Generic placeholder image

Anti-Cancer Agents in Medicinal Chemistry

Editor-in-Chief

ISSN (Print): 1871-5206
ISSN (Online): 1875-5992

Research Article

What Makes Species Productive of Anti-Cancer Drugs? Clues from Drugs’ Species Origin, Druglikeness, Target and Pathway

Author(s): Xiaofeng Li, Xiaoxu Li, Yinghong Li, Chunyan Yu, Weiwei Xue, Jie Hu*, Bo Li, Panpan Wang and Feng Zhu*

Volume 19, Issue 2, 2019

Page: [194 - 203] Pages: 10

DOI: 10.2174/1871520618666181029132017

Price: $65

Abstract

Background: Despite the substantial contribution of natural products to the FDA drug approval list, the discovery of anti-cancer drugs from the huge amount of species on the planet remains looking for a needle in a haystack.

Objective: Drug-productive clusters in the phylogenetic tree are thus proposed to narrow the searching scope by focusing on much smaller amount of species within each cluster, which enable prioritized and rational bioprospecting for novel drug-like scaffolds. However, the way anti-cancer nature-derived drugs distribute in phylogenetic tree has not been reported, and it is oversimplified to just focus anti-cancer drug discovery on the drug-productive clusters, since the number of species in each cluster remains too large to be managed.

Methods: In this study, 260 anti-cancer drugs approved in the past 70 years were comprehensively analyzed by hierarchical clustering of phylogenetic distribution.

Results: 207 out of these 260 drugs were derived from or inspired by the natural products isolated from 58 species. Phylogenetic distribution of those drugs further revealed that nature-derived anti-cancer drugs originated mostly from drug-productive families that tend to be clustered rather than scattered on the phylogenetic tree. Moreover, based on their productivity, drug-producing species were categorized into productive (CPS), newly emerging (CNS) and lessproductive (CLS). Statistical significances in druglikeness between drugs from CPS and CLS were observed, and drugs from CNS were found to share similar drug-like properties to those from CPS.

Conclusion: This finding indicated a great raise in drug approval standard, which suggested us to focus bioprospecting on the species yielding multiple drugs and keeping productive for long period of time.

Keywords: Anti-cancer drugs, nature-derived drugs, druglikeness, medicinal chemistry, phylogenetic distribution, target and pathway.

