基于网络药理学探讨阿帕替尼治疗乳腺癌的作用机制

朱婉婷, 范雪梅, 位华, 王义明, 李小芳, 王淑美, 陈万生, 罗国安

中国药学杂志 ›› 2016, Vol. 51 ›› Issue (18) : 1569-1573.

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中国药学杂志 ›› 2016, Vol. 51 ›› Issue (18) : 1569-1573. DOI: 10.11669/cpj.2016.18.007
论著

基于网络药理学探讨阿帕替尼治疗乳腺癌的作用机制

  • 朱婉婷1,2, 范雪梅2*, 位华3, 王义明2, 李小芳2, 王淑美1*, 陈万生3, 罗国安2
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Mechanism Research of Apatinib-Treated Breast Cancer Based on Network Pharmacology

  • ZHU Wan-ting1,2, FAN Xue-mei2*, WEI Hua3, WANG Yi-ming2 , LI Xiao-fang2, WANG Shu-mei1*, CHEN Wan-sheng3, LUO Guo-an2
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摘要

目的 探讨抗癌新药阿帕替尼治疗乳腺癌的潜在作用靶点和作用机制。方法 研究采用PharmMapper反向分子对接技术结合生物信息学分析,预测阿帕替尼的潜在作用靶点,并采用分子对接软件Autodock 4.0将预测得到的蛋白靶点与阿帕替尼进行分子对接分析,研究阿帕替尼化合物与靶蛋白的相互作用。结果 研究发现,4个蛋白Cell division protein kinase 2、GTPase HRas、NONE和CytochromeP450 2C9可能是阿帕替尼治疗乳腺癌的潜在重要作用靶点。阿帕替尼与这4个蛋白靶点之间有较稳定的氢键结合,并且具有较低的结合自由能,结合稳定。结论 阿帕替尼可能通过调控Cell division protein kinase 2、GTPase HRas、NONE和CytochromeP450 2C9的功能或生物作用来参与乳腺癌的治疗。本实验为阿帕替尼治疗乳腺癌的作用机制的深入探讨提供了理论依据和线索。

Abstract

OBJECTIVE To study the potential targets and action mechanism of apatinib-treated breast cancer. METHODS Combination with bioinformatic analysis, PharmMapper reverse docking technology was used to predict potential targets of apatinib and docking was performed using Autodock 4.0 to examine the interaction of aptinib wih predicted protein targets. RESULTS Cell division protein kinase 2,GTPase HRas, NONE and cytochrome P450 2C9 were found to be the potential targets of apatinib-treated breast cancer. Stable hydrogen bonds were formed between apatinib and the four predicted targets, and the binding free energy was low. CONCLUSION Apatinib maybe participate the functional regulation or biological action of cell division protein kinase 2, GTPase HRas, NONE and Cytochrome P4502C9 to treat breast cancer, which provides a theoretical basis and a clue for further exploration of the mechanism research of apatinib-treated breast cancer.

关键词

阿帕替尼 / 乳腺癌 / 网络药理学 / 靶点预测 / 分子对接

Key words

apatinib / breast cancer / network pharmacology / target predict / molecular docking

引用本文

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朱婉婷, 范雪梅, 位华, 王义明, 李小芳, 王淑美, 陈万生, 罗国安. 基于网络药理学探讨阿帕替尼治疗乳腺癌的作用机制[J]. 中国药学杂志, 2016, 51(18): 1569-1573 https://doi.org/10.11669/cpj.2016.18.007
ZHU Wan-ting, FAN Xue-mei, WEI Hua, WANG Yi-ming , LI Xiao-fang, WANG Shu-mei, CHEN Wan-sheng, LUO Guo-an. Mechanism Research of Apatinib-Treated Breast Cancer Based on Network Pharmacology[J]. Chinese Pharmaceutical Journal, 2016, 51(18): 1569-1573 https://doi.org/10.11669/cpj.2016.18.007
中图分类号: R965   

