Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Physical Sciences Reviews

Ed. by Giamberini, Marta / Jastrzab, Renata / Liou, Juin J. / Luque, Rafael / Nawab, Yasir / Saha, Basudeb / Tylkowski, Bartosz / Xu, Chun-Ping / Cerruti, Pierfrancesco / Ambrogi, Veronica / Marturano, Valentina / Gulaczyk, Iwona

Online
ISSN
2365-659X
See all formats and pricing
More options …

Fundamental physical and chemical concepts behind “drug-likeness” and “natural product-likeness”

Mohd AtharORCID iD: https://orcid.org/0000-0001-6337-1026 / Alfred Ndeme Sona / Boris Davy Bekono
  • Department of Physics, Ecole Normale Supérieure, University of Yaoundé I, P.O. Box 47, Yaoundé, Cameroon
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Fidele Ntie-Kang
  • Department of Chemistry, University of Buea, P. O. Box 63 Buea, Buea, Cameroon
  • Department of Pharmaceutical Chemistry, Martin-Luther University Halle-Wittenberg, Wolfgang-Langenbeck Str. 4, 06120 Halle (Saale), Germany
  • Department of Informatics and Chemistry, University of Chemistry and Technology Prague, Technická 5 166 28 Prague 6, Dejvice, Czech Republic
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2019-09-04 | DOI: https://doi.org/10.1515/psr-2018-0101

Abstract

The discovery of a drug is known to be quite cumbersome, both in terms of the microscopic fundamental research behind it and the industrial scale manufacturing process. A major concern in drug discovery is the acceleration of the process and cost reduction. The fact that clinical trials cannot be accelerated, therefore, emphasizes the need to accelerate the strategies for identifying lead compounds at an early stage. We, herein, focus on the definition of what would be regarded as a “drug-like” molecule and a “lead-like” one. In particular, “drug-likeness” is referred to as resemblance to existing drugs, whereas “lead-likeness” is characterized by the similarity with structural and physicochemical properties of a “lead”compound, i.e. a reference compound or a starting point for further drug development. It is now well known that a huge proportion of the drug discovery is inspired or derived from natural products (NPs), which have larger complexity as well as size when compared with synthetic compounds. Therefore, similar definitions of “drug-likeness” and “lead-likeness” cannot be applied for the NP-likeness. Rather, there is the dire need to define and explain NP-likeness in regard to chemical structure. An attempt has been made here to give an overview of the general concepts associated with NP discovery, and to provide the foundational basis for defining a molecule as a “drug”, a “lead” or a “natural compound.”

Keywords: fragment analysis; drug discovery; natural products; natural product score

References

  • [1]

    CSDD-Tufts Center for the Study of Drug Development. CNS drugs take 20 % longer to develop and to improve vs. non CNS drugs. Tufts CSDD impact reports, 2018. Available at: https://csdd.tufts.edu/tuftscsddreports/. Assessed: 26 Dec 2018.

  • [2]

    Lipinski CA. Lead-and drug-like compounds: the rule-of-five revolution. Drug Discov Today. 2004;1:337–41.CrossrefGoogle Scholar

  • [3]

    Athar M, Lone MY, Jha PC. First protein drug target’s appraisal of lead-likeness descriptors to unfold the intervening chemical space. J Mol Graph Model. 2017;72:272–82.CrossrefPubMedGoogle Scholar

  • [4]

    Ghose AK, Herbertz T, Hudkins RL, Dorsey BD, Mallamo JP. Knowledge-based, central nervous system (CNS) lead selection and lead optimization for CNS drug discovery. ACS Chem Neurosci. 2011;3:50–68.PubMedGoogle Scholar

  • [5]

    Veber DF, Johnson SR, Cheng H-Y, Smith BR, Ward KW, Kopple KD. Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem. 2002;45:2615–23.PubMedCrossrefGoogle Scholar

  • [6]

    Congreve M, Carr R, Murray C, Jhoti H. A ‘Rule of Three’ for fragment-based lead discovery. Drug Discov Today. 2003;8:876–7.PubMedCrossrefGoogle Scholar

  • [7]

