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Cheminformatics techniques in antimalarial drug discovery and development from natural products 1: basic concepts

Samuel Egieyeh
  • Corresponding author
  • University of the Western Cape, South African Medical Research Council Bioinformatics Unit, South African National Bioinformatics Institute, 7535 Bellville Cape Town, South Africa
  • University of the Western Cape, School of Pharmacy, 7535 Bellville, South Africa
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  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Sarel F. Malan / Alan Christoffels
  • University of the Western Cape, South African Medical Research Council Bioinformatics Unit, South African National Bioinformatics Institute, 7535 Bellville Cape Town, South Africa
  • Other articles by this author:
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Published Online: 2019-04-20 | DOI: https://doi.org/10.1515/psr-2018-0130

Abstract

A large number of natural products, especially those used in ethnomedicine of malaria, have shown varying in vitro antiplasmodial activities. Facilitating antimalarial drug development from this wealth of natural products is an imperative and laudable mission to pursue. However, limited manpower, high research cost coupled with high failure rate during preclinical and clinical studies might militate against the pursuit of this mission. These limitations may be overcome with cheminformatic techniques. Cheminformatics involves the organization, integration, curation, standardization, simulation, mining and transformation of pharmacology data (compounds and bioactivity) into knowledge that can drive rational and viable drug development decisions. This chapter will review the application of cheminformatics techniques (including molecular diversity analysis, quantitative-structure activity/property relationships and Machine learning) to natural products with in vitro and in vivo antiplasmodial activities in order to facilitate their development into antimalarial drug candidates and design of new potential antimalarial compounds.

Keywords: natural products; antiplasmodial; cheminformatics; profiling; antimalarial; drug development

References

  • [1]

    Price M History of ancient medicine in Mesopotamia and Iran. Iranchamber.com, October 2001.Google Scholar

  • [2]

    Willcox M, Bodeker G, Rasoanaivo P, Addae-Kyereme J. Traditional medicinal plants and malaria. Boca Raton, Florida 33431: CRC Press, 2004: ISBN 9780415301121.Google Scholar

  • [3]

    Yuan H, Ma Q, Ye L, Piao G. The traditional medicine and modern medicine from natural products. Molecules. 2016;21:559.CrossrefGoogle Scholar

  • [4]

    O’Neill PM, Ward SA, Berry NG, Jeyadevan J, Biagini GA, Asadollaly E, et al. A medicinal chemistry perspective on 4-aminoquinoline antimalarial drugs. Curr Top Med Chem. 2006;6:479–507.CrossrefPubMedGoogle Scholar

  • [5]

    ElSohly HN, Croom EM, Jr, El-Feraly FS, El-Sherei MM. A large-scale extraction technique of artemisinin from Artemisia annua. J Nat Prod. 1990;53:1560–4.CrossrefGoogle Scholar

  • [6]

    De Vries PJ, Dien TK. Clinical pharmacology and therapeutic potential of artemisinin and its derivatives in the treatment of malaria. Drugs. 1996;52:818–36.CrossrefPubMedGoogle Scholar

  • [7]

    Price RN, Nosten F, Luxemburger C, Ter Kuile F, Paiphun L, Chongsuphajaisiddhi T, et al. Effects of artemisinin derivatives on malaria transmissibility. The Lancet. 1996;347:1654–8.CrossrefGoogle Scholar

  • [8]

    World Health Organization. Guidelines for the treatment of malaria. CH-1211 Geneva 27 Switzerland: World Health Organization, 2006.Google Scholar

  • [9]

    World Health Organization. Guidelines for the treatment of malaria. CH-1211 Geneva 27 Switzerland: World Health Organization, 2015.Google Scholar

  • [10]

    Laurent D, Pietra F. Antiplasmodial marine natural products in the perspective of current chemotherapy and prevention of malaria. A review. Mar Biotechnol. 2006;8:433–47.CrossrefPubMedGoogle Scholar

  • [11]

    Kaur K, Jain M, Kaur T, Jain R. Antimalarials from nature. Bioorg Med Chem. 2009;17:3229–56.PubMedCrossrefGoogle Scholar

  • [12]

    Batista R, De Jesus Silva Júnior A, De Oliveira AB. Plant-derived antimalarial agents: new leads and efficient phytomedicines. Part II. Non-alkaloidal natural products. Molecules. 2009;14:3037–72.CrossrefPubMedGoogle Scholar

  • [13]

    Mojab F. Antimalarial natural products: a review. Avicenna J Phytomedicine. 2012;2:52.Google Scholar

  • [14]

