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Current Topics in Medicinal Chemistry

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ISSN (Print): 1568-0266
ISSN (Online): 1873-4294

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Review Article

Recent Trends in Computer-aided Drug Design for Anti-cancer Drug Discovery

Author(s): Iashia Tur Razia, Ayesha Kanwal, Hafiza Fatima Riaz, Abbeha Malik, Muhammad Ahsan, Muhammad Saleem Khan, Ali Raza, Sumera Sabir, Zureesha Sajid, Muhammad Fardeen Khan, Rana Adnan Tahir and Sheikh Arslan Sehgal*

Volume 23, Issue 30, 2023

Published on: 17 November, 2023

Page: [2844 - 2862] Pages: 19

DOI: 10.2174/0115680266258467231107102643

Price: $65

Abstract

Cancer is considered one of the deadliest diseases globally, and continuous research is being carried out to find novel potential therapies for myriad cancer types that affect the human body. Researchers are hunting for innovative remedies to minimize the toxic effects of conventional therapies being driven by cancer, which is emerging as pivotal causes of mortality worldwide. Cancer progression steers the formation of heterogeneous behavior, including self-sustaining proliferation, malignancy, and evasion of apoptosis, tissue invasion, and metastasis of cells inside the tumor with distinct molecular features. The complexity of cancer therapeutics demands advanced approaches to comprehend the underlying mechanisms and potential therapies. Precision medicine and cancer therapies both rely on drug discovery. In vitro drug screening and in vivo animal trials are the mainstays of traditional approaches for drug development; however, both techniques are laborious and expensive. Omics data explosion in the last decade has made it possible to discover efficient anti-cancer drugs via computational drug discovery approaches. Computational techniques such as computer-aided drug design have become an essential drug discovery tool and a keystone for novel drug development methods. In this review, we seek to provide an overview of computational drug discovery procedures comprising the target sites prediction, drug discovery based on structure and ligand-based design, quantitative structure-activity relationship (QSAR), molecular docking calculations, and molecular dynamics simulations with a focus on cancer therapeutics. The applications of artificial intelligence, databases, and computational tools in drug discovery procedures, as well as successfully computationally designed drugs, have been discussed to highlight the significance and recent trends in drug discovery against cancer. The current review describes the advanced computer-aided drug design methods that would be helpful in the designing of novel cancer therapies.

Keywords: Cancer, Computer Aided Drug Design (CADD), Drug Discovery, Bioinformatics, Molecular Docking, Anti- cancer drugs, Structure-based drug design.

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[1]
Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin., 2021, 71(3), 209-249.
[http://dx.doi.org/10.3322/caac.21660] [PMID: 33538338]
[2]
Braunhut, B.L.; Punnen, S.; Kryvenko, O.N. Updates on Grading and Staging of Prostate Cancer. Surg. Pathol. Clin., 2018, 11(4), 759-774.
[http://dx.doi.org/10.1016/j.path.2018.07.003] [PMID: 30447840]
[3]
Hortobagyi, G.N.; Edge, S.B.; Giuliano, A. New and Important Changes in the TNM Staging System for Breast Cancer. Am. Soc. Clin. Oncol. Educ. Book, 2018, 38(38), 457-467.
[http://dx.doi.org/10.1200/EDBK_201313] [PMID: 30231399]
[4]
Rotondo, J.C.; Mazziotta, C.; Lanzillotti, C.; Stefani, C.; Badiale, G.; Campione, G.; Martini, F.; Tognon, M. The role of purinergic P2X7 receptor in inflammation and cancer: Novel molecular insights and clinical applications. Cancers (Basel), 2022, 14(5), 1116.
[http://dx.doi.org/10.3390/cancers14051116] [PMID: 35267424]
[5]
Clinton, S.K.; Giovannucci, E.L.; Hursting, S.D. The World Cancer Research Fund/American Institute for Cancer Research Third Expert Report on Diet, Nutrition, Physical Activity, and Cancer: Impact and Future Directions. J. Nutr., 2020, 150(4), 663-671.
[http://dx.doi.org/10.1093/jn/nxz268] [PMID: 31758189]
[6]
Zhang, Y.B.; Pan, X.F.; Chen, J.; Cao, A.; Zhang, Y.G.; Xia, L.; Wang, J.; Li, H.; Liu, G.; Pan, A. Combined lifestyle factors, incident cancer, and cancer mortality: A systematic review and meta-analysis of prospective cohort studies. Br. J. Cancer, 2020, 122(7), 1085-1093.
[http://dx.doi.org/10.1038/s41416-020-0741-x] [PMID: 32037402]
[7]
Biller, L.H.; Schrag, D. Diagnosis and Treatment of Metastatic Colorectal Cancer. JAMA, 2021, 325(7), 669-685.
[http://dx.doi.org/10.1001/jama.2021.0106] [PMID: 33591350]
[8]
Campos-Contreras, A.R.; Díaz-Muñoz, M.; Vázquez-Cuevas, F.G. Purinergic signaling in the hallmarks of cancer. Cells, 2020, 9(7), 1612.
[http://dx.doi.org/10.3390/cells9071612] [PMID: 32635260]
[9]
Mou, X.; Kesari, S.; Wen, P.Y.; Huang, X. Crude drugs as anticancer agents. Int. J. Clin. Exp. Med., 2011, 4(1), 17-25.
[PMID: 21394282]
[10]
Tsafa, E.; Bentayebi, K.; Topanurak, S.; Yata, T.; Przystal, J.; Fongmoon, D.; Hajji, N.; Waramit, S.; Suwan, K.; Hajitou, A. Doxorubicin Improves Cancer Cell Targeting by Filamentous Phage Gene Delivery Vectors. Int. J. Mol. Sci., 2020, 21(21), 7867.
[http://dx.doi.org/10.3390/ijms21217867] [PMID: 33114050]
[11]
Pantziarka, P.; Capistrano I, R.; De Potter, A.; Vandeborne, L.; Bouche, G. An Open Access Database of Licensed Cancer Drugs. Front. Pharmacol., 2021, 12, 627574.
[http://dx.doi.org/10.3389/fphar.2021.627574] [PMID: 33776770]
[12]
Roma-Rodrigues, C.; Mendes, R.; Baptista, P.; Fernandes, A. Targeting Tumor Microenvironment for Cancer Therapy. Int. J. Mol. Sci., 2019, 20(4), 840.
[http://dx.doi.org/10.3390/ijms20040840] [PMID: 30781344]
[13]
Kapetanovic, I.M. Computer-aided drug discovery and development (CADDD): in silico-chemico-biological approach. Chem. Biol. Interact., 2008, 171(2), 165-176.
[http://dx.doi.org/10.1016/j.cbi.2006.12.006] [PMID: 17229415]
[14]
Cui, W.; Aouidate, A.; Wang, S.; Yu, Q.; Li, Y.; Yuan, S. Discovering anti-cancer drugs via computational methods. Front. Pharmacol., 2020, 11, 733.
[http://dx.doi.org/10.3389/fphar.2020.00733] [PMID: 32508653]
[15]
Kaldor, S.W.; Kalish, V.J.; Davies, J.F., II; Shetty, B.V.; Fritz, J.E.; Appelt, K.; Burgess, J.A.; Campanale, K.M.; Chirgadze, N.Y.; Clawson, D.K.; Dressman, B.A.; Hatch, S.D.; Khalil, D.A.; Kosa, M.B.; Lubbehusen, P.P.; Muesing, M.A.; Patick, A.K.; Reich, S.H.; Su, K.S.; Tatlock, J.H. Viracept (nelfinavir mesylate, AG1343): A potent, orally bioavailable inhibitor of HIV-1 protease. J. Med. Chem., 1997, 40(24), 3979-3985.
