释放Alisertib治疗乳腺癌的潜力:利用网络药理学和基因表达网络深入探索分子靶点

IF 2.7 4区 医学 Q2 PHARMACOLOGY & PHARMACY
Suad A. Alghamdi, Mohammed Alissa, Muhammad Suleman
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引用次数: 0

摘要

目的乳腺癌是一种常见的严重疾病,其特征是细胞生长不受控制。Alisertib是一种小分子抑制剂,通过阻断细胞增殖显示出癌症治疗的潜力。本研究使用网络药理学方法来确定Alisertib在乳腺癌中的潜在靶点。方法采用网络药理学、分子动力学模拟和结合自由能等方法,鉴定Alisertib治疗乳腺癌的潜在分子靶点。结果swisstarget、SuperPred和CLCpred分别鉴定出100,721和327个Alisertib的潜在靶点。从DisGeNet中,鉴定出6,941个与乳腺癌相关的标记,其中29个蛋白作为疾病相关基因和药物靶点重叠。此外,CytoHubba从29个共同靶点的PPI网络中鉴定出10个枢纽基因,其中FGFR2、FGFR4和MAPK7根据其程度评分排名最佳。此外,对接分析显示FGFR2、FGFR4和MAPK7-Alisertib复合物的对接评分分别为-8.854 kcal/mol、-7.373 kcal/mol和-7.262 kcal/mol。通过200 ns分子动力学模拟进一步验证了鉴定的靶点与Alisertib的稳定相互作用。使用MM/GBSA进行结合自由能计算,FGFR2-alisertib复合物的结合自由能为-61.0977 kcal/mol, FGFR4-alisertib复合物的结合自由能为-52.0032 kcal/mol, MAPK7-alisertib复合物的结合自由能为-47.9903 kcal/mol。这些结果表明,与对照组相比,Alisertib对FGFR2、FGFR4和MAPK7具有更强的结合亲和力。这些发现表明Alisertib与FGFR4、FGFR2和MAPK7具有很强的结合亲和力和良好的药理相互作用,突出了其作为乳腺癌靶向治疗的潜力。因此,Alisertib值得在临床前和临床环境中进一步研究,以评估其治疗乳腺恶性肿瘤的疗效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unlocking the Therapeutic Potential of Alisertib in Breast Cancer: An In-Depth Exploration of Molecular Targets Using Network Pharmacology and Gene Expression Network

Purpose

Breast cancer is a prevalent and serious disease marked by uncontrolled cell growth. Alisertib, a small-molecule inhibitor, shows potential in cancer treatment by blocking cell proliferation. This study uses a network pharmacology approach to identify Alisertib's potential targets in breast cancer.

Methods

We used network pharmacology, molecular dynamic simulation and binding free energies approaches to identify the potential molecular target of Alisertib in Breast Cancer.

Results

SwissTarget, SuperPred, and CLCpred identified 100, 721, and 327 potential targets for Alisertib, respectively. From DisGeNet, 6,941 markers associated with breast cancer were identified, among which 29 proteins overlapped as both disease-associated genes and drug targets. Furthermore, the CytoHubba identified 10 hub genes from the PPI network of 29 common targets, with FGFR2, FGFR4, and MAPK7 ranked best based on their degree score. Moreover, the docking analysis revealed a docking scores of -8.854 kcal/mol, -7.373 kcal/mol and -7.262 kcal/mol for FGFR2, FGFR4, and MAPK7-Alisertib complexes respectively. The stable interaction of identified targets and Alisertib was further validated by the 200 ns molecular dynamics simulation. Binding free energy calculations using MM/GBSA yielded values of -61.0977 kcal/mol for the FGFR2-alisertib complex, -52.0032 kcal/mol for FGFR4-alisertib, and -47.9903 kcal/mol for MAPK7-alisertib. These results suggest that Alisertib exhibits stronger binding affinity for FGFR2, FGFR4 and MAPK7 compared to the control.

Conclusion

These findings suggest that Alisertib has a strong binding affinity and favorable pharmacological interactions with FGFR4, FGFR2, and MAPK7, highlighting its potential as a targeted therapeutic for breast cancer. Consequently, Alisertib warrants further investigation in preclinical and clinical settings to evaluate its efficacy in treating malignant neoplasm of the breast.

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来源期刊
Journal of Pharmaceutical Innovation
Journal of Pharmaceutical Innovation PHARMACOLOGY & PHARMACY-
CiteScore
3.70
自引率
3.80%
发文量
90
审稿时长
>12 weeks
期刊介绍: The Journal of Pharmaceutical Innovation (JPI), is an international, multidisciplinary peer-reviewed scientific journal dedicated to publishing high quality papers emphasizing innovative research and applied technologies within the pharmaceutical and biotechnology industries. JPI''s goal is to be the premier communication vehicle for the critical body of knowledge that is needed for scientific evolution and technical innovation, from R&D to market. Topics will fall under the following categories: Materials science, Product design, Process design, optimization, automation and control, Facilities; Information management, Regulatory policy and strategy, Supply chain developments , Education and professional development, Journal of Pharmaceutical Innovation publishes four issues a year.
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