    Nondo RS, Zofou D, Moshi MJ, Erasto P, Wanji S, Ngemenya MN, et al. Ethnobotanical survey and in vitro antiplasmodial activity of medicinal plants used to treat malaria in Kagera and Lindi regions, Tanzania. J Med Plants Res. 2015;9:179–92.CrossrefGoogle Scholar

  • [15]

    Harvey AL, Edrada-Ebel R, Quinn RJ. The re-emergence of natural products for drug discovery in the genomics era. Nat Rev Drug Discovery. 2015;14:111.CrossrefGoogle Scholar

  • [16]

    Jamal S, Grover A. Cheminformatics approaches in modern drug discovery. InDrug Design: principles and Applications. Singapore: Springer, 2017:135–48.Google Scholar

  • [17]

    Gasteiger J. Handbook of chemoinformatics. Weinheim, Germany: Wiley-VCH, 2003:3–5.Google Scholar

  • [18]

    Ertl P. Cheminformatics and its role in the modern drug discovery process. Basel, Switzerland: University of Strasbourg. Novartis. nd Web 2014:18.Google Scholar

  • [19]

    Weisgerber DW. Chemical abstracts service chemical registry system: history, scope, and impacts. J Am Soc Inf Sci. 1997;48:349–60.CrossrefGoogle Scholar

  • [20]

    Covell DG. In: Tudor Oprea (University of New Mexico), editor. Chemoinformatics in drug discovery. Weinheim: Wiley-VCH, 2005:xxii 493. 17 × 25 cm. ISBN 3-527-30753-2.Google Scholar

  • [21]

    Brown FK. Chemoinformatics: what is it and how does it impact drug discovery. Annu Rep Med Chem. 1998;33:375–84.Google Scholar

  • [22]

    Ekins S, Freundlich JS, Hobrath JV, White EL, Reynolds RC. Combining computational methods for hit to lead optimization in Mycobacterium tuberculosis drug discovery. Pharm Res. 2014;31:414–35.CrossrefPubMedGoogle Scholar

  • [23]

    Tian S, Wang J, Li Y, Li D, Xu L, Hou T. The application of in silico drug-likeness predictions in pharmaceutical research. Adv Drug Deliv Rev. 2015;23:2–10.Google Scholar

  • [24]

    Hu Y, Stumpfe D, Bajorath J. Lessons learned from molecular scaffold analysis. J Chem Inf Model. 2011;51:1742–53.PubMedCrossrefGoogle Scholar

  • [25]

    Yongye AB, Waddell J, Medina‐Franco JL. Molecular scaffold analysis of natural products databases in the public domain. Chem Biol Drug Des. 2012;80:717–24.PubMedCrossrefGoogle Scholar

  • [26]

    Topliss J. Quantitative structure-activity relationships of drugs. Amsterdam, Netherlands: Elsevier, 2012.Google Scholar

  • [27]

    Reymond J, Ruddigkeit L, Blum L, van Deursen R. The enumeration of chemical space. Wiley Interdiscip Rev: Comput Mol Sci. 2012;2:717–33.Google Scholar

  • [28]

    Sud M, Fahy E, Subramaniam S. Template-based combinatorial enumeration of virtual compound libraries for lipids. J Cheminform. 2012;4:23.PubMedCrossrefGoogle Scholar

  • [29]

    Sanhueza CA, Cartmell J, El-Hawiet A, Szpacenko A, Kitova EN, Daneshfar R, et al. Evaluation of a focused virtual library of heterobifunctional ligands for Clostridium difficile toxins. Org Biomol Chem. 2015;13:283–98.CrossrefPubMedGoogle Scholar

  • [30]

    Feunang YD, Eisner R, Knox C, Chepelev L, Hastings J, Owen G, et al. ClassyFire: automated chemical classification with a comprehensive, computable taxonomy. J Cheminform. 2016;8:61.PubMedCrossrefGoogle Scholar

  • [31]

    Ghahremanpour MM, Van Maaren PJ, Van Der Spoel D. The Alexandria library, a quantum-chemical database of molecular properties for force field development. Sci Data. 2018;5:180062.PubMedCrossrefGoogle Scholar

  • [32]

    Todeschini R, Consonni V. Handbook of molecular descriptors. Weinheim: John Wiley & Sons, 2008.Google Scholar

  • [33]

    Planey SL, Kumar R. Lipophilicity indices for drug development. J Appl Biopharm Pharmacokinet. 2013;1:31–6.Google Scholar

  • [34]

    Kratochvíl M, Vondrášek J, Galgonek J. Sachem: a chemical cartridge for high-performance substructure search. J Cheminform. 2018;10:27.CrossrefPubMedGoogle Scholar