[http://dx.doi.org/10.1021/jm9704098] [PMID: 9397180]
[16]
Zhavoronkov, A.; Ivanenkov, Y.A.; Aliper, A.; Veselov, M.S.; Aladinskiy, V.A.; Aladinskaya, A.V.; Terentiev, V.A.; Polykovskiy, D.A.; Kuznetsov, M.D.; Asadulaev, A.; Volkov, Y.; Zholus, A.; Shayakhmetov, R.R.; Zhebrak, A.; Minaeva, L.I.; Zagribelnyy, B.A.; Lee, L.H.; Soll, R.; Madge, D.; Xing, L.; Guo, T.; Aspuru-Guzik, A. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat. Biotechnol., 2019, 37(9), 1038-1040.
[http://dx.doi.org/10.1038/s41587-019-0224-x] [PMID: 31477924]
[17]
Bray, F.; Ferlay, J.; Soerjomataram, I.; Siegel, R.L.; Torre, L.A.; Jemal, A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin., 2018, 68(6), 394-424.
[http://dx.doi.org/10.3322/caac.21492] [PMID: 30207593]
[18]
Chiang, Y.K.; Kuo, C.C.; Wu, Y.S.; Chen, C.T.; Coumar, M.S.; Wu, J.S.; Hsieh, H.P.; Chang, C.Y.; Jseng, H.Y.; Wu, M.H.; Leou, J.S.; Song, J.S.; Chang, J.Y.; Lyu, P.C.; Chao, Y.S.; Wu, S.Y. Generation of ligand-based pharmacophore model and virtual screening for identification of novel tubulin inhibitors with potent anticancer activity. J. Med. Chem., 2009, 52(14), 4221-4233.
[http://dx.doi.org/10.1021/jm801649y] [PMID: 19507860]
[19]
Yang, S.Y. Pharmacophore modeling and applications in drug discovery: Challenges and recent advances. Drug Discov. Today, 2010, 15(11-12), 444-450.
[http://dx.doi.org/10.1016/j.drudis.2010.03.013] [PMID: 20362693]
[20]
Gøtzsche, P.C.; Jørgensen, K.J.J.C.d.o.s.r. Screening for breast cancer with mammography. Cochrane Database Syst Rev., 2013, 2013(6), CD001877.
[http://dx.doi.org/10.1002/14651858.CD001877.pub5]
[21]
Ye, Q.; Ling, S.; Zheng, S.; Xu, X. Liquid biopsy in hepatocellular carcinoma: Circulating tumor cells and circulating tumor DNA. Mol. Cancer, 2019, 18(1), 114.
[http://dx.doi.org/10.1186/s12943-019-1043-x] [PMID: 31269959]
[22]
Pisapia, P.; Malapelle, U.; Troncone, G. Liquid Biopsy and Lung Cancer. Acta Cytol., 2019, 63(6), 489-496.
[http://dx.doi.org/10.1159/000492710] [PMID: 30566947]
[23]
Jiao, X.; Zhang, S.; Jiao, J.; Zhang, T.; Qu, W.; Muloye, G.M.; Kong, B.; Zhang, Q.; Cui, B. Promoter methylation of SEPT9 as a potential biomarker for early detection of cervical cancer and its overexpression predicts radioresistance. Clin. Epigenetics, 2019, 11(1), 120.
[http://dx.doi.org/10.1186/s13148-019-0719-9] [PMID: 31426855]
[24]
Dorrell, D.N.; Strowd, L.C. Skin Cancer Detection Technology. Dermatol. Clin., 2019, 37(4), 527-536.
[http://dx.doi.org/10.1016/j.det.2019.05.010] [PMID: 31466592]
[25]
Pasechnikov, V.; Chukov, S.; Fedorov, E.; Kikuste, I.; Leja, M. Gastric cancer: Prevention, screening and early diagnosis. World J. Gastroenterol., 2014, 20(38), 13842-13862.
[http://dx.doi.org/10.3748/wjg.v20.i38.13842] [PMID: 25320521]
[26]
Philp, L.; Jembere, N.; Wang, L.; Gao, J.; Maguire, B.; Kupets, R. Pap tests in the diagnosis of cervical cancer: Help or hinder? Gynecol. Oncol., 2018, 150(1), 61-66.
[http://dx.doi.org/10.1016/j.ygyno.2018.05.019] [PMID: 29773301]
[27]
Arnal, M.J.D.; Ferrández Arenas, Á.; Lanas Arbeloa, Á. Esophageal cancer: Risk factors, screening and endoscopic treatment in Western and Eastern countries. World J. Gastroenterol., 2015, 21(26), 7933-7943.
[http://dx.doi.org/10.3748/wjg.v21.i26.7933] [PMID: 26185366]
[28]
Schatten, H. Brief Overview of Prostate Cancer Statistics, Grading, Diagnosis and Treatment Strategies. Adv. Exp. Med. Biol., 2018, 1095, 1-14.
[http://dx.doi.org/10.1007/978-3-319-95693-0_1] [PMID: 30229546]
[29]
Rock, C.L.; Thomson, C.; Gansler, T. American Cancer Society guideline for diet and physical activity for cancer prevention. CA Cancer J Clin., 2020, 70(4), 245-271.
[http://dx.doi.org/10.3322/caac.21591]
[30]
Mancebo, S.E.; Wang, S.Q. Skin cancer: Role of ultraviolet radiation in carcinogenesis. Rev. Environ. Health, 2014, 29(3), 265-273.
[http://dx.doi.org/10.1515/reveh-2014-0041] [PMID: 25252745]
[31]
Grimes, D.R. Radiofrequency Radiation and Cancer. JAMA Oncol., 2022, 8(3), 456-461.
[http://dx.doi.org/10.1001/jamaoncol.2021.5964] [PMID: 34882171]
[32]
Gupta, S.; Sharma, R.S.; Singh, R. Non-ionizing radiation as possible carcinogen. Int. J. Environ. Health Res., 2022, 32(4), 916-940.
[http://dx.doi.org/10.1080/09603123.2020.1806212] [PMID: 32885667]
[33]
Ledda, C.; Rapisarda, V.J.C. Occupational and environmental carcinogenesis. Cancers (Basel)., 2020, 12(9), 2547.
[34]
Alipour, M. Molecular Mechanism of Helicobacter pylori-Induced Gastric Cancer. J. Gastrointest. Cancer, 2021, 52(1), 23-30.
[http://dx.doi.org/10.1007/s12029-020-00518-5] [PMID: 32926335]
[35]
Brianti, P.; De Flammineis, E.; Mercuri, S.R. Review of HPV-related diseases and cancers. New Microbiol., 2017, 40(2), 80-85.
[PMID: 28368072]
[36]
Mazziotta, C.; Lanzillotti, C.; Gafà, R.; Touzé, A.; Durand, M.A.; Martini, F.; Rotondo, J.C. The role of histone post-translational modifications in Merkel cell carcinoma. Front. Oncol., 2022, 12, 832047.
[http://dx.doi.org/10.3389/fonc.2022.832047] [PMID: 35350569]
[37]
Fujita, S.; Kotake, K. [Chemotherapy]. Jpn. J. Clin. Med., 2014, 72(1), 102-107.
[PMID: 24597356]
[38]
Hughes, J.R.; Parsons, J.L. FLASH Radiotherapy: Current Knowledge and Future Insights Using Proton-Beam Therapy. Int. J. Mol. Sci., 2020, 21(18), 6492.
[http://dx.doi.org/10.3390/ijms21186492] [PMID: 32899466]
[39]
Minniti, G.; Goldsmith, C.; Brada, M. Radiotherapy. Handb. Clin. Neurol., 2012, 104, 215-228.
[http://dx.doi.org/10.1016/B978-0-444-52138-5.00016-5] [PMID: 22230446]
[40]
Villet, R. The surgery and surgeons of tomorrow in the treatment of cancer. J. Visc. Surg., 2021, 158(6), 459-461.
