Abstract
Natural products (NPs) play an important role in drug discovery. No less than 50% of the Food and Drug Administration (FDA)-approved drugs were NPs or NPs derivatives. More importantly, NPs have become an indispensable component of polypharmacology and a constant source of bioactive compounds or a golden mine of multitarget drugs (MTD drugs) because of their multitargeting property: recent studies have unequivocally demonstrated that FDA-approved, clinically tested, and experimentally investigational NPs commonly act on multiple molecular targets (e.g., proteins, DNAs, RNAs). With diverse chemical structures, NPs and their derivatives contribute greatly to the landscape of new chemical entities (NCEs) for de novo drug discovery and development, as well as drug repositioning, in the past, at present, and in the future. This chapter will first explain the relationship between NPs and polypharmacology, which will be followed by an introduction to the relationships between systems pharmacology/network pharmacology and NPs polypharmacology. Then, the current applications of NPs as MTDs to cancer therapy will be summarized. Finally, resources and tools for exploiting NPs polypharmacology will be discussed.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Newman DJ, Cragg GM. Natural products as sources of new drugs over the 30 years from 1981 to 2010. J Nat Prod. 2012;75:311–35.
Ji HF, Li XJ, Zhang HY. Natural products and drug discovery: can thousands of years of ancient medical knowledge lead us to new and powerful drug combinations in the fight against cancer and dementia? EMBO Rep. 2009;10:194–200.
Harvey A. Natural products in drug discovery. Drug Discov Today. 2008;13:894–901.
Butler MS, Robertson AA, Cooper MA. Natural product and natural product derived drugs in clinical trials. Nat Prod Rep. 2014;31:1612–61.
Shen B. A new golden age of natural products drug discovery. Cell. 2015;163:1297–300.
Kingston DGI. Modern natural products drug discovery and its relevance to biodiversity conservation. J Nat Prod. 2011;74:496–511.
Chin YW, Balunas MJ, Chai HB, et al. Drug discovery from natural sources. AAPS J. 2006;8:E239–53.
Ho TT, Tran QT, Chai CL. The polypharmacology of natural products. Future Med Chem. 2018;10(11):1361–8.
Fang J, Liu C, Wang Q, et al. In silico polypharmacology of natural products. Brief Bioinform. 2018;19(6):1153–71.
Gu J, Gui Y, Chen L, et al. Use of natural products as chemical library for drug discovery and network pharmacology. PLoS One. 2013;8(4):e62839.
Kibble M, Saarinen N, Tang J, et al. Network pharmacology applications to map the unexplored target space and therapeutic potential of natural products. Nat Prod Rep. 2015;32(8):1249–66.
Yildirim MA, Goh KI, Cusick ME, et al. Drug-target network. Nat. Biotech. 2007;25:1119–26.
Barneh F, Jafari M, Mirzaie M. Updates on drug-target network; facilitating polypharmacology and data integration by growth of DrugBank database. Brief Bioinform. 2016;17:1070–80.
DeCorte BL. Underexplored opportunities for natural products in drug discovery. J Med Chem. 2016;59:9295–304.
Harvey AL, Edrada-Ebel R, Quinn RJ. The re-emergence of natural products for drug discovery in the genomics era. Nat Rev Drug Discov. 2015;14:111–29.
Li JW, Vederas JC. Drug discovery and natural products: end of an era or an endless frontier? Science. 2009;325:161–5.
Fang J, Cai C, Wang Q, et al. Systems pharmacology-based discovery of natural products for precision oncology through targeting cancer mutated genes. CPT Pharmacometrics Syst Pharmacol. 2017;6:177–87.
Clardy J, Walsh C. Lessons from natural molecules. Nature. 2004;432:829–37.
Gu J, Zhang H, Chen L, et al. Drug-target network and polypharmacology studies of a Traditional Chinese Medicine for type II diabetes mellitus. Computational Biol Chem. 2011;35:293–7.
Vogt I, Mestres J. Drug-target networks Mol Informatics. 2010;29:10–4.
Mayr F, Möller G, Garscha U, et al. Finding new molecular targets of familiar natural products using in silico target prediction. Int J Mol Sci. 2020;21(19):7102.
Newman DJ, Cragg GM. Natural products as sources of new drugs from 1981 to 2014. J Nat Prod. 2016;79:629–61.
Koehn FE, Carter GT. The evolving role of natural products in drug discovery. Nat Rev Drug Discov. 2005;4:206–20.
Koeberle A, Werz O. Multi-target approach for natural products in inflammation. Drug Discov Today. 2014;19:1871–82.
Rodrigues T, Reker D, Schneider P, et al. Counting on natural products for drug design. Nat Chem. 2016;8:531.
Clemons PA, Bodycombe NE, Carrinski HA, et al. Small molecules of different origins have distinct distributions of structural complexity that correlate with protein-binding profiles. Proc Natl Acad Sci U S A. 2010;107:18787.
Wang XJ, Wei XM, Thijssen B, et al. Three-dimensional reconstruction of protein networks provides insight into human genetic disease. Nat Biotech. 2012;30:159–64.
Yang K, Ma WZ, Liang HH, et al. Dynamic simulations on the arachidonic acid metabolic network. PloS Computational Biol. 2007;3:523–30.