    Ghose AK, Viswanadhan VN, Wendoloski JJ. A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases. J Comb Chem. 1999;1:55–68.CrossrefPubMedGoogle Scholar

  • [8]

    Santos GB, Ganesan A, Emery FS. Oral administration of peptide-based drugs: beyond Lipinski’s rule. ChemMedChem. 2016;11:2245–51.CrossrefPubMedGoogle Scholar

  • [9]

    Bickerton GR, Paolini GV, Besnard J, Muresan S, Hopkins AL. Quantifying the chemical beauty of drugs. Nat Chem. 2012;4:90.CrossrefPubMedGoogle Scholar

  • [10]

    Ntie-Kang F, Lifongo LL, Judson PN, Sippl W, Efange SM. How “drug-like” are naturally occurring anti-cancer compounds? J Mol Model. 2014;20:2069.CrossrefGoogle Scholar

  • [11]

    Ntie-Kang F. An in silico evaluation of the ADMET profile of the StreptomeDB database. SpringerPlus. 2013;2:353.CrossrefGoogle Scholar

  • [12]

    Ntie-Kang F, Zofou D, Babiaka SB, Meudom R, Scharfe M, Lifongo LL, et al. AfroDb: a select highly potent and diverse natural product library from African medicinal plants. PLoS One. 2013;8:e78085.CrossrefGoogle Scholar

  • [13]

    Newman DJ, Cragg GM. Natural products as sources of new drugs from 1981 to 2014. J Nat Prod. 2016;79:629–61. (add 3 refs each till the end).PubMedCrossrefGoogle Scholar

  • [14]

    Dobson PD, Kell DB. Carrier-mediated cellular uptake of pharmaceutical drugs: an exception or the rule? Nat Rev Drug Discov. 2008;7:205.PubMedCrossrefGoogle Scholar

  • [15]

    Bauer RA, Wurst JM, Tan DS. Expanding the range of ‘druggable’targets with natural product-based libraries: an academic perspective. Curr Opin Chem Biol. 2010;14:308–14.CrossrefPubMedGoogle Scholar

  • [16]

    Overington JP, Al-Lazikani B, Hopkins AL. How many drug targets are there? Nat Rev Drug Discov. 2006;5:993.PubMedCrossrefGoogle Scholar

  • [17]

    Hopkins AL, Groom CR. The druggable genome. Nat Rev Drug Discov. 2002;1:727.PubMedCrossrefGoogle Scholar

  • [18]

    Wells JA, McClendon CL. Reaching for high-hanging fruit in drug discovery at protein–protein interfaces. Nature. 2007;450:1001.CrossrefPubMedGoogle Scholar

  • [19]

    Hert J, Irwin JJ, Laggner C, Keiser MJ, Shoichet BK. Quantifying biogenic bias in screening libraries. Nat Chem Biol. 2009;5:479.CrossrefPubMedGoogle Scholar

  • [20]

    Dobson CM. Chemical space and biology. Nature. 2004;432:824.PubMedCrossrefGoogle Scholar

  • [21]

    Bohacek RS, McMartin C, Guida WC. The art and practice of structure‐based drug design: a molecular modeling perspective. Med Res Rev. 1996;16:3–50.CrossrefPubMedGoogle Scholar

  • [22]

    Newman DJ, Cragg GM. Natural products as sources of new drugs over the last 25 years. J Nat Prod. 2007;70:461–77.CrossrefPubMedGoogle Scholar

  • [23]

    Butler MS. Natural products to drugs: natural product-derived compounds in clinical trials. Nat Prod Rep. 2008;25:475–516.CrossrefPubMedGoogle Scholar

  • [24]

    Al H. Natural products in drug discovery. Drug Discov Today. 2008;13:894–901.CrossrefPubMedGoogle Scholar

  • [25]

    Sukuru SC, Jenkins JL, Beckwith RE, Scheiber J, Bender A, Mikhailov D, et al. Plate-based diversity selection based on empirical HTS data to enhance the number of hits and their chemical diversity. J Biomolecul Screen. 2009;14:690–9.CrossrefGoogle Scholar

  • [26]