[http://dx.doi.org/10.1016/j.jviscsurg.2021.11.011] [PMID: 34876252]
[41]
Abbott, M.; Ustoyev, Y. Cancer and the Immune System: The History and Background of Immunotherapy. Semin. Oncol. Nurs., 2019, 35(5), 150923.
[http://dx.doi.org/10.1016/j.soncn.2019.08.002] [PMID: 31526550]
[42]
Bhatia, K.; Bhumika; Das, A. Combinatorial drug therapy in cancer - New insights. Life Sci., 2020, 258, 118134.
[http://dx.doi.org/10.1016/j.lfs.2020.118134] [PMID: 32717272]
[43]
Jones, E.; Nissen, L.; McCarthy, A.; Steadman, K.; Windsor, C. Exploring the Use of Complementary and Alternative Medicine in Cancer Patients. Integr. Cancer Ther., 2019, 18
[http://dx.doi.org/10.1177/1534735419854134] [PMID: 31170844]
[44]
Deng, G. Integrative Medicine Therapies for Pain Management in Cancer Patients. Cancer J., 2019, 25(5), 343-348.
[http://dx.doi.org/10.1097/PPO.0000000000000399] [PMID: 31567462]
[45]
Forster, T.H.; Stoffel, F.; Gasser, T.C. Hormone therapy in advanced prostate cancer. Front. Radiat. Ther. Oncol., 2002, 36, 49-65.
[http://dx.doi.org/10.1159/000061329] [PMID: 11842755]
[46]
Dalmau, E.; Armengol-Alonso, A.; Muñoz, M.; Seguí-Palmer, M.Á. Current status of hormone therapy in patients with hormone receptor positive (HR+) advanced breast cancer. Breast, 2014, 23(6), 710-720.
[http://dx.doi.org/10.1016/j.breast.2014.09.006] [PMID: 25311296]
[47]
Sawai, H.; Ueno, S.; Yamaguchi, Y.; Suzuki, Y.; Murata, A.; Suganuma, E.; Yamamoto, K.; Kuzuya, H.; Koide, S.; Kurimoto, M.; Yanagi, T.; Koide, H.; Kamiya, A. Hyperthermia with Chemotherapy for Unresectable Gastric Cancer in a Patient with a Vagus Nerve Stimulator Implant: A Case Report. Am. J. Case Rep., 2021, 22, e931564.
[http://dx.doi.org/10.12659/AJCR.931564] [PMID: 34400601]
[48]
Notter, M.; Thomsen, A.R.; Grosu, A.L.; Vaupel, P. Recommendation of Regional Hyperthermia in the Treatment of Breast Cancer. Integr. Cancer Ther., 2021, 20
[http://dx.doi.org/10.1177/1534735420988606] [PMID: 33467939]
[49]
Zhang, Q.; Li, L. Photodynamic combinational therapy in cancer treatment. J. BUON, 2018, 23(3), 561-567.
[PMID: 30003719]
[50]
Duong, M.T.Q.; Qin, Y.; You, S.H.; Min, J.J. Bacteria-cancer interactions: Bacteria-based cancer therapy. Exp. Mol. Med., 2019, 51(12), 1-15.
[http://dx.doi.org/10.1038/s12276-019-0297-0] [PMID: 31827064]
[51]
Morales, M.; Xue, X. Targeting iron metabolism in cancer therapy. Theranostics, 2021, 11(17), 8412-8429.
[http://dx.doi.org/10.7150/thno.59092] [PMID: 34373750]
[52]
Shanbhag, V.C.; Gudekar, N.; Jasmer, K.; Papageorgiou, C.; Singh, K.; Petris, M.J. Copper metabolism as a unique vulnerability in cancer. Biochim. Biophys. Acta Mol. Cell Res., 2021, 1868(2), 118893.
[http://dx.doi.org/10.1016/j.bbamcr.2020.118893] [PMID: 33091507]
[53]
Neradil, J.; Pavlasova, G.; Veselska, R. New mechanisms for an old drug; DHFR- and non-DHFR-mediated effects of methotrexate in cancer cells. Klin Onkol., 2012, 25(Suppl 2), 2S87-92.
[54]
Ayati, A.; Moghimi, S.; Toolabi, M.; Foroumadi, A. Pyrimidine-based EGFR TK inhibitors in targeted cancer therapy. Eur. J. Med. Chem., 2021, 221, 113523.
[http://dx.doi.org/10.1016/j.ejmech.2021.113523] [PMID: 33992931]
[55]
Kowalska, A.; Pluta, K.; Latocha, M. Synthesis and anticancer activity of multisubstituted purines and xanthines with one or two propynylthio and aminobutynylthio groups. Med. Chem. Res., 2018, 27(5), 1384-1395.
[http://dx.doi.org/10.1007/s00044-018-2155-3] [PMID: 29706750]
[56]
Singh, R.K.; Kumar, S.; Prasad, D.N.; Bhardwaj, T.R. Therapeutic journery of nitrogen mustard as alkylating anticancer agents: Historic to future perspectives. Eur. J. Med. Chem., 2018, 151, 401-433.
[http://dx.doi.org/10.1016/j.ejmech.2018.04.001] [PMID: 29649739]
[57]
Venugopal, S.; Sharma, V.; Mehra, A.; Singh, I.; Singh, G. DNA intercalators as anticancer agents. Chem. Biol. Drug Des., 2022, 100(4), 580-598.
[http://dx.doi.org/10.1111/cbdd.14116] [PMID: 35822451]
[58]
Al-Balas, Q.A.; Al-Sha’er, M.A.; Hassan, M.A.; Al Zou’bi, E. Identification of the First “Two Digit Nano-molar” Inhibitors of the Human Glyoxalase-I Enzyme as Potential Anticancer Agents. Med. Chem., 2022, 18(4), 473-483.
[http://dx.doi.org/10.2174/1573406417666210714170403] [PMID: 34264188]
[59]
Varghese, R.; Dalvi, Y.B. Natural Products as Anticancer Agents. Curr. Drug Targets, 2021, 22(11), 1272-1287.
[http://dx.doi.org/10.2174/1389450121999201230204526] [PMID: 33390130]
[60]
Lazo, J.S.; Sharlow, E.R. Drugging undruggable molecular cancer targets. Annu. Rev. Pharmacol. Toxicol., 2016, 56(1), 23-40.
[http://dx.doi.org/10.1146/annurev-pharmtox-010715-103440] [PMID: 26527069]
[61]
Hopkins, A.L. Network pharmacology. Nat. Biotechnol., 2007, 25(10), 1110-1111.
[http://dx.doi.org/10.1038/nbt1007-1110] [PMID: 17921993]
[62]
Chen, X.; Liu, M.X.; Yan, G.Y. Drug–target interaction prediction by random walk on the heterogeneous network. Mol. Biosyst., 2012, 8(7), 1970-1978.
[http://dx.doi.org/10.1039/c2mb00002d] [PMID: 22538619]
[63]
Ghofrani, H.A.; Osterloh, I.H.; Grimminger, F. Sildenafil: From angina to erectile dysfunction to pulmonary hypertension and beyond. Nat. Rev. Drug Discov., 2006, 5(8), 689-702.
[http://dx.doi.org/10.1038/nrd2030] [PMID: 16883306]
[64]
Takarabe, M.; Kotera, M.; Nishimura, Y.; Goto, S.; Yamanishi, Y. Drug target prediction using adverse event report systems: A pharmacogenomic approach. Bioinformatics, 2012, 28(18), i611-i618.
[http://dx.doi.org/10.1093/bioinformatics/bts413] [PMID: 22962489]
[65]
Chen, X.; Yan, C.C.; Zhang, X.; Zhang, X.; Dai, F.; Yin, J.; Zhang, Y. Drug–target interaction prediction: Databases, web servers and computational models. Brief. Bioinform., 2016, 17(4), 696-712.