Zhang Y, Thiele I, Weekes D, et al. Three-dimensional structural view of the central metabolic network of thermotoga maritima. Science. 2009;325:1544–9.
Li QA, Li XD, Li CH, et al. A network-based multi-target computational estimation scheme for anticoagulant activities of compounds. PLoS One. 2011;6(3):e14774.
Hong JY. Role of natural product diversity in chemical biology. Curr Opinion Chem Biol. 2011;15:350–4.
Firn RD, Jones CG. Natural products – a simple model to explain chemical diversity. Nat Prod Reports. 2003;20:382–91.
Basso LA, da Silva LHP, Fett-Neto AG, et al. The use of biodiversity as source of new chemical entities against defined molecular targets for treatment of malaria, tuberculosis, and T-cell mediated diseases – a review. Memorias Do Instituto Oswaldo Cruz. 2005;100:575–606.
Quinn RJ, Carroll AR, Pham NB, et al. Developing a drug-like natural product library. J Nat Prod. 2008;71:464–8.
Feher M, Schmidt JM. Property distributions: differences between drugs, natural products, and molecules from combinatorial chemistry. J Chem Info Computer Sci. 2003;43:218–27.
Yongye AB, Waddell J, Medina-Franco JL. Molecular scaffold analysis of natural products databases in the public domain. Chem Biol & Drug Design. 2012;80:717–24.
Grabowski K, Baringhaus KH, Schneider G. Scaffold diversity of natural products: inspiration for combinatorial library design. Nat Prod Reports. 2008;25:892–904.
Lee ML, Schneider G. Scaffold architecture and pharmacophoric properties of natural products and trade drugs: application in the design of natural product-based combinatorial libraries. J Comb Chem. 2001;3:284–9.
Dobson CM. Chemical space and biology. Nature. 2004;432:824–8.
Rosen J, Gottfries J, Muresan S, et al. Novel chemical space exploration via natural products. J Medicinal Chem. 2009;52:1953–62.
Grabowski K, Schneider G. Properties and architecture of drugs and natural products revisited. Curr Chem Biol. 2007;1:115–27.
Henkel T, Brunne RM, Muller H, et al. Statistical investigation into the structural complementarity of natural products and synthetic compounds. Angewandte Chemie-International Edition. 1999;38:643–7.
Qiao XB, Hou TJ, Zhang W, et al. A 3D structure database of components from Chinese traditional medicinal herbs. J Chem Inf Comput Sci. 2002;42:481–9.
Doman TN, McGovern SL, Witherbee BJ, et al. Molecular docking and high-throughput screening for novel inhibitors of protein tyrosine phosphatase-1B. J Medicinal Chem. 2002;45:2213–21.
Zhu F, Shi Z, Qin C, et al. Therapeutic target database update 2012: a resource for facilitating target-oriented drug discovery. Nucl Acids Res. 2012;40:D1128–36.
Fang J, Wu Z, Cai C, et al. Quantitative and systems pharmacology. 1. In silico prediction of drug-target interactions of natural products enables new targeted cancer therapy. J Chem Inf Model. 2017;57(11):2657–71.
Luo H, Mattes W, Mendrick DL, et al. Molecular docking for identification of potential targets for drug repurposing. Curr Top Med Chem. 2016;16:3636–45.
Ye H, Wei J, Tang K, et al. Drug repositioning through network pharmacology. Curr Top Med Chem. 2016;16:3646–56.
Cheng F, Liu C, Jiang J, et al. Prediction of drug-target interactions and drug repositioning via network-based inference. PLoS Comput Biol. 2012;8:e1002503.
Cheng F, Zhou Y, Li W, et al. Prediction of chemical-protein interactions network with weighted network-based inference method. PLoS One. 2012;7:e41064.
Li J, Lei K, Wu Z, et al. Network-based identification of microRNAs as potential pharmacogenomic biomarkers for anticancer drugs. Oncotarget. 2016;7:45584–96.
Li J, Wu Z, Cheng F, et al. Computational prediction of microRNA networks incorporating environmental toxicity and disease etiology. Sci Rep. 2014;4:5576.
Wu Z, Cheng F, Li J, et al. SDTNBI: an integrated network and chemoinformatics tool for systematic prediction of drug-target interactions and drug repositioning. Brief Bioinform. 2017;18:333–47.
Wu Z, Lu W, Wu D, et al. In silico prediction of chemical mechanism of action via an improved network-based inference method. Br J Pharmacol. 2016;173:3372–85.
Cheng F, Li W, Wang X, et al. Adverse drug events: database construction and in silico prediction. J Chem Inf Model. 2013;53:744–52.
Cheng F, Li W, Wu Z, et al. Prediction of polypharmacological profiles of drugs by the integration of chemical, side effect, and therapeutic space. J Chem Inf Model. 2013;53:753–62.
Lu W, Cheng F, Jiang J, et al. FXR Antagonism of NSAIDs contributes to drug-induced liver injury identified by systems pharmacology approach. Sci Rep. 2015;5:8114.
Campbell IB, Macdonald SJF, Procopiou PA. Medicinal chemistry in drug discovery in big pharma: past, present and future. Drug Discov Today. 2018;23:219–34.
Chen C, Huang H, Wu CH. Protein bioinformatics databases and resources. In: Chen C, Huang H, Wu CH, editors. Fundamentals of protein bioinformatics, vol. 1558. New York: Humana Press; 2017. p. 3–39.