Graphical Abstract

[1]
Harvey, A.L.; Edrada-Ebel, R.; Quinn, R.J. The re-emergence of natural products for drug discovery in the genomics era. Nat. Rev. Drug Discov., 2015, 14(2), 111-129.
[2]
Newman, D.J.; Cragg, G.M. Natural products as sources of new drugs from 1981 to 2014. J. Nat. Prod., 2016, 79(3), 629-661.
[3]
Wilson, M.R.; Zha, L.; Balskus, E.P. Natural product discovery from the human microbiome. J. Biol. Chem., 2017, 292(21), 8546-8552.
[4]
Zhu, F.; Shi, Z.; Qin, C.; Tao, L.; Liu, X.; Xu, F.; Zhang, L.; Song, Y.; Liu, X.; Zhang, J.; Han, B.; Zhang, P.; Chen, Y. Therapeutic target database update 2012: A resource for facilitating target-oriented drug discovery. Nucleic Acids Res., 2012, 40(Database issue), D1128-D1136.
[5]
Patridge, E.; Gareiss, P.; Kinch, M.S.; Hoyer, D. An analysis of FDA-approved drugs: Natural products and their derivatives. Drug Discov. Today, 2016, 21(2), 204-207.
[6]
Yang, H.; Qin, C.; Li, Y.; Tao, L.; Zhou, J.; Yu, C.; Xu, F.; Chen, Z.; Zhu, F.; Chen, Y. Therapeutic target database update 2016: Enriched resource for bench to clinical drug target and targeted pathway information. Nucleic Acids Res., 2016, 44(D1), D1069-D1074.
[7]
Tao, L.; Zhu, F.; Qin, C.; Zhang, C.; Xu, F.; Tan, C.; Jiang, Y.; Chen, Y. Nature’s contribution to today’s pharmacopeia. Nat. Biotechnol., 2014, 32(10), 979-980.
[8]
Zhu, F.; Han, B.; Kumar, P.; Liu, X.; Ma, X.; Wei, X.; Huang, L.; Guo, Y.; Han, L.; Zheng, C.; Chen, Y. Update of TTD: Therapeutic Target Database. Nucleic Acids Res., 2010, 38(Database issue), D787-D791.
[9]
Mullard, A. FDA drug approvals. Nat. Rev. Drug Discov., 2013, 12(2), 87-90.
[10]
Mullard, A. FDA drug approvals. Nat. Rev. Drug Discov., 2014, 13(2), 85-89.
[11]
Mullard, A. FDA drug approvals. Nat. Rev. Drug Discov., 2015, 14(2), 77-81.
[12]
Mullard, A. FDA drug approvals. Nat. Rev. Drug Discov., 2016, 15(2), 73-76.
[13]
Mullard, A. FDA drug approvals. Nat. Rev. Drug Discov., 2017, 16(2), 73-76.
[14]
Lambert, M.; Wolfender, J.L.; Staerk, D.; Christensen, S.B.; Hostettmann, K.; Jaroszewski, J.W. Identification of natural products using HPLC-SPE combined with CapNMR. Anal. Chem., 2007, 79(2), 727-735.
[15]
Hoffmann, T.; Krug, D.; Huttel, S.; Muller, R. Improving natural products identification through targeted LC-MS/MS in an untargeted secondary metabolomics workflow. Anal. Chem., 2014, 86(21), 10780-10788.
[16]
Schmid, I.I.; Sattler, I.I.; Grabley, S.; Thiericke, R. Natural products in high throughput screening: Automated high-quality sample preparation. J. Biomol. Screen., 1999, 4(1), 15-25.
[17]
Bugni, T.S.; Richards, B.; Bhoite, L.; Cimbora, D.; Harper, M.K.; Ireland, C.M. Marine natural product libraries for high-throughput screening and rapid drug discovery. J. Nat. Prod., 2008, 71(6), 1095-1098.
[18]
Smith, A.J.; Hancock, M.K.; Bi, K.; Andrews, J.; Harrison, P.; Vaughan, T.J. Feasibility of implementing cell-based pathway reporter assays in early high-throughput screening assay cascades for antibody drug discovery. J. Biomol. Screen., 2012, 17(6), 713-726.
[19]
Yang, F.; Fu, T.; Zhang, X.; Hu, J.; Xue, W.; Zheng, G.; Li, B.; Li, Y.; Yao, X.; Zhu, F. Comparison of computational model and X-ray crystal structure of human serotonin transporter: Potential application for the pharmacology of human monoamine transporters. Mol. Simul., 2017, 43, 1-10.
[20]