    Grabowski K, Schneider G. Properties and architecture of drugs and natural products revisited. Curr Chem Biol. 2007;1:115–27.Google Scholar

  • [27]

    Koch MA, Wittenberg L-O, Basu S, Jeyaraj DA, Gourzoulidou E, Reinecke K, et al. Compound library development guided by protein structure similarity clustering and natural product structure. Proc Nat Acad Sci USA. 2004;101:16721–6.CrossrefGoogle Scholar

  • [28]

    Koch MA, Schuffenhauer A, Scheck M, Wetzel S, Casaulta M, Odermatt A, et al. Charting biologically relevant chemical space: a structural classification of natural products (SCONP). Proc Nat Acad Sci USA. 2005;102:17272–7.CrossrefGoogle Scholar

  • [29]

    Wetzel S, Klein K, Renner S, Rauh D, Oprea TI, Mutzel P, et al. Interactive exploration of chemical space with Scaffold Hunter. Nat Chem Biol. 2009;5:581.PubMedCrossrefGoogle Scholar

  • [30]

    Renner S, Van Otterlo WA, Seoane MD, Möcklinghoff S, Hofmann B, Wetzel S, et al. Bioactivity-guided mapping and navigation of chemical space. Nat Chem Biol. 2009;5:585.CrossrefPubMedGoogle Scholar

  • [31]

    Hung AW, Ramek A, Wang Y, Kaya T, Wilson JA, Clemons PA, et al. Route to three-dimensional fragments using diversity-oriented synthesis. Proc Nat Acad Sci USA. 2011;108:6799–804.CrossrefGoogle Scholar

  • [32]

    Firth NC, Brown N, Blagg J. Plane of best fit: a novel method to characterize the three-dimensionality of molecules. J Chem Inf Model. 2012;52:2516–25.CrossrefGoogle Scholar

  • [33]

    Liao JJ. Molecular recognition of protein kinase binding pockets for design of potent and selective kinase inhibitors. J Med Chem. 2007;50:409–24.CrossrefPubMedGoogle Scholar

  • [34]

    Zinzalla G, Thurston DE. Targeting protein–protein interactions for therapeutic intervention: a challenge for the future. Future Med Chem. 2009;1:65–93.PubMedCrossrefGoogle Scholar

  • [35]

    Murray JK, Gellman SH. Targeting protein–protein interactions: lessons from p53/MDM2. Biopolymers. 2007;88:657–86.PubMedCrossrefGoogle Scholar

  • [36]

    Verdine GL, Walensky LD. The challenge of drugging undruggable targets in cancer: lessons learned from targeting BCL-2 family members. Clin Cancer Res. 2007;13:7264–70.CrossrefPubMedGoogle Scholar

  • [37]

    Berg T. Small-molecule inhibitors of protein–protein interactions. In: Martin Zacharias, editor, Protein-protein complexes: analysis, modeling and drug design. London, UK: World Scientific, 2010:318–39.Google Scholar

  • [38]

    Wilson AJ. Inhibition of protein–protein interactions using designed molecules. Chem Soc Rev. 2009;38:3289–300.CrossrefPubMedGoogle Scholar

  • [39]

    Fry DC. Drug-like inhibitors of protein-protein interactions: a structural examination of effective protein mimicry. Curr Protein Pept Sci. 2008;9:240–7.PubMedCrossrefGoogle Scholar

  • [40]

    Keskin O, Gursoy A, Ma B, Nussinov R. Principles of protein− protein interactions: What are the preferred ways for proteins to interact? Chem Rev. 2008;108:1225–44.PubMedCrossrefGoogle Scholar

  • [41]

    Singh N, Guha R, Giulianotti MA, Pinilla C, Houghten RA, Medina-Franco JL. Chemoinformatic analysis of combinatorial libraries, drugs, natural products, and molecular libraries small molecule repository. J Chem Inf Model. 2009;49:1010–24.CrossrefPubMedGoogle Scholar

  • [42]

    Feher M, Schmidt JM. Property distributions: differences between drugs, natural products, and molecules from combinatorial chemistry. J Chem Inf Comput Sci. 2003;43:218–27.PubMedCrossrefGoogle Scholar

  • [43]