Sliwoski G, Kothiwale S, Meiler J, et al. Computational methods in drug discovery. Pharmacol Rev. 2014;66:334–95.
Ashburn TT, Thor KB. Drug repositioning: identifying and developing new uses for existing drugs. Nat Rev Drug Discov. 2004;3:673–83.
Aronson JK. Old drugs—new uses. Br J Clin Pharmacol. 2007;64:563–5.
Hurle MR, Yang L, Xie Q, et al. Computational drug repositioning: from data to therapeutics. Clin Pharmacol Ther. 2013;93:335–41.
Cereto-Massagué A, Ojeda MJ, Valls C, et al. Tools for in silico target fishing. Methods. 2015;71:98–103.
Huang Y-W, Pineau I, Chang H-J, et al. Critical residues for the specificity of cofactors and substrates in human estrogenic 17β-hydroxysteroid dehydrogenase 1: variants designed from the three-dimensional structure of the enzyme. Mol Endocrinol. 2001;15:2010–20.
Sydow D, Burggraaff L, Szengel A, et al. Advances and challenges in computational target prediction. J Chem Inf Model. 2019;59:1728–42.
Kim S, Chen J, Cheng T, et al. PubChem 2019 update: improved access to chemical data. Nucleic Acids Res. 2018;47:D1102–9.
Casey SC, Amedei A, Aquilano K, et al. Cancer prevention and therapy through the modulation of the tumor microenvironment. Semin Cancer Biol. 2015;35:S199–223.
Liu Y, Cao X. Immunosuppressive cells in tumor immune escape and metastasis. J Mol Med (Berl). 2016;94:509–22.
Kotecha R, Takami A, Espinoza JL. Dietary phytochemicals and cancer chemoprevention: a review of the clinical evidence. Oncotarget. 2016;7:52517–29.
Jantan I, Ahmad W, Bukhari SN. Plant-derived immunomodulators: an insight on their preclinical evaluation and clinical trials. Front Plant Sci. 2015;6:655.
Calderon-Montano JM, Burgos-Moron E, Perez-Guerrero C, et al. A review on the dietary flavonoid kaempferol. Mini Rev Med Chem. 2011;11:298–344.
Kim SH, Hwang KA, Choi KC. Treatment with kaempferol suppresses breast cancer cell growth caused by estrogen and triclosan in cellular and xenograft breast cancer models. J Nutr Biochem. 2016;28:70–82.
Luo H, Rankin GO, Liu L, et al. Kaempferol inhibits angiogenesis and VEGF expression through both HIF dependent and independent pathways in human ovarian cancer cells. Nutr Cancer. 2009;61:554–63.
Goettert M, Schattel V, Koch P, et al. Biological evaluation and structural determinants of p38alpha mitogen-activated-protein kinase and c-Jun-N-terminal kinase 3 inhibition by flavonoids. Chembiochem. 2010;11:2579–88.
Arai Y, Endo S, Miyagi N, et al. Structure-activity relationship of flavonoids as potent inhibitors of carbonyl reductase 1 (CBR1). Fitoterapia. 2015;101:51–6.
Kasi PD, Tamilselvam R, Skalicka-Wozniak K, et al. Molecular targets of curcumin for cancer therapy: an updated review. Tumour Biol. 2016;37:13017–28.
Bar-Sela G, Epelbaum R, Schaffer M. Curcumin as an anticancer agent: review of the gap between basic and clinical applications. Curr Med Chem. 2010;17:190–7.
Chakraborty G, Jain S, Kale S, et al. Curcumin suppresses breast tumor angiogenesis by abrogating osteopontin-induced VEGF expression. Mol Med Rep. 2008;1:641–6.
Bhaumik S, Jyothi MD, Khar A. Differential modulation of nitric oxide production by curcumin in host macrophages and NK cells. FEBS Lett. 2000;483:78–82.
Surh YJ, Chun KS, Cha HH, et al. Molecular mechanisms underlying chemopreventive activities of anti-inflammatory phytochemicals: down-regulation of COX-2 and iNOS through suppression of NF-kappa B activation. Mutat Res. 2001;480–481:243–68.
Lu Y, Miao L, Wang Y, et al. Curcumin micelles remodel tumor microenvironment and enhance vaccine activity in an advanced melanoma model. Mol Ther. 2016;24:364–74.
Khan F, Niaz K, Maqbool F, et al. Molecular targets underlying the anticancer effects of quercetin: an update. Nutrients. 2016;8:529.
He D, Guo X, Zhang E, et al. Quercetin induces cell apoptosis of myeloma and displays a synergistic effect with dexamethasone in vitro and in vivo xenograft models. Oncotarget. 2016;7:45489–99.
Pratheeshkumar P, Budhraja A, Son YO, et al. Quercetin inhibits angiogenesis mediated human prostate tumor growth by targeting VEGFR- 2 regulated AKT/mTOR/P70S6K signaling pathways. PLoS One. 2012;7:e47516.
Kashyap D, Mittal S, Sak K, et al. Molecular mechanisms of action of quercetin in cancer: recent advances. Tumour Biol. 2016;37:12927–39.
Sternberg Z, Chadha K, Lieberman A, et al. Quercetin and interferon-beta modulate immune response(s) in peripheral blood mononuclear cells isolated from multiple sclerosis patients. J Neuroimmunol. 2008;205:142–7.