[http://dx.doi.org/10.1093/bib/bbv066] [PMID: 26283676]
[66]
Campillos, M.; Kuhn, M.; Gavin, A.C.; Jensen, L.J.; Bork, P. Drug target identification using side-effect similarity. Science, 2008, 321(5886), 263-266.
[http://dx.doi.org/10.1126/science.1158140] [PMID: 18621671]
[67]
Klipp, E.; Wade, R.C.; Kummer, U. Biochemical network-based drug-target prediction. Curr. Opin. Biotechnol., 2010, 21(4), 511-516.
[http://dx.doi.org/10.1016/j.copbio.2010.05.004] [PMID: 20554441]
[68]
Lee, K.; Shin, W.; Kim, B.; Lee, S.; Choi, Y.; Kim, S.; Jeon, M.; Tan, A.C.; Kang, J. HiPub: Translating PubMed and PMC texts to networks for knowledge discovery. Bioinformatics, 2016, 32(18), 2886-2888.
[http://dx.doi.org/10.1093/bioinformatics/btw511] [PMID: 27485446]
[69]
Srivastava, N. Dropout: A simple way to prevent neural networks from overfitting. J. Mach. Learn. Res., 2014, 15(1), 1929-1958.
[70]
Li, X.; Chen, H. Recommendation as link prediction in bipartite graphs: A graph kernel-based machine learning approach. Decis. Support Syst., 2013, 54(2), 880-890.
[http://dx.doi.org/10.1016/j.dss.2012.09.019]
[71]
Yıldırım, M.A.; Goh, K.I.; Cusick, M.E.; Barabási, A.L.; Vidal, M. Drug—target network. Nat. Biotechnol., 2007, 25(10), 1119-1126.
[http://dx.doi.org/10.1038/nbt1338] [PMID: 17921997]
[72]
Mayr, A.; Klambauer, G.; Unterthiner, T.; Steijaert, M.; Wegner, J.K.; Ceulemans, H.; Clevert, D.A.; Hochreiter, S. Large-scale comparison of machine learning methods for drug target prediction on ChEMBL. Chem. Sci. (Camb.), 2018, 9(24), 5441-5451.
[http://dx.doi.org/10.1039/C8SC00148K] [PMID: 30155234]
[73]
Wang, J.L.; Liu, D.; Zhang, Z.J.; Shan, S.; Han, X.; Srinivasula, S.M.; Croce, C.M.; Alnemri, E.S.; Huang, Z. Structure-based discovery of an organic compound that binds Bcl-2 protein and induces apoptosis of tumor cells. Proc. Natl. Acad. Sci. USA, 2000, 97(13), 7124-7129.
[http://dx.doi.org/10.1073/pnas.97.13.7124] [PMID: 10860979]
[74]
Prada-Gracia, D.; Huerta-Yépez, S.; Moreno-Vargas, L.M. Application of computational methods for anticancer drug discovery, design, and optimization. Bol. Méd. Hosp. Infant. México, 2016, 73(6), 411-423.
[http://dx.doi.org/10.1016/j.bmhimx.2016.10.006] [PMID: 29421286]
[75]
Lu, P. Computer-aided drug discovery. Springer: Cham, 2018; pp. 7-24.
[76]
Urwyler, S. Allosteric modulation of family C G-protein-coupled receptors: From molecular insights to therapeutic perspectives. Pharmacol. Rev., 2011, 63(1), 59-126.
[http://dx.doi.org/10.1124/pr.109.002501] [PMID: 21228259]
[77]
Anthony, C.S.; Masuyer, G.; Sturrock, E.D.; Acharya, K.R. Structure based drug design of angiotensin-I converting enzyme inhibitors. Curr. Med. Chem., 2012, 19(6), 845-855.
[http://dx.doi.org/10.2174/092986712799034950] [PMID: 22214449]
[78]
Debnath, S.; Kanakaraju, M.; Islam, M.; Yeeravalli, R.; Sen, D.; Das, A. in silico design, synthesis and activity of potential drug-like chrysin scaffold-derived selective EGFR inhibitors as anticancer agents. Comput. Biol. Chem., 2019, 83, 107156.
[http://dx.doi.org/10.1016/j.compbiolchem.2019.107156] [PMID: 31710991]
[79]
Hong, J.Y.; Price, I.R.; Bai, J.J.; Lin, H. A glycoconjugated SIRT2 inhibitor with aqueous solubility allows structure-based design of SIRT2 inhibitors. ACS Chem. Biol., 2019, 14(8), 1802-1810.
[http://dx.doi.org/10.1021/acschembio.9b00384] [PMID: 31373792]
[80]
Mendoza, J.L.; Escalante, N.K.; Jude, K.M.; Sotolongo Bellon, J.; Su, L.; Horton, T.M.; Tsutsumi, N.; Berardinelli, S.J.; Haltiwanger, R.S.; Piehler, J.; Engleman, E.G.; Garcia, K.C. Structure of the IFNγ receptor complex guides design of biased agonists. Nature, 2019, 567(7746), 56-60.
[http://dx.doi.org/10.1038/s41586-019-0988-7] [PMID: 30814731]
[81]
Itoh, Y. Drug discovery researches on modulators of lysine-modifying enzymes based on strategic chemistry approaches. Chem. Pharm. Bull. (Tokyo), 2020, 68(1), 34-45.
[http://dx.doi.org/10.1248/cpb.c19-00741] [PMID: 31902900]
[82]
Tondo, A.R.; Caputo, L.; Mangiatordi, G.F.; Monaci, L.; Lentini, G.; Logrieco, A.F.; Montaruli, M.; Nicolotti, O.; Quintieri, L. Structure-based identification and design of angiotensin converting enzyme-inhibitory peptides from whey proteins. J. Agric. Food Chem., 2020, 68(2), 541-548.
[http://dx.doi.org/10.1021/acs.jafc.9b06237] [PMID: 31860295]
[83]
Maia, E.H.B.; Assis, L.C.; de Oliveira, T.A.; da Silva, A.M.; Taranto, A.G. Structure-based virtual screening: From classical to artificial intelligence. Front Chem., 2020, 8, 343.
[http://dx.doi.org/10.3389/fchem.2020.00343] [PMID: 32411671]
[84]
Ferreira, L.G.; Ricardo, N. Dos Santos, Glaucius Oliva, and Adriano D. Andricopulo. Molecules, 2015, 20, 13384-13421.
[http://dx.doi.org/10.3390/molecules200713384] [PMID: 26205061]
[85]
Halperin, I.; Ma, B. Principles of docking: An overview of search algorithms and a guide to scoring functions. Proteins: Struct. Func, 2002, 47, 409-443.
[http://dx.doi.org/10.1002/prot.10115]
[86]
Dias, R.; de Azevedo, W., Jr; Caceres, R.; De Azevedo, W.F., Jr Molecular docking algorithms. Curr. Drug Targets, 2008, 9(12), 1040-1047.
[http://dx.doi.org/10.2174/138945008786949432] [PMID: 19128213]
[87]
Honarparvar, B.; Govender, T.; Maguire, G.E.M.; Soliman, M.E.S.; Kruger, H.G. Integrated approach to structure-based enzymatic drug design: Molecular modeling, spectroscopy, and experimental bioactivity. Chem. Rev., 2014, 114(1), 493-537.
[http://dx.doi.org/10.1021/cr300314q] [PMID: 24024775]
[88]
Ferreira, L.; dos Santos, R.; Oliva, G.; Andricopulo, A. Molecular docking and structure-based drug design strategies. Molecules, 2015, 20(7), 13384-13421.
[http://dx.doi.org/10.3390/molecules200713384] [PMID: 26205061]
[89]
Kortagere, S.; Ekins, S. Troubleshooting computational methods in drug discovery. J. Pharmacol. Toxicol. Methods, 2010, 61(2), 67-75.