参考文献

[1] ZHANG Y E, ZHANG T B, TONG D J. Apatinib-treated gastric cancer listing [N]. Science and Technology Daily(科技日报), 2014-11-08.
[2] GENG R, LI J. Apatinib for the treatment of gastric cancer [J]. Expert Opin Pharmacother, 2015, 16(1):117-122.
[3] ZHANG H J. Apatinib for molecular targeted therapy in tumor[J]. Drug Des Devel Ther, 2015, 9:6075-6081.
[4] YAN J M, YONG J L, HUANG H B, et al. Apatinib (YN968D1) reverses multidrug resistance by inhibiting the efflux function of multiple ATP-binding cassette transporters [J]. Cancer Res, 2010, 70(20):7981-7991.
[5] FANG M H. Studies on predictive and prognostic factors for recurrent or metastatic breast cancer treated with small molecular anti-angiogenic agents [D]. Shanghai : Fudan University, 2013.
[6] HU X C, ZHANG H, XU B, et al. Multicenter phase II study of apatinib, a novel VEGFR inhibitor in heavily pretreated patients with metastatic triple-negative breast cancer[J]. Int J Cancer, 2014, 135(8) :1961-1969.
[7] GAO L, LIU A L, DU G H. Application and progress of computer aided drug design(CADD) in drug development [J]. Chin Pharm J(中国药学杂志), 2011,46(9):641-645.
[8] SONG J L, XIANGY H, ZHAO L L, et al. Potential target prediction and molecular docking studies of SAHA [J]. Comput Chem(计算机与应用化学),2013, 30 (1) :97-101.
[9] HOPKINS A L. Network pharmacology [J]. Nat Biotechnol, 2007,25(10):1110-1111.
[10] HSIN K Y, GHOSH S, KITANO H. Combining machine learning systems and multiple docking simulation packages to improve docking prediction reliability for network pharmacology [J]. PLoS One, 2013, 8 (12):16-16.
[11] TAO W, XU X, WANG X, et al. Network pharmacology-based prediction of the Radix Curcumae ingredients and potential targets of Chinese herbal Radix Curcumae formula for application to cardiovascular disease [J]. J Ethnopharmacol, 2013, 145(1):1-10.
[12] LIU X, OUYANG S, YU B, et al. PharmMapper server:a web server for potential drug target identification using pharmacophore mapping approach [J]. Nucleic Acids Res, 2010, 38(2):609-614.
[13] BAI Y,FAN X M, SUN H, et al. The mechanism of rosiglitazone compound based on network pharmacology [J]. Acta Pharm Sin(药学学报), 2015, 50 (3):284-290.
[14] LI J H, HU A, ZHENG W J, et al. Screening of target protein of triptolide on psoriasis by molecular docking[J]. Chin Pharm J(中国药学杂志), 2014,49 (13) :1133-1138.
[15] BERMAN H M, BATTISTUZ T, BHAT T N, et al. The protein data bank [J]. Nucleic Acids Res, 2000, 28(1):235-242 .
[16] WANG Y R, ZHANG X Z, LI N, et al. Action mechanism of Shuangyu granules on upper respiratorytract infections based on network pharmacology [J]. Chin New Drugs J (中国新药杂志), 2015, 24(11):1222-1241.
[17] KIM S J, MASUDA N, TSUKAMOTO F, et al. The cell cycle profiling-risk score based on CDK1 and 2 predicts early recurrence in node-negative, hormone receptor-positive breast cancer treated with endocrine therapy [J]. Cancer Lett, 2014, 355(2):217-223.
[18] KIM S J, NAKAYAMA S, SHIMAZU K, et al. Recurrence risk score based on the specific activity of CDK1 and CDK2 predicts response to neoadjuvant paclitaxel followed by 5-fluorouracil, epirubicin and cyclophosphamide in breast cancers[J]. Ann Oncol, 2012, 23(4):891-897.
[19] JAUTELAT R, BRUMBY T, SCHAFER M, et al. From the insoluble dye indirubin towards highly active, soluble CDK2-inhibitors [J]. Chembiochem, 2005, 6(3):531-540.
[20] SCHUBBERT S, SHANNON K, BOLLAG G. Hyperactive Ras in developmental disorders and cancer [J]. Nat Rev Cancer, 2007, 7(4):295-308.
[21] DA SILVA L, SIMPSON P T, SMART C E, et al. HER3 and downstream pathways are involved in colonization of brain metastases from breast cancer [J]. Breast Cancer Res, 2010, 12(4):46.
[22] CASTAGNINO N, TORTOLINA L, BALBI A, et al. Dynamic simulations of pathways downstream of ERBB-family, including mutations and treatments:concordance with experimental results [J]. Curr Cancer Drug Targets,2010, 10(7):737-757.
[23] FINLAYSON C A, CHAPPELL J, LEITNER J W, et al. Enhanced insulin signaling via Shc in human breast cancer [J]. Metabolism, 2003, 52(12):1606-1611.
[24] USHA T, MIDDHA S K, GOYAL A K, et al. Molecular docking studies of anti-cancerous candidates in Hippophae rhamnoides and Hippophae salicifolia [J]. J Biomed Mater Res, 2014, 28(5):406-415.
[25] APOSTOLI A J, ROCHE J M, SCHNEIDER M M, et al. Opposing roles for mammary epithelial-specific PPARγ signaling and activation during breast tumour progression [J]. Mol Cancer, 2015, 14(1):1-14.
[26] KOPP T L, LUNDQVIST J, PETERSEN R K, et al. In vitro screening of inhibition of PPAR-γ activity as a first step in identification of potential breast carcinogens [J]. Hum Exp Toxicol, 2015, 34(11):1106-1118.
[27] CASIMIRO-GARCIA A, BIGGE C F, DAVIS J A, et al. Synthesis and evaluation of novelalpha-heteroaryl-phenylpropanoicacidderivatives as PPAR alpha/gammadualagonists [J]. Bioorg Med Chem, 2009, 17(20):7113-7125.
[28] MICHELIS U R, FISSLTHALER B, BARBOSA-SICARD E, et al. Cytochrome P450 epoxygenases 2C8 and 2C9 are implicated in hypoxia-induced endothelial cell migration and angiogenesis [J]. J Cell Sci, 2005,118(23):5489-5498.
[29] BELTON O, FITZGERALD D J. Cyclooxygenase isoforms and atherosclerosis [J]. Expert Rev Mol Med, 2003, 5 (9) :1-18.
[30] DING Y F. Metabolism and pharmacokinetics of anti-cancer drug, apatinib in human [D]. Beijing:University of Chinese Academy of Sciences,2013.

基金

国家自然科学基金重点资助项目(81130066);青年科学基金资助项目(81302731)
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