  • [35]

    Evans DA. History of the Harvard ChemDraw project. Angew Chem Int Ed. 2014;53:11140–5.CrossrefGoogle Scholar

  • [36]

    Backman TW, Cao Y, Girke T. ChemMine tools: an online service for analyzing and clustering small molecules. Nucleic Acids Res. 2011;39:W486–91.PubMedCrossrefGoogle Scholar

  • [37]

    Skinnider MA, Dejong CA, Franczak BC, McNicholas PD, Magarvey NA. Comparative analysis of chemical similarity methods for modular natural products with a hypothetical structure enumeration algorithm. J Cheminform. 2017;9:46.PubMedCrossrefGoogle Scholar

  • [38]

    Saldívar-González FI, Naveja JJ, Palomino-Hernández O, Medina-Franco JL. Getting SMARt in drug discovery: chemoinformatics approaches for mining structure–multiple activity relationships. RSC Adv. 2017;7:632–41.CrossrefGoogle Scholar

  • [39]

    Daina A, Michielin O, Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep. 2017;7:42717.CrossrefPubMedGoogle Scholar

  • [40]

    Chen H, Engkvist O, Wang Y, Olivecrona M, Blaschke T. The rise of deep learning in drug discovery. Drug Discov Today. 2018;23:1241–50.CrossrefPubMedGoogle Scholar

  • [41]

    Hessler G, Baringhaus K. Artificial intelligence in drug design. Molecules. 2018;23:2520.CrossrefGoogle Scholar

  • [42]

    Ekins S, Clark AM, Dole K, Gregory K, Mcnutt AM, Spektor AC. Data mining and computational modeling of high-throughput screening datasets. Methods in Molecular Biology. Heidelberg: Springer, 2018:197–221.Google Scholar

  • [43]

    Grisoni F, Ballabio D, Todeschini R, Consonni V. Molecular descriptors for structure–activity applications: a hands-on approach. Computational toxicology. Heidelberg: Springer, 2018:3–53.Google Scholar

  • [44]

    Subramanian U, Sivapunniyam A, Pudukadu Munusamy A, Sundaram R. An in silico approach towards the prediction of druglikeness properties of inhibitors of plasminogen activator inhibitor1. Adv Bioinf. 2014;2014:385418.Google Scholar

  • [45]

    Tian S, Wang J, Li Y, Li D, Xu L, Hou T. The application of in silico drug-likeness predictions in pharmaceutical research. Adv Drug Deliv Rev. 2015;86:2–10.CrossrefPubMedGoogle Scholar

  • [46]

    Wu Y, Hu M, Yang L, Li X, Bian J, Jiang F, et al. Novel natural-product-like caged xanthones with improved druglike properties and in vivo antitumor potency. Bioorg Med Chem Lett. 2015;15:2584–8.Google Scholar

  • [47]

    Wawer MJ, Li K, Gustafsdottir SM, Ljosa V, Bodycombe NE, Marton MA, et al. Toward performance-diverse small-molecule libraries for cell-based phenotypic screening using multiplexed high-dimensional profiling. Proceedings of the National Academy of Sciences. 2014;111:10911–16.CrossrefGoogle Scholar

  • [48]

    Giroud C, Du Y, Marin M, Min Q, Jui NT, Fu H, et al. Screening and functional profiling of small-molecule HIV-1 entry and fusion inhibitors. Assay Drug Dev Technol. 2017;15:53–63.PubMedCrossrefGoogle Scholar

  • [49]

    Xia J, Hu H, Xue W, Wang XS, Wu S. The discovery of novel HDAC3 inhibitors via virtual screening and in vitro bioassay. J Enzyme Inhib Med Chem. 2018;33:525–35.CrossrefPubMedGoogle Scholar

  • [50]

    Scott JS, Waring MJ. Practical application of ligand efficiency metrics in lead optimisation. Bioorg Med Chem. 2018;26:3006–15.PubMedCrossrefGoogle Scholar

  • [51]

    Heikal A, Nakatani Y, Jiao W, Wilson C, Rennison D, Weimar MR, et al. ‘Tethering’fragment-based drug discovery to identify inhibitors of the essential respiratory membrane protein type II NADH dehydrogenase. Bioorg Med Chem Lett. 2018;28:2239–43.CrossrefPubMedGoogle Scholar

  • [52]

    Stamford AW, Scott JD, Li SW, Babu S, Tadesse D, Hunter R, et al. Discovery of an orally available, brain penetrant BACE1 inhibitor that affords robust CNS Aβ reduction. ACS Med Chem Lett. 2012;3:897–902.PubMedCrossrefGoogle Scholar