[http://dx.doi.org/10.1016/j.vascn.2010.02.005] [PMID: 20176118]
[90]
Yuriev, E.; Holien, J.; Ramsland, P.A. Improvements, trends, and new ideas in molecular docking: 2012-2013 in review. J. Mol. Recognit., 2015, 28(10), 581-604.
[http://dx.doi.org/10.1002/jmr.2471] [PMID: 25808539]
[91]
Yuriev, E.; Agostino, M.; Ramsland, P.A. Challenges and advances in computational docking: 2009 in review. J. Mol. Recognit., 2011, 24(2), 149-164.
[http://dx.doi.org/10.1002/jmr.1077] [PMID: 21360606]
[92]
Pirhadi, S.; Shiri, F.; Ghasemi, J.B. Methods and applications of structure based pharmacophores in drug discovery. Curr. Top. Med. Chem., 2013, 13(9), 1036-1047.
[http://dx.doi.org/10.2174/1568026611313090006] [PMID: 23651482]
[93]
Wolber, G.; Dornhofer, A.A.; Langer, T. Efficient overlay of small organic molecules using 3D pharmacophores. J. Comput. Aided Mol. Des., 2007, 20(12), 773-788.
[http://dx.doi.org/10.1007/s10822-006-9078-7] [PMID: 17051340]
[94]
Ortuso, F.; Langer, T.; Alcaro, S. GBPM: GRID-based pharmacophore model: Concept and application studies to protein–protein recognition. Bioinformatics, 2006, 22(12), 1449-1455.
[http://dx.doi.org/10.1093/bioinformatics/btl115] [PMID: 16567363]
[95]
Chen, J.; Lai, L. Pocket v.2: Further developments on receptor-based pharmacophore modeling. J. Chem. Inf. Model., 2006, 46(6), 2684-2691.
[http://dx.doi.org/10.1021/ci600246s] [PMID: 17125208]
[96]
Lu, X.; Yang, H.; Chen, Y.; Li, Q.; He, S.; Jiang, X.; Feng, F.; Qu, W.; Sun, H. The development of pharmacophore modeling: Generation and recent applications in drug discovery. Curr. Pharm. Des., 2018, 24(29), 3424-3439.
[http://dx.doi.org/10.2174/1381612824666180810162944] [PMID: 30101699]
[97]
Böhm, H-J. The computer program LUDI: A new method for the de novo design of enzyme inhibitors. J. Comput. Aided Mol. Des., 1992, 6(1), 61-78.
[http://dx.doi.org/10.1007/BF00124387] [PMID: 1583540]
[98]
Sanders, M.P.A.; McGuire, R.; Roumen, L.; de Esch, I.J.P.; de Vlieg, J.; Klomp, J.P.G.; de Graaf, C. From the protein’s perspective: The benefits and challenges of protein structure-based pharmacophore modeling. MedChemComm, 2012, 3(1), 28-38.
[http://dx.doi.org/10.1039/C1MD00210D]
[99]
Aparoy, P.; Kumar Reddy, K.; Reddanna, P. Structure and ligand based drug design strategies in the development of novel 5- LOX inhibitors. Curr. Med. Chem., 2012, 19(22), 3763-3778.
[http://dx.doi.org/10.2174/092986712801661112] [PMID: 22680930]
[100]
Rush, T.S., III; Grant, J.A.; Mosyak, L.; Nicholls, A. A shape-based 3-D scaffold hopping method and its application to a bacterial protein-protein interaction. J. Med. Chem., 2005, 48(5), 1489-1495.
[http://dx.doi.org/10.1021/jm040163o] [PMID: 15743191]
[101]
Bologa, C.G.; Revankar, C.M.; Young, S.M.; Edwards, B.S.; Arterburn, J.B.; Kiselyov, A.S.; Parker, M.A.; Tkachenko, S.E.; Savchuck, N.P.; Sklar, L.A.; Oprea, T.I.; Prossnitz, E.R. Virtual and biomolecular screening converge on a selective agonist for GPR30. Nat. Chem. Biol., 2006, 2(4), 207-212.
[http://dx.doi.org/10.1038/nchembio775] [PMID: 16520733]
[102]
Hu, G.; Kuang, G.; Xiao, W.; Li, W.; Liu, G.; Tang, Y. Performance evaluation of 2D fingerprint and 3D shape similarity methods in virtual screening. J. Chem. Inf. Model., 2012, 52(5), 1103-1113.
[http://dx.doi.org/10.1021/ci300030u] [PMID: 22551340]
[103]
Buckle, D.R.; Erhardt, P.W.; Ganellin, C.R.; Kobayashi, T.; Perun, T.J.; Proudfoot, J.; Senn-Bilfinger, J. Glossary of terms used in medicinal chemistry. Part II (IUPAC Recommendations 2013). Pure Appl. Chem., 2013, 85(8), 1725-1758.
[http://dx.doi.org/10.1351/PAC-REC-12-11-23]
[104]
Chao, W.R.; Yean, D.; Amin, K.; Green, C.; Jong, L. Computer-aided rational drug design: A novel agent (SR13668) designed to mimic the unique anticancer mechanisms of dietary indole-3-carbinol to block Akt signaling. J. Med. Chem., 2007, 50(15), 3412-3415.
[http://dx.doi.org/10.1021/jm070040e] [PMID: 17602463]
[105]
Mendenhall, J.; Meiler, J. Improving quantitative structure–activity relationship models using Artificial Neural Networks trained with dropout. J. Comput. Aided Mol. Des., 2016, 30(2), 177-189.
[http://dx.doi.org/10.1007/s10822-016-9895-2] [PMID: 26830599]
[106]
Jeffrey Conn, P.; Christopoulos, A.; Lindsley, C.W. Allosteric modulators of GPCRs: A novel approach for the treatment of CNS disorders. Nat. Rev. Drug Discov., 2009, 8(1), 41-54.
[http://dx.doi.org/10.1038/nrd2760] [PMID: 19116626]
[107]
Tautermann, C.S. GPCR structures in drug design, emerging opportunities with new structures. Bioorg. Med. Chem. Lett., 2014, 24(17), 4073-4079.
[http://dx.doi.org/10.1016/j.bmcl.2014.07.009] [PMID: 25086683]
[108]
Flock, T.; Ravarani, C.N.J.; Sun, D.; Venkatakrishnan, A.J.; Kayikci, M.; Tate, C.G.; Veprintsev, D.B.; Babu, M.M. Universal allosteric mechanism for Gα activation by GPCRs. Nature, 2015, 524(7564), 173-179.
[http://dx.doi.org/10.1038/nature14663] [PMID: 26147082]
[109]
DeVree, B.T.; Mahoney, J.P.; Vélez-Ruiz, G.A.; Rasmussen, S.G.F.; Kuszak, A.J.; Edwald, E.; Fung, J.J.; Manglik, A.; Masureel, M.; Du, Y.; Matt, R.A.; Pardon, E.; Steyaert, J.; Kobilka, B.K.; Sunahara, R.K. Allosteric coupling from G protein to the agonist-binding pocket in GPCRs. Nature, 2016, 535(7610), 182-186.
[http://dx.doi.org/10.1038/nature18324] [PMID: 27362234]
[110]
Sabbadin, D.; Moro, S. Supervised molecular dynamics (SuMD) as a helpful tool to depict GPCR-ligand recognition pathway in a nanosecond time scale. J. Chem. Inf. Model., 2014, 54(2), 372-376.
[http://dx.doi.org/10.1021/ci400766b] [PMID: 24456045]
[111]
Deganutti, G.; Cuzzolin, A.; Ciancetta, A.; Moro, S. Understanding allosteric interactions in G protein-coupled receptors using Supervised Molecular Dynamics: A prototype study analysing the human A3 adenosine receptor positive allosteric modulator LUF6000. Bioorg. Med. Chem., 2015, 23(14), 4065-4071.