Koffas, M.; Roberge, C.; Lee, K.; Stephanopoulos, G. Metabolic engineering. Annu. Rev. Biomed. Eng., 1999, 1, 535-557.
[21]
Miralpeix, B.; Rischer, H.; Hakkinen, S.T.; Ritala, A.; Seppanen-Laakso, T.; Oksman-Caldentey, K.M.; Capell, T.; Christou, P. Metabolic engineering of plant secondary products: Which way forward? Curr. Pharm. Des., 2013, 19(31), 5622-5639.
[22]
Li, B.; Tang, J.; Yang, Q.; Li, S.; Cui, X.; Li, Y.; Chen, Y.; Xue, W.; Li, X.; Zhu, F. NOREVA: Normalization and evaluation of MS-based metabolomics data Nucleic Acids Res., 2017, 45(Web Server issue), W160-W170.
[23]
Li, B.; Tang, J.; Yang, Q.; Cui, X.; Li, S.; Chen, S.; Cao, Q.; Xue, W.; Chen, N.; Zhu, F. Performance evaluation and online realization of data-driven normalization methods used in LC/MS based untargeted metabolomics analysis. Sci. Rep., 2016, 6, 38881.
[24]
Speck-Planche, A.; Cordeiro, M.N. Simultaneous modeling of antimycobacterial activities and ADMET profiles: A chemoinformatic approach to medicinal chemistry. Curr. Top. Med. Chem., 2013, 13(14), 1656-1665.
[25]
Speck-Planche, A.; Cordeiro, M.N. Chemoinformatics for medicinal chemistry: In silico model to enable the discovery of potent and safer anti-cocci agents. Future Med. Chem., 2014, 6(18), 2013-2028.
[26]
Speck-Planche, A.; Cordeiro, M.N. Simultaneous virtual prediction of anti-Escherichia coli activities and ADMET profiles: A chemoinformatic complementary approach for high-throughput screening. ACS Comb. Sci., 2014, 16(2), 78-84.
[27]
Kleandrova, V.V.; Ruso, J.M.; Speck-Planche, A.; Dias Soeiro Cordeiro, M.N. Enabling the discovery and virtual screening of potent and safe antimicrobial peptides. Simultaneous prediction of antibacterial activity and cytotoxicity. ACS Comb. Sci., 2016, 18(8), 490-498.
[28]
Romero-Duran, F.J.; Alonso, N.; Yanez, M.; Caamano, O.; Garcia-Mera, X.; Gonzalez-Diaz, H. Brain-inspired cheminformatics of drug-target brain interactome, synthesis, and assay of TVP1022 derivatives. Neuropharmacology, 2016, 103, 270-278.
[29]
Wang, P.; Fu, T.; Zhang, X.; Yang, F.; Zheng, G.; Xue, W.; Chen, Y.; Yao, X.; Zhu, F. Differentiating physicochemical properties between NDRIs and sNRIs clinically important for the treatment of ADHD. Biochim. Biophys. Acta, 2017, 1861, 2766-2777.
[30]
Rao, H.; Zhu, F.; Yang, G.; Li, Z.; Chen, Y. Update of PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence Nucleic Acids Res, 2011, 39(Web Server issue), W385-W390.
[31]
Mitchell, W. Natural products from synthetic biology. Curr. Opin. Chem. Biol., 2011, 15(4), 505-515.
[32]
Seyedsayamdost, M.R.; Clardy, J. Natural products and synthetic biology. ACS Synth. Biol., 2014, 3(10), 745-747.
[33]
Wang, P.; Zhang, X.; Fu, T.; Li, S.; Li, B.; Xue, W.; Yao, X.; Chen, Y.; Zhu, F. Differentiating physicochemical properties between addictive and nonaddictive ADHD drugs revealed by molecular dynamics simulation studies. ACS Chem. Neurosci., 2017, 8(6), 1416-1428.
[34]
Xue, W.; Wang, P.; Li, B.; Li, Y.; Xu, X.; Yang, F.; Yao, X.; Chen, Y.; Xu, F.; Zhu, F. Identification of the inhibitory mechanism of FDA approved selective serotonin reuptake inhibitors: An insight from molecular dynamics simulation study. Phys. Chem. Chem. Phys., 2016, 18(4), 3260-3271.
[35]
Li, J.; Vederas, J.C. Drug discovery and natural products: End of an era or an endless frontier? Science, 2009, 325(5937), 161-165.
[36]
