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基于结构的药物设计技术对 EGFR 和 HER-2 抑制剂双重活性的研究。

Studies on the Dual Activity of EGFR and HER-2 Inhibitors Using Structure-Based Drug Design Techniques.

机构信息

School of Arts, Sciences and Humanities (EACH), University of São Paulo (USP), São Paulo 03828-000, Brazil.

Center for Natural and Human Sciences (CCNH), Federal University of ABC (UFABC), Santo André 09210-580, Brazil.

出版信息

Int J Mol Sci. 2018 Nov 23;19(12):3728. doi: 10.3390/ijms19123728.

Abstract

HER-2 and EGFR are biological targets related to the development of cancer and the discovery and/or development of a dual inhibitor could be a good strategy to design an effective drug candidate. In this study, analyses of the chemical properties of a group of substances having affinity for both HER-2 and EGFR were carried out with the aim of understanding the main factors involved in the interaction between these inhibitors and the biological targets. Comparative analysis of molecular interaction fields (CoMFA) and comparative molecular similarity index analysis (CoMSIA) techniques were applied on 63 compounds. From CoMFA analyses, we found for both HER-2 (r² calibration = 0.98 and q² = 0.83) and EGFR (r² calibration = 0.98 and q² = 0.73) good predictive models. Good models for CoMSIA technique have also been found for HER-2 (r² calibration = 0.92 and q² = 0.74) and EGFR (r² calibration = 0.97 and q² = 0.72). The constructed models could indicate some important characteristics for the inhibition of the biological targets. New compounds were proposed as candidates to inhibit both proteins. Therefore, this study may guide future projects for the development of new drug candidates for the treatment of breast cancer.

摘要

HER-2 和 EGFR 是与癌症发展相关的生物靶点,发现和/或开发双重抑制剂可能是设计有效药物候选物的一种很好的策略。在这项研究中,对一组对 HER-2 和 EGFR 都具有亲和力的物质的化学性质进行了分析,目的是了解这些抑制剂与生物靶点相互作用的主要因素。对 63 种化合物进行了分子相互作用场(CoMFA)和比较分子相似性指数分析(CoMSIA)技术的比较分析。从 CoMFA 分析中,我们发现对于 HER-2(r²校准= 0.98 和 q²= 0.83)和 EGFR(r²校准= 0.98 和 q²= 0.73),都得到了良好的预测模型。对于 CoMSIA 技术,也为 HER-2(r²校准= 0.92 和 q²= 0.74)和 EGFR(r²校准= 0.97 和 q²= 0.72)找到了良好的模型。所构建的模型可以指示抑制生物靶点的一些重要特征。提出了新的化合物作为抑制两种蛋白质的候选物。因此,这项研究可能为开发治疗乳腺癌的新药候选物提供指导。

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