  • [53]

    Hopkins AL, Groom CR, Alex A. Ligand efficiency: a useful metric for lead selection. Drug Discov Today. 2004;9:430–1.PubMedCrossrefGoogle Scholar

  • [54]

    Carr RA, Congreve M, Murray CW, Rees DC. Fragment-based lead discovery: leads by design. Drug Discov Today. 2005;10:987–92.PubMedCrossrefGoogle Scholar

  • [55]

    Mortenson PN, Murray CW. Assessing the lipophilicity of fragments and early hits. J Comput Aided Mol Des. 2011;25:663–7.CrossrefPubMedGoogle Scholar

  • [56]

    Leeson PD, Springthorpe B. The influence of drug-like concepts on decision-making in medicinal chemistry. Nat Rev Drug Discovery. 2007;6:881–90.CrossrefGoogle Scholar

  • [57]

    Mowbray CE, Burt C, Corbau R, Gayton S, Hawes M, Perros M, et al. Pyrazole NNRTIs 4: selection of UK-453,061 (lersivirine) as a development candidate. Bioorg Med Chem Lett. 2009;19:5857–60.PubMedCrossrefGoogle Scholar

  • [58]

    Tarcsay A, Nyíri K, Keserű GM. Impact of lipophilic efficiency on compound quality. J Med Chem. 2012;55:1252–60.CrossrefPubMedGoogle Scholar

  • [59]

    Wager TT, Chandrasekaran RY, Hou X, Troutman MD, Verhoest PR, Villalobos A, et al. Defining desirable central nervous system drug space through the alignment of molecular properties, in vitro ADME, and safety attributes. ACS Chem Neurosci. 2010;1:420–34.CrossrefPubMedGoogle Scholar

  • [60]

    Keserü GM, Makara GM. The influence of lead discovery strategies on the properties of drug candidates. Nat Rev Drug Discovery. 2009;8:203–12.CrossrefGoogle Scholar

  • [61]

    Egieyeh SA, Syce J, Malan SF, Christoffels A. Prioritization of anti-malarial hits from nature: chemo-informatic profiling of natural products with in vitro antiplasmodial activities and currently registered anti-malarial drugs. Malar J. 2016;15:50.PubMedCrossrefGoogle Scholar

  • [62]

    Larose DT. Discovering knowledge in data: an introduction to data mining. Hoboken, New Jersey.: John Wiley & Sons, 2014.Google Scholar

  • [63]

    Thomas MC, Zhu W, Romagnoli JA. Data mining and clustering in chemical process databases for monitoring and knowledge discovery. J Process Control. 2018;67:160–75.CrossrefGoogle Scholar

  • [64]

    In: Bajorath J, Bajorath J, editor(s). Chemoinformatics and computational chemical biology. Totowa, New Jersey: Humana Press, 2011.Google Scholar

  • [65]

    Bajorath J. Chemoinformatics: concepts, methods, and tools for drug discovery. Heidelberg: Springer Science & Business Media, 2004.Google Scholar

  • [66]

    Sheridan RP, Kearsley SK. Why do we need so many chemical similarity search methods? Drug Discov Today. 2002;7:903–11.PubMedCrossrefGoogle Scholar

  • [67]

    Varnek A, Tropsha A. Chemoinformatics approaches to virtual screening. Cambridge, United Kingdom: Royal Society of Chemistry, 2008.Google Scholar

  • [68]

    Guha R. Exploring structure–activity data using the landscape paradigm. Wiley Interdiscip Rev: Comput Mol Sci. 2012;2:829–41.Google Scholar

  • [69]

    Hu Y, Stumpfe D, Bajorath J. Advancing the activity cliff concept. F1000Research. 2013;2:199.PubMedCrossrefGoogle Scholar

  • [70]

    Stumpfe D, Hu Y, Dimova D, Bajorath J. Recent progress in understanding activity cliffs and their utility in medicinal chemistry: miniperspective. J Med ChemJJ. 2013;57:18–28. DOI: .CrossrefGoogle Scholar

  • [71]

    Maggiora GM. On outliers and activity cliffs why QSAR often disappoints. J Chem Inf Model. 2006;46:1535–1535.PubMedCrossrefGoogle Scholar

  • [72]

    Sander T, Freyss J, von Korff M, Rufener C. DataWarrior: an open-source program for chemistry aware data visualization and analysis. J Chem Inf Model. 2015;55:460–73.CrossrefPubMedGoogle Scholar

