PDF(Pubmed)
Abstract：
The cannabinoid receptor 1 (CB1R) plays a pivotal role in regulating various physiopathological processes, thus positioning itself as a promising and sought-after therapeutic target. However, the search for specific and effective CB1R ligands has been challenging, prompting the exploration of drug repurposing (DR) strategies. In this study, we present an innovative DR approach that combines computational screening and experimental validation to identify potential Food and Drug Administration (FDA)-approved compounds that can interact with the CB1R. Initially, a large-scale virtual screening was conducted using molecular docking simulations, where a library of FDA-approved drugs was screened against the CB1R\'s three-dimensional structures. This in silico analysis allowed us to prioritize compounds based on their binding affinity through two different filters. Subsequently, the shortlisted compounds were subjected to in vitro assays using cellular and biochemical models to validate their interaction with the CB1R and determine their functional impact. Our results reveal FDA-approved compounds that exhibit promising interactions with the CB1R. These findings open up exciting opportunities for DR in various disorders where CB1R signaling is implicated. In conclusion, our integrated computational and experimental approach demonstrates the feasibility of DR for discovering CB1R modulators from existing FDA-approved compounds. By leveraging the wealth of existing pharmacological data, this strategy accelerates the identification of potential therapeutics while reducing development costs and timelines. The findings from this study hold the potential to advance novel treatments for a range of CB1R -associated diseases, presenting a significant step forward in drug discovery research.
摘要：
大麻素受体1（CB1R）在调节各种病理生理过程中起着关键作用，因此将自己定位为一个有希望和追捧的治疗目标。然而,寻找特异性和有效的CB1R配体一直具有挑战性，促使人们探索药物再利用（DR）策略。在这项研究中,我们提出了一种创新的DR方法，该方法结合了计算筛选和实验验证，以鉴定可能与CB1R相互作用的潜在食品和药物管理局（FDA）批准的化合物。最初，使用分子对接模拟进行了大规模的虚拟筛选，其中FDA批准的药物库针对CB1R的三维结构进行了筛选。这种计算机模拟分析使我们能够根据化合物通过两种不同的过滤器的结合亲和力对化合物进行优先级排序。随后，使用细胞和生化模型对入围化合物进行体外分析,以验证其与CB1R的相互作用并确定其功能影响.我们的结果揭示了FDA批准的化合物表现出与CB1R的有希望的相互作用。这些发现为涉及CB1R信号传导的各种疾病的DR开辟了令人兴奋的机会。总之,我们的综合计算和实验方法证明了DR从现有FDA批准的化合物中发现CB1R调节剂的可行性.通过利用丰富的现有药理学数据，这一策略加速了潜在治疗方法的确定,同时降低了开发成本和时间.这项研究的发现有可能为一系列CB1R相关疾病提供新的治疗方法。在药物发现研究方面迈出了重要的一步。