Hamalainen M, Nieminen R, Vuorela P, et al. Anti-inflammatory effects of flavonoids: genistein, kaempferol, quercetin, and daidzein inhibit STAT-1 and NF-kappaB activations, whereas flavone, isorhamnetin, naringenin, and pelargonidin inhibit only NF-kappaB activation along with their inhibitory effect on iNOS expression and NO production in activated macrophages. Mediat Inflamm. 2007;2007:45673.
Ruiz PA, Braune A, Holzlwimmer G, et al. Quercetin inhibits TNF-induced NF-kappaB transcription factor recruitment to proinflammatory gene promoters in murine intestinal epithelial cells. J Nutr. 2007;137:1208–15.
Lee J, Choi JW, Sohng JK, et al. The immunostimulating activity of quercetin 3-O-xyloside in murine macrophages via activation of the ASK1/MAPK/NF-kappaB signaling pathway. Int Immunopharmacol. 2016;31:88–97.
Spagnuolo C, Russo GL, Orhan IE, et al. Genistein and cancer: current status, challenges, and future directions. Adv Nutr. 2015;6:408–19.
Li F, Zhang J, Arfuso F, et al. NF-kappaB in cancer therapy. Arch Toxicol. 2015;89:711–31.
Xie J, Wang J, Zhu B. Genistein inhibits the proliferation of human multiple myeloma cells through suppression of nuclear factor-kappaB and upregulation of microRNA-29b. Mol Med Rep. 2016;13:1627–32.
Mir IA, Tiku AB. Chemopreventive and therapeutic potential of “naringenin,” a flavanone present in citrus fruits. Nutr Cancer. 2015;67:27–42.
Qin L, Jin L, Lu L, et al. Naringenin reduces lung metastasis in a breast cancer resection model. Protein Cell. 2011;2:507–16.
Zhang F, Dong W, Zeng W, et al. Naringenin prevents TGF-beta1 secretion from breast cancer and suppresses pulmonary metastasis by inhibiting PKC activation. Breast Cancer Res. 2016;18:38.
Vanamala J, Leonardi T, Patil BS, Turner ND, et al. Suppression of colon carcinogenesis by bioactive compounds in grapefruit. Carcinogenesis. 2006;27:1257–65.
Lim W, Park S, Bazer FW, et al. Naringenin-induced apoptotic cell death in prostate cancer cells is mediated via the PI3K/AKT and MAPK signaling pathways. J Cell Biochem. 2017;118:1118–31.
Varoni EM, Lo Faro AF, Sharifi-Rad J, et al. Anticancer molecular mechanisms of resveratrol. Front Nutr. 2016;3:8.
Jang M, Cai L, Udeani GO, et al. Cancer chemopreventive activity of resveratrol, a natural product derived from grapes. Science. 1997;275:218–20.
MacCarrone M, Lorenzon T, Guerrieri P, et al. Resveratrol prevents apoptosis in K562 cells by inhibiting lipoxygenase and cyclooxygenase activity. Eur J Biochem. 1999;265:27–34.
Robb EL, Stuart JA. Resveratrol interacts with estrogen receptor-beta to inhibit cell replicative growth and enhance stress resistance by upregulating mitochondrial superoxide dismutase. Free Radic Biol Med. 2011;50:821–31.
Wang J, Guo Z, Fu Y, et al. Weak-binding molecules are not drugs? Toward a systematic strategy for finding effective weak-binding drugs. Briefing Bioinf. 2017;18:321–32.
Mestres J, Gregori-Puigjane E. Conciliating binding efficiency and polypharmacology. Trends Pharmacol Sci. 2009;30:470–4.
Jeong SK, Yang K, Park YS, et al. Interferon gamma induced by resveratrol analog, HS-1793, reverses the properties of tumor associated macrophages. Int Immunopharmacol. 2014;22:303–10.
Lai X, Pei Q, Song X, et al. The enhancement of immune function and activation of NF-kappaB by resveratrol-treatment in immunosuppressive mice. Int Immunopharmacol. 2016;33:42–7.
Jiao Y, Hannafon BN, Ding WQ. Disulfiram's anticancer activity: evidence and mechanisms. Anti Cancer Agents Med Chem. 2016;16:1378–84.
Chen D, Cui QC, Yang H, et al. Disulfiram, a clinically used anti-alcoholism drug and copper-binding agent, induces apoptotic cell death in breast cancer cultures and xenografts via inhibition of the proteasome activity. Cancer Res. 2006;66:10425–33.
Kim JY, Cho Y, Oh E, et al. Disulfiram targets cancer stem-like properties and the HER2/Akt signaling pathway in HER2-positive breast cancer. Cancer Lett. 2016;379:39–48.
Liu X, Wang L, Cui W, et al. Targeting ALDH1A1 by disulfiram/copper complex inhibits non-small cell lung cancer recurrence driven by ALDH-positive cancer stem cells. Oncotarget. 2016;7:58516–30.
Hatoum D, McGowan EM. Recent advances in the use of metformin: can treating diabetes prevent breast cancer? Biomed Res Int. 2015;2015:548436.
Gwak H, Kim Y, An H, et al. Metformin induces degradation of cyclin D1 via AMPK/GSK3beta axis in ovarian cancer. Mol Carcinog. 2017;56:349–58.
Gan RY, Li HB, Sui ZQ, et al. Absorption, metabolism, anticancer effect and molecular targets of epigallocatechin gallate (EGCG): an updated review. Crit Rev Food Sci Nutr. 2018;58(6):924–41.
Singh BN, Shankar S, Srivastava RK. Green tea catechin, epigallocatechin-3-gallate (EGCG): mechanisms, perspectives and clinical applications. BiochemPharmacol. 2011;82:1807–21.
Cheng CW, Shieh PC, Lin YC, et al. Indoleamine 2,3-dioxygenase, an immunomodulatory protein, is suppressed by (−)-epigallocatechin-3-gallate via blocking of gammainterferon-induced JAK-PKC-delta-STAT1 signaling in human oral cancer cells. J Agric Food Chem. 2010;58:887–94.
Shim JH, Choi HS, Pugliese A, et al. (-)-Epigallocatechin gallate regulates CD3-mediated T cell receptor signaling in leukemia through the inhibition of ZAP-70 kinase. J Biol Chem. 2008;283:28370–9.
Jin Y, Khadka DB, Cho WJ. Pharmacological effects of berberine and its derivatives: a patent update. Expert Opin Ther Pat. 2016;26:229–43.
Jabbarzadeh Kaboli P, Rahmat A, Ismail P, et al. Targets and mechanisms of berberine, a natural drug with potential to treat cancer with special focus on breast cancer. Eur J Pharmacol. 2014;740:584–95.
Wang N, Tan HY, Li L, et al. Berberine and Coptidis Rhizoma as potential anticancer agents: recent updates and future perspectives. J Ethnopharmacol. 2015;176:35–48.
Mantena SK, Sharma SD, Katiyar SK. Berberine, a natural product, induces G1-phase cell cycle arrest and caspase-3-dependent apoptosis in human prostate carcinoma cells. Mol Cancer Ther. 2006;5:296–308.
Ji C, Yang B, Yang YL, et al. Exogenous cell-permeable C6 ceramide sensitizes multiple cancer cell lines to Doxorubicin-induced apoptosis by promoting AMPK activation and mTORC1 inhibition. Oncogene. 2010;29:6557–68.
Lu JJ, Fu L, Tang Z, et al. Melatonin inhibits AP-2beta/hTERT, NF-kappaB/COX-2 and Akt/ERK and activates caspase/Cyto C signaling to enhance the antitumor activity of berberine in lung cancer cells. Oncotarget. 2016;7:2985–3001.
Ho YT, Yang JS, Li TC, et al. Berberine suppresses in vitro migration and invasion of human SCC-4 tongue squamous cancer cells through the inhibitions of FAK, IKK, NF-kappaB, u-PA and MMP-2 and -9. Cancer Lett. 2009;279:155–62.
Deng S, Hu B, Shen KP, et al. Inflammation, macrophage in cancer progression and Chinese herbal treatment. J Basic Clin Pharm. 2012;3:269–72.
Yildirim MA, Goh KI, Cusick ME, et al. Drug-target network. Nat Biotechnol. 2007;25:1119–26.
Bento AP, Gaulton A, Hersey A, et al. The ChEMBL bioactivity database: an update. Nucleic Acids Res. 2014;42:D1083–90.
Gilson MK, Liu T, Baitaluk M, et al. BindingDB in 2015: a public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res. 2016;44:D1045–53.
Wang Y, Bolton E, Dracheva S, et al. An overview of the PubChem BioAssay resource. Nucleic Acids Res. 2010;38:D255–66.
Law V, Knox C, Djoumbou Y, et al. DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res. 2014;42:D1091–7.
Yang H, Qin C, Li YH, et al. Therapeutic target database update 2016: enriched resource for bench to clinical drug target and targeted pathway information. Nucleic Acids Res. 2016;44:D1069–74.
Wagner AH, Coffman AC, Ainscough BJ, et al. DGIdb 2.0: mining clinically relevant drug-gene interactions. Nucleic Acids Res. 2016;44:D1036–44.
Kuhn M, Szklarczyk D, Pletscher-Frankild S, et al. STITCH 4: integration of protein-chemical interactions with user data. Nucleic Acids Res. 2014;42:D401–7.
Nickel J, Gohlke BO, Erehman J, et al. SuperPred: update on drug classification and target prediction. Nucleic Acids Res. 2014;42:W26–31.
Ye H, Ye L, Kang H, et al. HIT: linking herbal active ingredients to targets. Nucleic Acids Res. 2011;39:D1055–9.
Xue R, Fang Z, Zhang M, et al. TCMID: traditional Chinese Medicine integrative database for herb molecular mechanism analysis. Nucleic Acids Res. 2013;41:D1089–95.
Ru J, Li P, Wang J, et al. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J Cheminform. 2014;6:13.
Mangal M, Sagar P, Singh H, et al. NPACT: naturally occurring plant-based anti-cancer compound-activity-target database. Nucleic Acids Res. 2013;41:D1124–9.
Tao W, Li B, Gao S, et al. CancerHSP: anticancer herbs database of systems pharmacology. Sci Rep. 2015;5:11481.
Bredel M, Jacoby E. Chemogenomics: an emerging strategy for rapid target and drug discovery. Nat Rev Genet. 2004;5:262–75.
Lamb J, Crawford ED, Peck D, et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science. 2006;313:1929–35.
Duan Q, Flynn C, Niepel M, et al. LINCS Canvas Browser: interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures. Nucleic Acids Res. 2014;42:W449–60.
Igarashi Y, Nakatsu N, Yamashita T, et al. Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids Res. 2015;43:D921–7.
Ganter B, Snyder RD, Halbert DN, et al. Toxicogenomics in drug discovery and development: mechanistic analysis of compound/class-dependent effects using the DrugMatrix database. Pharmacogenomics. 2006;7:1025–44.
Lamb J. The connectivity map: a new tool for biomedical research. Nat Rev Cancer. 2007;7:54–60.
Adams JU. Genetics: big hopes for big data. Nature. 2015;527:S108–9.
Chatr-Aryamontri A, Breitkreutz BJ, Oughtred R, et al. The BioGRID interaction database: 2015 update. Nucleic Acids Res. 2015;43:D470–8.
Keshava Prasad TS, Goel R, Kandasamy K, et al. Human protein reference database–2009 update. Nucleic Acids Res. 2009;37:D767–72.
Mosca R, Ceol A, Aloy P. Interactome3D: adding structural details to protein networks. Nat Methods. 2013;10:47–53.
Szklarczyk D, Morris JH, Cook H, et al. The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res. 2017;45:D362–8.
Licata L, Briganti L, Peluso D, et al. MINT, the molecular interaction database: 2012 update. Nucleic Acids Res. 2012;40:D857–61.
Cheng F, Jia P, Wang Q, et al. Quantitative network mapping of the human kinome interactome reveals new clues for rational kinase inhibitor discovery and individualized cancer therapy. Oncotarget. 2014;5:3697–710.
Cheng F, Liu C, Lin CC, et al. A gene gravity model for the evolution of cancer genomes: a study of 3,000 cancer genomes across 9 cancer types. PLoS Comput Biol. 2015;11:e1004497.
Zhang C, Hong H, Mendrick DL, et al. Biomarker-based drug safety assessment in the age of systems pharmacology: from foundational to regulatory science. BiomarkMed. 2015;9:1241–52.
Cheng F, Murray JL, Zhao J, et al. Systems biology-based investigation of cellular antiviral drug targets identified by gene-trap insertional mutagenesis. PLoS Comput Biol. 2016;12:e1005074.
Cheng F, Liu C, Shen B, et al. Investigating cellular network heterogeneity and modularity in cancer: a network entropy and unbalanced motif approach. BMC Syst Biol. 2016;10(Suppl 3):65.
Cheng F, Zhao J, Hanker AB, et al. Transcriptome- and proteome-oriented identification of dysregulated eIF4G, STAT3, and Hippo pathways altered by PIK3CA H1047R in HER2/ER-positive breast cancer. Breast Cancer Res Treat. 2016;160:457–74.
Cheng F, Jia P, Wang Q, et al. Studying tumorigenesis through network evolution and somatic mutational perturbations in the cancer interactome. Mol Biol Evol. 2014;31:2156–69.
Cheng F, Zhao J, Zhao Z. Advances in computational approaches for prioritizing driver mutations and significantly mutated genes in cancer genomes. Brief Bioinform. 2016;17:642–56.
Vogelstein B, Papadopoulos N, Velculescu VE, et al. Cancer genome landscapes. Science. 2013;339:1546–58.
Hayes DN, Kim WY. The next steps in next-gen sequencing of cancer genomes. J Clin Invest. 2015;125:462–8.
Chin L, Andersen JN, Futreal PA. Cancer genomics: from discovery science to personalized medicine. Nat Med. 2011;17:297–303.
International Cancer Genome C, Hudson TJ, Anderson W, et al. International network of cancer genome projects. Nature. 2010;464:993–8.
Gao J, Aksoy BA, Dogrusoz U, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 2013;6:pl1.
Forbes SA, Beare D, Gunasekaran P, et al. COSMIC: exploring the world’s knowledge of somatic mutations in human cancer. Nucleic Acids Res. 2015;43:D805–11.
Nakagawa H, Wardell CP, Furuta M, et al. Cancer whole-genome sequencing: present and future. Oncogene. 2015;34:5943–50.
Chen X, Yan CC, Zhang X, et al. Drug-target interaction prediction: databases, web servers and computational models. Brief Bioinform. 2016;17:696–712.
Koutsoukas A, Simms B, Kirchmair J, et al. From in silico target prediction to multi-target drug design: current databases, methods and applications. J Proteome. 2011;74:2554–74.
Yue R, Shan L, Yang X, et al. Approaches to target profiling of natural products. Curr Med Chem. 2012;19:3841–55.
Jenkins JL, Bender A, Davies JW. In silico target fishing: predicting biological targets from chemical structure. Drug Discov Today Technol. 2007;3:413–21.
Harren J, Andrew RL. Structure-based drug discovery. Dordrecht: Springer; 2007. ISB ISBN: 978-1-4020-4406-9
Taboureau O, Baell JB, Fernandez-Recio J, et al. Established and emerging trends in computational drug discovery in the structural genomics era. Chem Biol. 2012;19:29–41.
Sakkiah S, Ng HW, Tong W, et al. Structures of androgen receptor bound with ligands: advancing understanding of biological functions and drug discovery. Expert Opin Ther Targets. 2016;20:1267–82.
Liu LJ, Leung KH, Chan DS, et al. Identification of a natural product-like STAT3 dimerization inhibitor by structure-based virtual screening. Cell Death Dis. 2014;5:e1293.
Zhong HJ, Lee BR, Boyle JW, et al. Structure-based screening and optimization of cytisine derivatives as inhibitors of the menin-MLL interaction. Chem Commun. 2016;52:5788–91.
Singh T, Gupta NA, Xu S, et al. Honokiol inhibits the growth of head and neck squamous cell carcinoma by targeting epidermal growth factor receptor. Oncotarget. 2015;6:21268–82.
Zhong HJ, Ma VP, Cheng Z, et al. Discovery of a natural product inhibitor targeting protein neddylation by structure-based virtual screening. Biochimie. 2012;94:2457–60.
Lee HM, Chan DS, Yang F, et al. Identification of natural product fonsecin B as a stabilizing ligand of c-myc G-quadruplex DNA by high-throughput virtual screening. Chem Commun. 2010;46:4680–2.
Ma D-L, Chan DS-H, Leung C-H. Molecular docking for virtual screening of natural product databases. ChemSci. 2011;2:1656–65.
Cierpicki T, Grembecka J. Challenges and opportunities in targeting the menin-MLL interaction. Future Med Chem. 2014;6:447–62.
Chen YZ, Zhi DG. Ligand-protein inverse docking and its potential use in the computer search of protein targets of a small molecule. Proteins. 2001;43:217–26.
Wang JC, Chu PY, Chen CM, et al. idTarget: a web server for identifying protein targets of small chemical molecules with robust scoring functions and a divide-and-conquer docking approach. Nucleic Acids Res. 2012;40:W393–9.
Lauro G, Romano A, Riccio R, et al. Inverse virtual screening of antitumor targets: pilot study on a small database of natural bioactive compounds. J Nat Prod. 2011;74:1401–7.
Lauro G, Masullo M, Piacente S, et al. Inverse virtual screening allows the discovery of the biological activity of natural compounds. Bioorg Med Chem. 2012;20:3596–602.
Vuong H, Cheng F, Lin CC, et al. Functional consequences of somatic mutations in cancer using protein pocket-based prioritization approach. Genome Med. 2014;6:81.
Zhao J, Cheng F, Wang Y, et al. Systematic prioritization of druggable mutations in approximately 5000 genomes across 16 cancer types using a structural genomics-based approach. Mol Cell Proteomics. 2016;15:642–56.
Lavecchia A. Machine-learning approaches in drug discovery: methods and applications. Drug Discov Today. 2015;20:318–31.
Yan X, Liao C, Liu Z, et al. Chemical structure similarity search for ligand-based virtual screening: methods and computational resources. Curr Drug Targets. 2016;17:1580–5.
Liu X, Ouyang S, Yu B, et al. PharmMapper server: a web server for potential drug target identification using pharmacophore mapping approach. Nucleic Acids Res. 2010;38:W609–14.
Yan X, Li J, Liu Z, et al. Enhancing molecular shape comparison by weighted Gaussian functions. J Chem Inf Model. 2013;53:1967–78.
Fang J, Yang R, Gao L, et al. Predictions of BuChE inhibitors using support vector machine and naive Bayesian classification techniques in drug discovery. J Chem Inf Model. 2013;53:3009–20.
Fang J, Yang R, Gao L, et al. Consensus models for CDK5 inhibitors in silico and their application to inhibitor discovery. Mol Divers. 2015;19:149–62.
Fang J, Li Y, Liu R, et al. Discovery of multitarget-directed ligands against Alzheimer’s disease through systematic prediction of chemical-protein interactions. J Chem Inf Model. 2015;55:149–64.
Fang J, Pang X, Wu P, et al. Discovery of neuroprotective compounds by machine learning approaches. RSC Adv. 2016;6:9857.
Cheng F, Li W, Liu G, et al. In silico ADMET prediction: recent advances, current challenges and future trends. Curr Top Med Chem. 2013;13:1273–89.
Sprague B, Shi Q, Kim MT, et al. Design, synthesis and experimental validation of novel potential chemopreventive agents using random forest and support vector machine binary classifiers. J Comput Aided Mol Des. 2014;28:631–46.
Bilsland AE, Pugliese A, Liu Y, et al. Identification of a selective G1-phase benzimidazolone inhibitor by a senescence targeted virtual screen using artificial neural networks. Neoplasia. 2015;17:704–15.
Liu H, Sun J, Guan J, et al. Improving compound-protein interaction prediction by building up highly credible negative samples. Bioinformatics. 2015;31:i221–9.
Mousavian Z, Masoudi-Nejad A. Drug-target interaction prediction via chemogenomic space: learning-based methods. Expert Opin Drug Metab Toxicol. 2014;10:1273–87.
Yamanishi Y. Chemogenomic approaches to infer drug target interaction networks. Methods Mol Biol. 2013;939:97–113.
Zhao S, Li S. Network-based relating pharmacological and genomic spaces for drug target identification. PLoS One. 2010;5:e11764.
Yamanishi Y, Araki M, Gutteridge A, et al. Prediction of drug target interaction networks from the integration of chemical and genomic spaces. Bioinformatics. 2008;24:i232–40.