  • [73]

    Golbraikh A, Tropsha A. 12 QSAR/QSPR revisited. In: Engel T, Gasteiger J, editor(s). Chemoinformatics: basic concepts and methods. Weinheim: Wiley-VCH, 2018:465.Google Scholar

  • [74]

    Dimova D, Stumpfe D, Bajorath J. Systematic assessment of coordinated activity cliffs formed by kinase inhibitors and detailed characterization of activity cliff clusters and associated SAR information. Eur J Med Chem. 2015;90:414–27.CrossrefPubMedGoogle Scholar

  • [75]

    Naveja JJ, Medina-Franco JL. Activity landscape of DNA methyltransferase inhibitors bridges chemoinformatics with epigenetic drug discovery. Expert Opin Drug Discov. 2015;10:1059–70.PubMedCrossrefGoogle Scholar

  • [76]

    Ojeda-Montes MJ, Gimeno A, Tomas‐Hernández S, Cereto‐Massagué A, Beltrán‐Debón R, Valls C, et al. Activity and selectivity cliffs for DPP-IV inhibitors: lessons we can learn from SAR studies and their application to virtual screening. Med Res Rev. 2018;38:1874–915.CrossrefPubMedGoogle Scholar

  • [77]

    Afendi FM, Ono N, Nakamura Y, Nakamura K, Darusman LK, Kibinge N, et al. Data mining methods for omics and knowledge of crude medicinal plants toward big data biology. Comput Struct Biotechnol J. 2013;4:1–14.Google Scholar

  • [78]

    Baumann K, Becker GF, Mestres J, Schneider G. Systems approaches and big data in molecular informatics. Mol Inform. 2015;34:2–2.CrossrefPubMedGoogle Scholar

  • [79]

    Fourches D. Cheminformatics: at the crossroad of eras. Application of computational techniques in pharmacy and medicine. Heidelberg: Springer, 2014:539–46.Google Scholar

  • [80]

    Melagraki G, Afantitis A. Editorial [Thematic issue: advances in cheminformatics: drug discovery, computational toxicology and nanomaterials [Part I]]. Comb Chem High Throughput Screen. 2015;18:236–7.CrossrefGoogle Scholar

  • [81]

    Xu J, Hagler A. Chemoinformatics and drug discovery. Molecules. 2002;7:566–600.CrossrefGoogle Scholar

About the article

Samuel Egieyeh

Dr. Samuel Ayodele Egieyeh is a seasoned and experienced (over 20 years) pharmacist with B.Pharm (University of Lagos, Nigeria), M.Pharm (University of the Western Cape, Cape Town South Africa) and Ph.D. in Bioinformatics (University of the Western Cape, Cape Town South Africa). He also has post-graduate certificates in clinical research and drug development from the University of Basel, Basel Switzerland. He started his career as a research fellow in 2001 at the Department of Pharmaceutics and Pharmaceutical Technology, National Institute for Pharmaceutical Research and Development (NIPRD), Abuja Nigeria where he was involved in the formulation, production and quality control of herbal products (a remedy for sickle cell anemia and malaria). He was later posted, as a pharmacist in charge, to the Antiretroviral Therapy Clinic where implemented various pharmaceutical care strategies for HIV infected clients under the Presidential Emergency Plan For AIDS Relief (PEPFAR) project. In 2008, he joined the International Centre for Genetic Engineering and Biotechnology (ICGEB), Cape Town component for a two-year research fellowship. He is currently a lecturer at the Department of Basic pharmacology and Clinical Pharmacy, School of Pharmacy, University of the Western Cape, Bellville South Africa. He is also a facilitator for the University of the Western Cape-Healthcare Learning post-graduate programme (Masters of Science in Pharmaceutical Medicine and Regulatory Sciences). His research focuses on computational drug discovery, design and development, analysis and interpretation of chemical and bioactivity data using Cheminformatics, Bioinformatics, Machine Learning and Biostatistics techniques in conjunction with relevant in-vitro bioassays in order to discover and design novel drug candidates, especially from natural products, for infectious and non-infectious diseases. His career goal is to contribute to the improvement of healthcare worldwide through research in drug and development. His personal goals are to impart knowledge to the next generation through teaching and mentoring and to serve God and humanity. Contact details: segieyeh@uwc.ac.za, segieyeh@gmail.com, +27843477250


Published Online: 2019-04-20


Citation Information: Physical Sciences Reviews, Volume 4, Issue 7, 20180130, ISSN (Online) 2365-659X, DOI: https://doi.org/10.1515/psr-2018-0130.

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