Li, Y.; Wang, P.; Li, X.; Yu, C.; Yang, H.; Zhou, J.; Xue, W.; Tan, J.; Zhu, F. The human kinome targeted by FDA approved multi-target drugs and combination products: A comparative study from the drug-target interaction network perspective. PLoS One, 2016, 11(11), e0165737.
[37]
Xu, J.; Wang, P.; Yang, H.; Zhou, J.; Li, Y.; Li, X.; Xue, W.; Yu, C.; Tian, Y.; Zhu, F. Comparison of FDA approved kinase targets to clinical trial ones: Insights from their system profiles and drug-target interaction networks. BioMed Res. Int., 2016, 2016, 2509385.
[38]
Rehan, M. An anti-cancer drug candidate OSI-027 and its analog as inhibitors of mTOR: Computational insights into the inhibitory mechanisms. J. Cell. Biochem., 2017, 118(12), 4558-4567.
[39]
Li, H.; Ma, Y.; Ma, Y.; Li, Y.; Chen, X.; Dong, W.; Wang, R. The design of novel inhibitors for treating cancer by targeting CDC25B through disruption of CDC25B-CDK2/Cyclin A interaction using computational approaches. Oncotarget, 2017, 8(20), 33225-33240.
[40]
Issa, N.T.; Wathieu, H.; Ojo, A.; Byers, S.W.; Dakshanamurthy, S. Drug metabolism in preclinical drug development: A survey of the discovery process, toxicology, and computational tools. Curr. Drug Metab., 2017, 18(6), 556-565.
[41]
Garcia, I.; Fall, Y.; Gomez, G.; Gonzalez-Diaz, H. First computational chemistry multi-target model for anti-Alzheimer, anti-parasitic, anti-fungi, and anti-bacterial activity of GSK-3 inhibitors in vitro, in vivo, and in different cellular lines. Mol. Divers., 2011, 15(2), 561-567.
[42]
Alonso, N.; Caamano, O.; Romero-Duran, F.J.; Luan, F.; Mn, D.S.C.; Yanez, M.; Gonzalez-Diaz, H.; Garcia-Mera, X. Model for high-throughput screening of multitarget drugs in chemical neurosciences: synthesis, assay, and theoretic study of rasagiline carbamates. ACS Chem. Neurosci., 2013, 4(10), 1393-1403.
[43]
Luan, F.; Cordeiro, M.N.; Alonso, N.; Garcia-Mera, X.; Caamano, O.; Romero-Duran, F.J.; Yanez, M.; Gonzalez-Diaz, H. TOPS-MODE model of multiplexing neuroprotective effects of drugs and experimental-theoretic study of new 1,3-rasagiline derivatives potentially useful in neurodegenerative diseases. Bioorg. Med. Chem., 2013, 21(7), 1870-1879.
[44]
Jia, J.; Zhu, F.; Ma, X.; Cao, Z.; Cao, Z.W.; Li, Y.; Li, Y.; Chen, Y. Mechanisms of drug combinations: Interaction and network perspectives. Nat. Rev. Drug Discov., 2009, 8(2), 111-128.
[45]
Chaudhari, R.; Tan, Z.; Huang, B.; Zhang, S. Computational polypharmacology: A new paradigm for drug discovery. Expert Opin. Drug Discov., 2017, 12(3), 279-291.
[46]
Gayvert, K.M.; Aly, O.; Platt, J.; Bosenberg, M.W.; Stern, D.F.; Elemento, O. A computational approach for identifying synergistic drug combinations. PLOS Comput. Biol., 2017, 13(1), e1005308.
[47]
Speck-Planche, A.; Cordeiro, M.N.D.S. In Bladder Cancer: Risk Factors, Emerging Treatment Strategies and Challenges; Haggerty, S., Ed.; Nova Science Publishers, Inc.: New York, 2014, pp. 71-93.
[48]
Speck-Planche, A.; Cordeiro, M.N.D.S. In Multi-Scale Approaches in Drug Discovery: From Empirical Knowledge to in Silico Experiments and Back; Speck-Planche, A., Ed.; Elsevier: Oxford, UK, 2017, pp. 127-147.
[49]
Kleandrova, V.; Speck-Planche, A. In Multi-Scale Approaches in Drug Discovery: From Empirical Knowledge to in Silico Experiments and Back; Speck-Planche, A., Ed.; Elsevier: Oxford, UK, 2017, pp. 55-81.
[50]
Gras, J. Ingenol mebutate: A new option for actinic keratosis treatment. Drugs Today (Barc), 2013, 49(1), 15-22.