Bleakley K, Yamanishi Y. Supervised prediction of drug target interactions using bipartite local models. Bioinformatics. 2009;25:2397–403.
Keum J, Yoo S, Lee D, et al. Prediction of compound-target interactions of natural products using large-scale drug and protein information. BMC Bioinformatics. 2016;17(Suppl 6):219.
Yu H, Chen J, Xu X, et al. A systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data. PLoS One. 2012;7:e37608.
Huang C, Zheng C, Li Y, et al. Systems pharmacology in drug discovery and therapeutic insight for herbal medicines. Brief Bioinform. 2014;15:710–33.
Sawada R, Kotera M, Yamanishi Y. Benchmarking a wide range of chemical descriptors for drug-target interaction prediction using a chemogenomic approach. Mol Inform. 2014;33:719–31.
Cheng F, Zhou Y, Li J, et al. Prediction of chemical-protein interactions: multitarget-QSAR versus computational chemogenomic methods. Mol BioSyst. 2012;8:2373–84.
Cheng F, Li W, Zhou Y, et al. Prediction of human genes and diseases targeted by xenobiotics using predictive toxicogenomic derived models (PTDMs). Mol BioSyst. 2013;9:1316–25.
Kitano H. Systems biology: a brief overview. Science. 2002;295:1662–4.
Kitano H. Computational systems biology. Nature. 2002;420:206–10.
Berg EL. Systems biology in drug discovery and development. Drug Discov Today. 2014;19:113–25.
Cheng F, Murray JL, Rubin DH. Drug repurposing: new treatments for Zika virus infection? Trends Mol Med. 2016;22:919–21.
Qu XA, Rajpal DK. Applications of connectivity map in drug discovery and development. Drug Discov Today. 2012;17:1289–98.
Hieronymus H, Lamb J, Ross KN, et al. Gene expression signature-based chemical genomic prediction identifies a novel class of HSP90 pathway modulators. Cancer Cell. 2006;10:321–30.
Wei G, Twomey D, Lamb J, et al. Gene expression-based chemical genomics identifies rapamycin as a modulator of MCL1 and glucocorticoid resistance. Cancer Cell. 2006;10:331–42.
Langley SR, Dwyer J, Drozdov I, et al. Proteomics: from single molecules to biological pathways. Cardiovasc Res. 2013;97:612–22.
Bensimon A, Heck AJ, Aebersold R. Mass spectrometry-based proteomics and network biology. Annu Rev Biochem. 2012;81:379–405.
Savitski MM, Reinhard FB, Franken H, et al. Tracking cancer drugs in living cells by thermal profiling of the proteome. Science. 2014;346:1255784.
Franken H, Mathieson T, Childs D, et al. Thermal proteome profiling for unbiased identification of direct and indirect drug targets using multiplexed quantitative mass spectrometry. Nat Protoc. 2015;10:1567–93.
Reinhard FB, Eberhard D. Thermal proteome profiling monitors ligand interactions with cellular membrane proteins. Nat Methods. 2015;12:1129–31.
Sacco F, Silvestri A, Posca D, et al. Deep proteomics of breast cancer cells reveals that metformin rewires signaling networks away from a pro-growth state. Cell Syst. 2016;2:159–71.
Kaddurah-Daouk R, Kristal BS, Weinshilboum RM. Metabolomics: a global biochemical approach to drug response and disease. Annu Rev Pharmacol Toxicol. 2008;48:653–83.
Birkenstock T, Liebeke M, Winstel V, et al. Exometabolome analysis identifies pyruvate dehydrogenase as a target for the antibiotic triphenylbismuthdichloride in multiresistant bacterial pathogens. J Biol Chem. 2012;287:2887–95.
Zhang B, Watts KM, Hodge D, et al. A second target of the antimalarial and antibacterial agent fosmidomycin revealed by cellular metabolic profiling. Biochemistry. 2011;50:3570–7.
Bayet-Robert M, Lim S, Barthomeuf C, et al. Biochemical disorders induced by cytotoxic marine natural products in breast cancer cells as revealed by proton NMR spectroscopy-based metabolomics. Biochem Pharmacol. 2010;80:1170–9.
Pulido MR, Garcia-Quintanilla M, Gil-Marques ML, et al. Identifying targets for antibiotic development using omics technologies. Drug Discov Today. 2016;21:465–72.
Zhao Y, Hu Q, Cheng F, et al. SoNar, a highly responsive NADþ/NADH sensor, allows high-throughput metabolic screening of anti-tumor agents. Cell Metab. 2015;21:777–89.
Kim HK, Wilson EG, Choi YH, et al. Metabolomics: a tool for anticancer lead-finding from natural products. Planta Med. 2010;76:1094–102.
Collins GS, de Groot JA, Dutton S, et al. External validation of multivariable prediction models: a systematic review of methodological conduct and reporting. BMC Med Res Methodol. 2014;14:40.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Wang, Z., Yang, B. (2022). Polypharmacology and Natural Products. In: Polypharmacology. Springer, Cham. https://doi.org/10.1007/978-3-031-04998-9_15
Download citation
DOI: https://doi.org/10.1007/978-3-031-04998-9_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-04997-2
Online ISBN: 978-3-031-04998-9
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)