[http://dx.doi.org/10.1016/j.bmc.2015.03.039] [PMID: 25868747]
[112]
Cuzzolin, A.; Sturlese, M.; Deganutti, G.; Salmaso, V.; Sabbadin, D.; Ciancetta, A.; Moro, S. Deciphering the complexity of ligand–protein recognition pathways using supervised molecular dynamics (SuMD) simulations. J. Chem. Inf. Model., 2016, 56(4), 687-705.
[http://dx.doi.org/10.1021/acs.jcim.5b00702] [PMID: 27019343]
[113]
Hancock, J.F. Ras proteins: Different signals from different locations. Nat. Rev. Mol. Cell Biol., 2003, 4(5), 373-385.
[http://dx.doi.org/10.1038/nrm1105] [PMID: 12728271]
[114]
Tong, M.; Seeliger, M.A. Targeting conformational plasticity of protein kinases. ACS Chem. Biol., 2015, 10(1), 190-200.
[http://dx.doi.org/10.1021/cb500870a] [PMID: 25486330]
[115]
Mandlik, V.; Bejugam, P.R.; Singh, S. Application of artificial neural networks in modern drug discovery.Artificial neural network for drug design, delivery and disposition; Elsevier: Amsterdam, 2016, pp. 123-139.
[http://dx.doi.org/10.1016/B978-0-12-801559-9.00006-5]
[116]
Schaefer, A.J. Modeling medical treatment using Markov decision processes. In: Operations Research and Health Care; Springer: Cham, 2004.
[117]
McEntire, R.; Szalkowski, D.; Butler, J.; Kuo, M.S.; Chang, M.; Chang, M.; Freeman, D.; McQuay, S.; Patel, J.; McGlashen, M.; Cornell, W.D.; Xu, J.J. Application of an automated natural language processing (NLP) workflow to enable federated search of external biomedical content in drug discovery and development. Drug Discov. Today, 2016, 21(5), 826-835.
[http://dx.doi.org/10.1016/j.drudis.2016.03.006] [PMID: 26979546]
[118]
Muhsin, M.; Graham, J.; Kirkpatrick, P. Gefitinib. Nat. Rev. Cancer, 2003, 3(8), 556-557.
[http://dx.doi.org/10.1038/nrc1159]
[119]
Wilhelm, S.; Carter, C.; Lynch, M.; Lowinger, T.; Dumas, J.; Smith, R.A.; Schwartz, B.; Simantov, R.; Kelley, S. Discovery and development of sorafenib: A multikinase inhibitor for treating cancer. Nat. Rev. Drug Discov., 2006, 5(10), 835-844.
[http://dx.doi.org/10.1038/nrd2130] [PMID: 17016424]
[120]
Grünwald, V.; Hidalgo, M. Development of the epidermal growth factor receptor inhibitor OSI-774.Seminars in oncology; Elsevier: Amsterdam, 2003.
[http://dx.doi.org/10.1007/978-1-4615-0081-0_19]
[121]
Jarman, M.; Barrie, S.E.; Llera, J.M. The 16,17-double bond is needed for irreversible inhibition of human cytochrome p45017α by abiraterone (17-(3-pyridyl)androsta-5, 16-dien-3β-ol) and related steroidal inhibitors. J. Med. Chem., 1998, 41(27), 5375-5381.
[http://dx.doi.org/10.1021/jm981017j] [PMID: 9876107]
[122]
Wood, E.R.; Truesdale, A.T.; McDonald, O.B.; Yuan, D.; Hassell, A.; Dickerson, S.H.; Ellis, B.; Pennisi, C.; Horne, E.; Lackey, K.; Alligood, K.J.; Rusnak, D.W.; Gilmer, T.M.; Shewchuk, L. A unique structure for epidermal growth factor receptor bound to GW572016 (Lapatinib): Relationships among protein conformation, inhibitor off-rate, and receptor activity in tumor cells. Cancer Res., 2004, 64(18), 6652-6659.
[http://dx.doi.org/10.1158/0008-5472.CAN-04-1168] [PMID: 15374980]
[123]
Butrynski, J.E.; D’Adamo, D.R.; Hornick, J.L.; Dal Cin, P.; Antonescu, C.R.; Jhanwar, S.C.; Ladanyi, M.; Capelletti, M.; Rodig, S.J.; Ramaiya, N.; Kwak, E.L.; Clark, J.W.; Wilner, K.D.; Christensen, J.G.; Jänne, P.A.; Maki, R.G.; Demetri, G.D.; Shapiro, G.I. Crizotinib in ALK-rearranged inflammatory myofibroblastic tumor. N. Engl. J. Med., 2010, 363(18), 1727-1733.
[http://dx.doi.org/10.1056/NEJMoa1007056] [PMID: 20979472]
[124]
Reker, D.; Rodrigues, T.; Schneider, P.; Schneider, G. Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus. Proc. Natl. Acad. Sci. USA, 2014, 111(11), 4067-4072.
[http://dx.doi.org/10.1073/pnas.1320001111] [PMID: 24591595]
[125]
Rodrigues, T.; Werner, M.; Roth, J.; da Cruz, E.H.G.; Marques, M.C.; Akkapeddi, P.; Lobo, S.A.; Koeberle, A.; Corzana, F.; da Silva Júnior, E.N.; Werz, O.; Bernardes, G.J.L. Machine intelligence decrypts β-lapachone as an allosteric 5-lipoxygenase inhibitor. Chem. Sci. (Camb.), 2018, 9(34), 6899-6903.
[http://dx.doi.org/10.1039/C8SC02634C] [PMID: 30310622]
[126]
Gómez-Bombarelli, R.; Wei, J.N.; Duvenaud, D.; Hernández-Lobato, J.M.; Sánchez-Lengeling, B.; Sheberla, D.; Aguilera-Iparraguirre, J.; Hirzel, T.D.; Adams, R.P.; Aspuru-Guzik, A. Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent. Sci., 2018, 4(2), 268-276.
[http://dx.doi.org/10.1021/acscentsci.7b00572] [PMID: 29532027]
[127]
Born, J. PaccMannRL: Designing anticancer drugs from transcriptomic data via reinforcement learning. 24th Annual International Conference, RECOMB 2020, Padua, ItalyMay 10–13, 2020
[http://dx.doi.org/10.1007/978-3-030-45257-5_18]
[128]
Ferreira, A. Developing novel anticancer drug candidates regarding the integration of three main knowledge fields: Computer-aided drug design, chemical synthesis, and pharmacological evaluation. J. Drug Res., 2017, 4(2), 1035.
[129]
Kumar, V.; Krishna, S.; Siddiqi, M.I. Virtual screening strategies: Recent advances in the identification and design of anti-cancer agents. Methods, 2015, 71, 64-70.
[http://dx.doi.org/10.1016/j.ymeth.2014.08.010] [PMID: 25171960]
[130]
Wilson, G.M.; Muftuoglu, Y. Computational strategies in cancer drug discovery.Advances in Cancer Management; IntechOpen Limited: London, 2012.
[131]
Mustata, G.; Follis, A.V.; Hammoudeh, D.I.; Metallo, S.J.; Wang, H.; Prochownik, E.V.; Lazo, J.S.; Bahar, I. Discovery of novel Myc-Max heterodimer disruptors with a three-dimensional pharmacophore model. J. Med. Chem., 2009, 52(5), 1247-1250.
[http://dx.doi.org/10.1021/jm801278g] [PMID: 19215087]
[132]
Mottamal, M.; Zheng, S.; Huang, T.; Wang, G. Histone deacetylase inhibitors in clinical studies as templates for new anticancer agents. Molecules, 2015, 20(3), 3898-3941.
[http://dx.doi.org/10.3390/molecules20033898] [PMID: 25738536]
[133]
Seo, S.Y. Multi-targeted hybrids based on HDAC inhibitors for anti-cancer drug discovery. Arch. Pharm. Res., 2012, 35(2), 197-200.