[51]
Monk, B.J.; Dalton, H.; Benjamin, I.; Tanovic, A. Trabectedin as a new chemotherapy option in the treatment of relapsed platinum sensitive ovarian cancer. Curr. Pharm. Des., 2012, 18(25), 3754-3769.
[52]
Lu, S.; Wang, J. Homoharringtonine and omacetaxine for myeloid hematological malignancies. J. Hematol. Oncol., 2014, 7, 2.
[53]
VanderMolen, K.M.; McCulloch, W.; Pearce, C.J.; Oberlies, N.H. Romidepsin (Istodax, NSC 630176, FR901228, FK228, depsipeptide): A natural product recently approved for cutaneous T-cell lymphoma. J. Antibiot. (Tokyo), 2011, 64(8), 525-531.
[54]
Atanasov, A.G.; Waltenberger, B.; Pferschy-Wenzig, E.M.; Linder, T.; Wawrosch, C.; Uhrin, P.; Temml, V.; Wang, L.; Schwaiger, S.; Heiss, E.H.; Rollinger, J.M.; Schuster, D.; Breuss, J.M.; Bochkov, V.; Mihovilovic, M.D.; Kopp, B.; Bauer, R.; Dirsch, V.M.; Stuppner, H. Discovery and resupply of pharmacologically active plant-derived natural products: A review. Biotechnol. Adv., 2015, 33(8), 1582-1614.
[55]
Beutler, J.A. Natural products as a foundation for drug discovery. Curr. Protocols Pharmacol., 2009, 46(1), 9-11.
[56]
Nierode, G.; Kwon, P.S.; Dordick, J.S.; Kwon, S.J. Cell-based assay design for high-content screening of drug candidates. J. Microbiol. Biotechnol., 2016, 26(2), 213-225.
[57]
Fabricant, D.S.; Farnsworth, N.R. The value of plants used in traditional medicine for drug discovery. Environ. Health Perspect., 2001, 109(Suppl. 1), 69-75.
[58]
Pan, L.; Chai, H.B.; Kinghorn, A.D. Discovery of new anticancer agents from higher plants. Front. Biosci. (Schol. Ed.), 2012, 4, 142-156.
[59]
Zhu, F.; Qin, C.; Tao, L.; Liu, X.; Shi, Z.; Ma, X.; Jia, J.; Tan, Y.; Cui, C.; Lin, J.; Tan, C.; Jiang, Y.; Chen, Y. Clustered patterns of species origins of nature-derived drugs and clues for future bioprospecting. Proc. Natl. Acad. Sci. USA, 2011, 108(31), 12943-12948.
[60]
Wang, P.; Yang, F.; Yang, H.; Xu, X.; Liu, D.; Xue, W.; Zhu, F. Identification of dual active agents targeting 5-HT1A and SERT by combinatorial virtual screening methods. Biomed. Mater. Eng., 2015, 26(Suppl. 1), S2233-S2239.
[61]
Ronsted, N.; Symonds, M.R.; Birkholm, T.; Christensen, S.B.; Meerow, A.W.; Molander, M.; Molgaard, P.; Petersen, G.; Rasmussen, N.; Van-Staden, J.; Stafford, G.I.; Jager, A.K. Can phylogeny predict chemical diversity and potential medicinal activity of plants? A case study of Amaryllidaceae. BMC Evol. Biol., 2012, 12, 182.
[62]
Federhen, S. The NCBI Taxonomy database. Nucleic Acids Res., 2012, 40(Database issue), D136-D143.
[63]
Roepke, J.; Salim, V.; Wu, M.; Thamm, A.M.; Murata, J.; Ploss, K.; Boland, W.; De-Luca, V. Vinca drug components accumulate exclusively in leaf exudates of Madagascar periwinkle. Proc. Natl. Acad. Sci. USA, 2010, 107(34), 15287-15292.
[64]
Zhu, F.; Ma, X.; Qin, C.; Tao, L.; Liu, X.; Shi, Z.; Zhang, C.; Tan, C.; Chen, Y.; Jiang, Y. Drug discovery prospect from untapped species: Indications from approved natural product drugs. PLoS One, 2012, 7(7), e39782.
[65]
Zheng, G.; Xue, W.; Wang, P.; Yang, F.; Li, B.; Li, X.
Li, Y.; Yao, X.; Zhu, F. Exploring the Inhibitory mechanism of approved selective norepinephrine reuptake inhibitors and reboxetine enantiomers by molecular dynamics study. Sci. Rep., 2016, 6, 26883.
[66]
