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通过计算和体外功能表征鉴定海因生物碱副甲藻素为新型 CXCR4 拮抗剂。

Identification of the hydantoin alkaloids parazoanthines as novel CXCR4 antagonists by computational and in vitro functional characterization.

机构信息

Institute of Biomolecular Chemistry, National Research Council (ICB-CNR), Via Campi Flegrei 34, 80078, Pozzuoli, (NA), Italy.

Section of Pharmacology, Department of Internal Medicine, University of Genova, 16132 Genova, Italy.

出版信息

Bioorg Chem. 2020 Dec;105:104337. doi: 10.1016/j.bioorg.2020.104337. Epub 2020 Oct 6.

Abstract

CXCR4 chemokine receptor represents an attractive pharmacological target due to its key role in cancer metastasis and inflammatory diseases. Starting from our previously-developed pharmacophoric model, we applied a combined computational and experimental approach that led to the identification of the hydantoin alkaloids parazoanthines, isolated from the Mediterranean Sea anemone Parazoanthus axinellae, as novel CXCR4 antagonists. Parazoanthine analogues were then synthesized to evaluate the contribution of functional groups to the overall activity. Within the panel of synthesized natural and non-natural parazoanthines, parazoanthine-B was identified as the most potent CXCR4 antagonist with an IC value of 9.3 nM, even though all the investigated compounds were able to antagonize in vitro the down-stream effects of CXC12, albeit with variable potency and efficacy. The results of our study strongly support this class of small molecules as potent CXCR4 antagonists in tumoral pathologies characterized by an overexpression of this receptor. Furthermore, their structure-activity relationships allowed the optimization of our pharmacophoric model, useful for large-scale in silico screening.

摘要

趋化因子受体 CXCR4 因其在癌症转移和炎症性疾病中的关键作用,成为有吸引力的药物靶点。基于我们之前开发的药效团模型,我们采用了一种组合的计算和实验方法,从而鉴定出从地中海海葵 Parazoanthus axinellae 中分离出的海因类生物碱假珊瑚素是新型 CXCR4 拮抗剂。然后合成了假珊瑚素类似物,以评估官能团对整体活性的贡献。在所合成的天然和非天然假珊瑚素中,假珊瑚素-B 被鉴定为最有效的 CXCR4 拮抗剂,IC 值为 9.3 nM,尽管所有研究的化合物都能够拮抗 CXC12 的下游效应,但效力和效能存在差异。我们的研究结果强烈支持这类小分子作为过表达该受体的肿瘤病理学中有效的 CXCR4 拮抗剂。此外,它们的构效关系允许我们对药效团模型进行优化,该模型可用于大规模的计算机筛选。

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