[http://dx.doi.org/10.1007/s12272-012-0221-9] [PMID: 22370774]
[134]
Geromichalos, G.D. Importance of molecular computer modeling in anticancer drug development. J. BUON, 2007, 12(1)(Suppl. 1), S101-S118.
[PMID: 17935268]
[135]
Marin-Sanguino, A. Biochemical pathway modeling tools for drug target detection in cancer and other complex diseases.Methods in Enzymology; Elsevier: Amsterdam, 2011, pp. 319-369.
[136]
Shaikh, N.; Sharma, M.; Garg, P. An improved approach for predicting drug–target interaction: Proteochemometrics to molecular docking. Mol. Biosyst., 2016, 12(3), 1006-1014.
[http://dx.doi.org/10.1039/C5MB00650C] [PMID: 26822863]
[137]
Shim, J.S.; Liu, J.O. Recent advances in drug repositioning for the discovery of new anticancer drugs. Int. J. Biol. Sci., 2014, 10(7), 654-663.
[http://dx.doi.org/10.7150/ijbs.9224] [PMID: 25013375]
[138]
Chong, C.R.; Xu, J.; Lu, J.; Bhat, S.; Sullivan, D.J., Jr; Liu, J.O. Inhibition of angiogenesis by the antifungal drug itraconazole. ACS Chem. Biol., 2007, 2(4), 263-270.
[http://dx.doi.org/10.1021/cb600362d] [PMID: 17432820]
[139]
Hassan Baig, M.; Ahmad, K.; Roy, S.; Mohammad Ashraf, J.; Adil, M.; Haris Siddiqui, M.; Khan, S.; Amjad Kamal, M.; Provazník, I.; Choi, I. Computer aided drug design: Success and limitations. Curr. Pharm. Des., 2016, 22(5), 572-581.
[http://dx.doi.org/10.2174/1381612822666151125000550] [PMID: 26601966]
[140]
Altevogt, B.M. Improving and accelerating therapeutic development for nervous system disorders: Workshop summary; National Academies Press: Washington, D.C, 2014.
[141]
Christensen, J.G.; Burrows, J.; Salgia, R. c-Met as a target for human cancer and characterization of inhibitors for therapeutic intervention. Cancer Lett., 2005, 225(1), 1-26.
[http://dx.doi.org/10.1016/j.canlet.2004.09.044] [PMID: 15922853]
[142]
Peruzzi, B.; Bottaro, D.P. Targeting the c-Met signaling pathway in cancer. Clin. Cancer Res., 2006, 12(12), 3657-3660.
[http://dx.doi.org/10.1158/1078-0432.CCR-06-0818] [PMID: 16778093]
[143]
Liu, X.; Yao, W.; Newton, R.C.; Scherle, P.A. Targeting the c-MET signaling pathway for cancer therapy. Expert Opin. Investig. Drugs, 2008, 17(7), 997-1011.
[http://dx.doi.org/10.1517/13543784.17.7.997] [PMID: 18549337]
[144]
Meadows, K.L.; Hurwitz, H.I. Anti-VEGF therapies in the clinic. Cold Spring Harb. Perspect. Med., 2012, 2(10), a006577.
[http://dx.doi.org/10.1101/cshperspect.a006577] [PMID: 23028128]
[145]
Kania, R.S. Structure-Based Design and Characterization of Axitinib.Kinase Inhibitor Drugs; Wiley: Hoboken, New Jersey, 2009.
[http://dx.doi.org/10.1002/9780470524961.ch7]
[146]
Faucette, S.; Wagh, S.; Trivedi, A.; Venkatakrishnan, K.; Gupta, N. Reverse translation of US Food and Drug Administration reviews of oncology new molecular entities approved in 2011–2017: Lessons learned for anticancer drug development. Clin. Transl. Sci., 2018, 11(2), 123-146.
[http://dx.doi.org/10.1111/cts.12527] [PMID: 29266809]
[147]
Whitesell, L.; Lindquist, S.L. HSP90 and the chaperoning of cancer. Nat. Rev. Cancer, 2005, 5(10), 761-772.
[http://dx.doi.org/10.1038/nrc1716] [PMID: 16175177]
[148]
Workman, P.; Burrows, F.; Neckers, L.; Rosen, N. Drugging the cancer chaperone HSP90: Combinatorial therapeutic exploitation of oncogene addiction and tumor stress. Ann. N. Y. Acad. Sci., 2007, 1113(1), 202-216.
[http://dx.doi.org/10.1196/annals.1391.012] [PMID: 17513464]
[149]
Pearl, L.H.; Prodromou, C. Structure and mechanism of the Hsp90 molecular chaperone machinery. Annu. Rev. Biochem., 2006, 75(1), 271-294.
[http://dx.doi.org/10.1146/annurev.biochem.75.103004.142738] [PMID: 16756493]
[150]
Cheung, K.M.J.; Matthews, T.P.; James, K.; Rowlands, M.G.; Boxall, K.J.; Sharp, S.Y.; Maloney, A.; Roe, S.M.; Prodromou, C.; Pearl, L.H.; Aherne, G.W.; McDonald, E.; Workman, P. The identification, synthesis, protein crystal structure and in vitro biochemical evaluation of a new 3,4-diarylpyrazole class of Hsp90 inhibitors. Bioorg. Med. Chem. Lett., 2005, 15(14), 3338-3343.
[http://dx.doi.org/10.1016/j.bmcl.2005.05.046] [PMID: 15955698]
[151]
Smith, N.F.; Hayes, A.; James, K.; Nutley, B.P.; McDonald, E.; Henley, A.; Dymock, B.; Drysdale, M.J.; Raynaud, F.I.; Workman, P. Preclinical pharmacokinetics and metabolism of a novel diaryl pyrazole resorcinol series of heat shock protein 90 inhibitors. Mol. Cancer Ther., 2006, 5(6), 1628-1637.
[http://dx.doi.org/10.1158/1535-7163.MCT-06-0041] [PMID: 16818523]
[152]
Felip, E.; Barlesi, F.; Besse, B.; Chu, Q.; Gandhi, L.; Kim, S.W.; Carcereny, E.; Sequist, L.V.; Brunsvig, P.; Chouaid, C.; Smit, E.F.; Groen, H.J.M.; Kim, D.W.; Park, K.; Avsar, E.; Szpakowski, S.; Akimov, M.; Garon, E.B. Phase 2 Study of the HSP-90 Inhibitor AUY922 in previously treated and molecularly defined patients with advanced non–small cell lung cancer. J. Thorac. Oncol., 2018, 13(4), 576-584.
[http://dx.doi.org/10.1016/j.jtho.2017.11.131] [PMID: 29247830]
[153]
Jorge, S.E.; Lucena-Araujo, A.R.; Yasuda, H.; Piotrowska, Z.; Oxnard, G.R.; Rangachari, D.; Huberman, M.S.; Sequist, L.V.; Kobayashi, S.S.; Costa, D.B. EGFR Exon 20 Insertion Mutations Display Sensitivity to Hsp90 Inhibition in Preclinical Models and Lung Adenocarcinomas. Clin. Cancer Res., 2018, 24(24), 6548-6555.
[http://dx.doi.org/10.1158/1078-0432.CCR-18-1541] [PMID: 30154228]
[154]
Piotrowska, Z.; Costa, D.B.; Oxnard, G.R.; Huberman, M.; Gainor, J.F.; Lennes, I.T.; Muzikansky, A.; Shaw, A.T.; Azzoli, C.G.; Heist, R.S.; Sequist, L.V. Activity of the Hsp90 inhibitor luminespib among non-small-cell lung cancers harboring EGFR exon 20 insertions. Ann. Oncol., 2018, 29(10), 2092-2097.
[http://dx.doi.org/10.1093/annonc/mdy336] [PMID: 30351341]
[155]
Rong, B.; Yang, S. Molecular mechanism and targeted therapy of Hsp90 involved in lung cancer: New discoveries and developments (Review). Int. J. Oncol., 2018, 52(2), 321-336.