Li, Y.; Xu, J.; Tao, L.; Li, X.; Li, S.; Zeng, X.; Chen, S.; Zhang, P.; Qin, C.; Zhang, C.; Chen, Z.; Zhu, F.; Chen, Y. SVM-Prot 2016: A web-server for machine learning prediction of protein functional families from sequence irrespective of similarity. PLoS One, 2016, 11(8), e0155290.
[67]
Gu, J.; Gui, Y.; Chen, L.; Yuan, G.; Lu, H.; Xu, X. Use of natural products as chemical library for drug discovery and network pharmacology. PLoS One, 2013, 8(4), e62839.
[68]
Zhu, F.; Zheng, C.; Han, L.; Xie, B.; Jia, J.; Liu, X.; Tammi, M.T.; Yang, S.; Wei, Y.; Chen, Y. Trends in the exploration of anticancer targets and strategies in enhancing the efficacy of drug targeting. Curr. Mol. Pharmacol., 2008, 1(3), 213-232.
[69]
Zhu, F.; Han, L.; Chen, X.; Lin, H.; Ong, S.; Xie, B.; Zhang, H.; Chen, Y. Homology-free prediction of functional class of proteins and peptides by support vector machines. Curr. Protein Pept. Sci., 2008, 9(1), 70-95.
[70]
Tao, L.; Zhu, F.; Xu, F.; Chen, Z.; Jiang, Y.; Chen, Y. Co-targeting cancer drug escape pathways confers clinical advantage for multi-target anticancer drugs. Pharmacol. Res., 2015, 102, 123-131.
[71]
Letunic, I.; Bork, P. Interactive Tree of Life v2: Online annotation and display of phylogenetic trees made easy. Nucleic Acids Res, 2011, 39(Web server issue), W475-W478.
[72]
Li, Z.; Han, L.; Xue, Y.; Yap, C.W.; Li, H.; Jiang, L.; Chen, Y. MODEL-molecular descriptor lab: A web-based server for computing structural and physicochemical features of compounds. Biotechnol. Bioeng., 2007, 97(2), 389-396.
[73]
Kim, S.; Thiessen, P.A.; Bolton, E.E.; Chen, J.; Fu, G.; Gindulyte, A.; Han, L.; He, J.; He, S.; Shoemaker, B.A.; Wang, J.; Yu, B.; Zhang, J.; Bryant, S.H. Pub chem substance and compound databases. Nucleic Acids Res., 2016, 44(D1), D1202-D1213.
[74]
Lipinski, C.A.; Lombardo, F.; Dominy, B.W.; Feeney, P.J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev., 2001, 46(1-3), 3-26.
[75]
Wager, T.T.; Hou, X.; Verhoest, P.R.; Villalobos, A. Moving beyond rules: The development of a Central Nervous System Multi-Parameter Optimization (CNS MPO) approach to enable alignment of druglike properties. ACS Chem. Neurosci., 2010, 1(6), 435-449.
[76]
Nissink, J.W. Simple size-independent measure of ligand efficiency. J. Chem. Inf. Model., 2009, 49(6), 1617-1622.
[77]
Kohler, G.; Milstein, C. Continuous cultures of fused cells secreting antibody of predefined specificity. 1975. J. Immunol., 2005, 174(5), 2453-2455.
[78]
Druker, B.J.; Tamura, S.; Buchdunger, E.; Ohno, S.; Segal, G.M.; Fanning, S.; Zimmermann, J.; Lydon, N.B. Effects of a selective inhibitor of the Abl tyrosine kinase on the growth of Bcr-Abl positive cells. Nat. Med., 1996, 2(5), 561-566.
[79]
Katz, R. FDA: Evidentiary standards for drug development and approval. NeuroRx, 2004, 1(3), 307-316.
[80]
Kesselheim, A.S.; Wang, B.; Franklin, J.M.; Darrow, J.J. Trends in utilization of FDA expedited drug development and approval programs, 1987-2014: Cohort study. Brit. Med. J., 2015, 351, h4633.
[81]
Zhu, F.; Han, L.; Zheng, C.; Xie, B.; Tammi, M.T.; Yang, S.; Wei, Y.; Chen, Y. What are next generation innovative therapeutic targets? Clues from genetic, structural, physicochemical, and systems profiles of successful targets. J. Pharmacol. Exp. Ther., 2009, 330(1), 304-315.
[82]
Kanehisa, M.; Furumichi, M.; Tanabe, M.; Sato, Y.; Morishima, K. KEGG: New perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res., 2017, 45(D1), D353-D361.

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy