we summarized the latest research about drug repurposing to overcome ICI resistance. Repurposed drugs work by either exerting immunostimulatory activities or abolishing the immunosuppressive tumor microenvironment (TME). Compared to the de novo drug design strategy, they provide novel and affordable treatment options to enhance cancer immunotherapy that can be readily evaluated in the clinic. Biomarkers are exploited to identify the right patient population to benefit from the repurposed drugs and drug combinations. Phenotypic screening of chemical libraries has been conducted to search for T-cell-modifying drugs. Genomics and integrated bioinformatics analysis, artificial intelligence, machine and deep learning approaches are employed to identify novel modulators of the immunosuppressive TME.
免疫检查点抑制剂（ICI）在癌症治疗中取得了前所未有的临床成功。然而，对ICI治疗的耐药性是一个主要障碍，阻碍癌症患者对治疗产生反应或实现持久的疾病控制。药物重新利用是指将具有明确药理特性和已知不良反应谱的临床批准药物应用于新的适应症。它也已成为克服耐药性的一种有前景的策略。在本综述中，我们总结了关于药物重新利用以克服ICI耐药性的最新研究。重新利用的药物通过发挥免疫刺激活性或消除免疫抑制性肿瘤微环境（TME）来发挥作用。与从头设计药物策略相比，它们提供了新颖且经济实惠的治疗选择，以增强可在临床中轻松评估的癌症免疫疗法。利用生物标志物来确定能从重新利用的药物和药物组合中获益的合适患者群体。已经对化学文库进行了表型筛选以寻找调节T细胞的药物。采用基因组学和综合生物信息学分析、人工智能、机器学习和深度学习方法来识别免疫抑制性TME的新型调节剂。