[PMID: 29207057]
[156]
Johnson, C.O.; Nguyen, M.; Roth, G.A.; Nichols, E.; Alam, T.; Abate, D.; Abd-Allah, F.; Abdelalim, A.; Abraha, H.N.; Abu-Rmeileh, N.M.E.; Adebayo, O.M.; Adeoye, A.M.; Agarwal, G.; Agrawal, S.; Aichour, A.N.; Aichour, I.; Aichour, M.T.E.; Alahdab, F.; Ali, R.; Alvis-Guzman, N.; Anber, N.H.; Anjomshoa, M.; Arabloo, J.; Arauz, A.; Ärnlöv, J.; Arora, A.; Awasthi, A.; Banach, M.; Barboza, M.A.; Barker-Collo, S.L.; Bärnighausen, T.W.; Basu, S.; Belachew, A.B.; Belayneh, Y.M.; Bennett, D.A.; Bensenor, I.M.; Bhattacharyya, K.; Biadgo, B.; Bijani, A.; Bikbov, B.; Bin Sayeed, M.S.; Butt, Z.A.; Cahuana-Hurtado, L.; Carrero, J.J.; Carvalho, F.; Castañeda-Orjuela, C.A.; Castro, F.; Catalá-López, F.; Chaiah, Y.; Chiang, P.P-C.; Choi, J-Y.J.; Christensen, H.; Chu, D-T.; Cortinovis, M.; Damasceno, A.A.M.; Dandona, L.; Dandona, R.; Daryani, A.; Davletov, K.; de Courten, B.; De la Cruz-Góngora, V.; Degefa, M.G.; Dharmaratne, S.D.; Diaz, D.; Dubey, M.; Duken, E.E.; Edessa, D.; Endres, M.; Faraon, E.J.A.; Farzadfar, F.; Fernandes, E.; Fischer, F.; Flor, L.S.; Ganji, M.; Gebre, A.K.; Gebremichael, T.G.; Geta, B.; Gezae, K.E.; Gill, P.S.; Gnedovskaya, E.V.; Gómez-Dantés, H.; Goulart, A.C.; Grosso, G.; Guo, Y.; Gupta, R.; Haj-Mirzaian, A.; Haj-Mirzaian, A.; Hamidi, S.; Hankey, G.J.; Hassen, H.Y.; Hay, S.I.; Hegazy, M.I.; Heidari, B.; Herial, N.A.; Hosseini, M.A.; Hostiuc, S.; Irvani, S.S.N.; Islam, S.M.S.; Jahanmehr, N.; Javanbakht, M.; Jha, R.P.; Jonas, J.B.; Jozwiak, J.J.; Jürisson, M.; Kahsay, A.; Kalani, R.; Kalkonde, Y.; Kamil, T.A.; Kanchan, T.; Karch, A.; Karimi, N.; Karimi-Sari, H.; Kasaeian, A.; Kassa, T.D.; Kazemeini, H.; Kefale, A.T.; Khader, Y.S.; Khalil, I.A.; Khan, E.A.; Khang, Y-H.; Khubchandani, J.; Kim, D.; Kim, Y.J.; Kisa, A.; Kivimäki, M.; Koyanagi, A.; Krishnamurthi, R.K.; Kumar, G.A.; Lafranconi, A.; Lewington, S.; Li, S.; Lo, W.D.; Lopez, A.D.; Lorkowski, S.; Lotufo, P.A.; Mackay, M.T.; Majdan, M.; Majdzadeh, R.; Majeed, A.; Malekzadeh, R.; Manafi, N.; Mansournia, M.A.; Mehndiratta, M.M.; Mehta, V.; Mengistu, G.; Meretoja, A.; Meretoja, T.J.; Miazgowski, B.; Miazgowski, T.; Miller, T.R.; Mirrakhimov, E.M.; Mohajer, B.; Mohammad, Y.; Mohammadoo-khorasani, M.; Mohammed, S.; Mohebi, F.; Mokdad, A.H.; Mokhayeri, Y.; Moradi, G.; Morawska, L.; Moreno Velásquez, I.; Mousavi, S.M.; Muhammed, O.S.S.; Muruet, W.; Naderi, M.; Naghavi, M.; Naik, G.; Nascimento, B.R.; Negoi, R.I.; Nguyen, C.T.; Nguyen, L.H.; Nirayo, Y.L.; Norrving, B.; Noubiap, J.J.; Ofori-Asenso, R.; Ogbo, F.A.; Olagunju, A.T.; Olagunju, T.O.; Owolabi, M.O.; Pandian, J.D.; Patel, S.; Perico, N.; Piradov, M.A.; Polinder, S.; Postma, M.J.; Poustchi, H.; Prakash, V.; Qorbani, M.; Rafiei, A.; Rahim, F.; Rahimi, K.; Rahimi-Movaghar, V.; Rahman, M.; Rahman, M.A.; Reis, C.; Remuzzi, G.; Renzaho, A.M.N.; Ricci, S.; Roberts, N.L.S.; Robinson, S.R.; Roever, L.; Roshandel, G.; Sabbagh, P.; Safari, H.; Safari, S.; Safiri, S.; Sahebkar, A.; Salehi Zahabi, S.; Samy, A.M.; Santalucia, P.; Santos, I.S.; Santos, J.V.; Santric Milicevic, M.M.; Sartorius, B.; Sawant, A.R.; Schutte, A.E.; Sepanlou, S.G.; Shafieesabet, A.; Shaikh, M.A.; Shams-Beyranvand, M.; Sheikh, A.; Sheth, K.N.; Shibuya, K.; Shigematsu, M.; Shin, M-J.; Shiue, I.; Siabani, S.; Sobaih, B.H.; Sposato, L.A.; Sutradhar, I.; Sylaja, P.N.; Szoeke, C.E.I.; Te Ao, B.J.; Temsah, M-H.; Temsah, O.; Thrift, A.G.; Tonelli, M.; Topor-Madry, R.; Tran, B.X.; Tran, K.B.; Truelsen, T.C.; Tsadik, A.G.; Ullah, I.; Uthman, O.A.; Vaduganathan, M.; Valdez, P.R.; Vasankari, T.J.; Vasanthan, R.; Venketasubramanian, N.; Vosoughi, K.; Vu, G.T.; Waheed, Y.; Weiderpass, E.; Weldegwergs, K.G.; Westerman, R.; Wolfe, C.D.A.; Wondafrash, D.Z.; Xu, G.; Yadollahpour, A.; Yamada, T.; Yatsuya, H.; Yimer, E.M.; Yonemoto, N.; Yousefifard, M.; Yu, C.; Zaidi, Z.; Zamani, M.; Zarghi, A.; Zhang, Y.; Zodpey, S.; Feigin, V.L.; Vos, T.; Murray, C.J.L. Global, regional, and national burden of stroke, 1990–2016: A systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol., 2019, 18(5), 439-458.
[http://dx.doi.org/10.1016/S1474-4422(19)30034-1] [PMID: 30871944]
[157]
Reimann, Z.; Miller, J.R.; Dahle, K.M.; Hooper, A.P.; Young, A.M.; Goates, M.C.; Magnusson, B.M.; Crandall, A. Executive functions and health behaviors associated with the leading causes of death in the United States: A systematic review. J. Health Psychol., 2020, 25(2), 186-196.
[http://dx.doi.org/10.1177/1359105318800829] [PMID: 30230381]
[158]
Hauser, A.S.; Attwood, M.M.; Rask-Andersen, M.; Schiöth, H.B.; Gloriam, D.E. Trends in GPCR drug discovery: New agents, targets and indications. Nat. Rev. Drug Discov., 2017, 16(12), 829-842.
[http://dx.doi.org/10.1038/nrd.2017.178] [PMID: 29075003]

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