    Shelat AA, Guy RK. Scaffold composition and biological relevance of screening libraries. Nat Chem Biol. 2007;3:442.CrossrefPubMedGoogle Scholar

  • [44]

    Ertl P, Roggo S, Schuffenhauer A. Natural product-likeness score and its application for prioritization of compound libraries. J Chem Inf Model. 2008;48:68–74.CrossrefPubMedGoogle Scholar

  • [45]

    Silva DG, Emery FS. Strategies towards expansion of chemical space of natural product-based compounds to enable drug discovery. Braz J Pharm Sci. 2018;54:e01004.Google Scholar

  • [46]

    Lovering F, Bikker J, Humblet C. Escape from flatland: increasing saturation as an approach to improving clinical success. J Med Chem. 2009;52:6752–6.CrossrefPubMedGoogle Scholar

  • [47]

    Firn RD, Jones CG. Natural products–a simple model to explain chemical diversity. Nat Prod Rep. 2003;20:382–91.CrossrefPubMedGoogle Scholar

  • [48]

    Maplestone RA, Stone MJ, Williams DH. The evolutionary role of secondary metabolites—a review. Gene. 1992;115:151–7.CrossrefPubMedGoogle Scholar

  • [49]

    Balamurugan R, Dekker FJ, Waldmann H. Design of compound libraries based on natural product scaffolds and protein structure similarity clustering (PSSC). Mol Biosyst. 2005;1:36–45.PubMedCrossrefGoogle Scholar

  • [50]

    Haustedt L, Mang C, Siems K, Schiewe H. Rational approaches to natural-product-based drug design. Curr Opin Drug Discov Devel. 2006;9:445–62.PubMedGoogle Scholar

  • [51]

    Jayaseelan KV, Moreno P, Truszkowski A, Ertl P, Steinbeck C. Natural product-likeness score revisited: an open-source, open-data implementation. BMC Bioinformics. 2012;13:106.CrossrefGoogle Scholar

  • [52]

    Dobson PD, Patel Y, Kell DB. ‘Metabolite-likeness’ as a criterion in the design and selection of pharmaceutical drug libraries. Drug Discov Today. 2009;14:31–40.CrossrefPubMedGoogle Scholar

  • [53]

    Baker M. Fragment-based lead discovery grows up. Nat Rev Drug Discov. 2013;12:5–7.PubMedCrossrefGoogle Scholar

  • [54]

    Chen H, Zhou X, Wang A, Zheng Y, Gao Y, Zhou J. Evolutions in fragment-based drug design: the deconstruction–reconstruction approach. Drug Discov Today. 2015;20:105–13.CrossrefPubMedGoogle Scholar

  • [55]

    Joseph-McCarthy D, Campbell AJ, Kern G, Moustakas D. Fragment-based lead discovery and design. J Chem Inf Model. 2014;54:693–704.CrossrefPubMedGoogle Scholar

  • [56]

    Murray CW, Rees DC. Opportunity knocks: organic chemistry for fragment‐based drug discovery (FBDD). Angew Chem Int Ed Engl. 2016;55:488–92.PubMedCrossrefGoogle Scholar

  • [57]

    Scott DE, Coyne AG, Hudson SA, Abell C. Fragment-based approaches in drug discovery and chemical biology. Biochemistry. 2012;51:4990–5003.CrossrefPubMedGoogle Scholar

  • [58]

    Genis D, Kirpichenok M, Kombarov R. A minimalist fragment approach for the design of natural-product-like synthetic scaffolds. Drug Discov Today. 2012;17:1170–4.PubMedCrossrefGoogle Scholar

  • [59]

    Austin MJ, Hearnshaw SJ, Mitchenall LA, McDermott PJ, Howell LA, Maxwell A, et al. A natural product inspired fragment-based approach towards the development of novel anti-bacterial agents. MedChemComm. 2016;7:1387–91.CrossrefGoogle Scholar

  • [60]

    Pascolutti M, Campitelli M, Nguyen B, Pham N, Gorse A-D, Quinn RJ. Capturing nature’s diversity. PLoS One. 2015;10:e0120942.Google Scholar

  • [61]

    Prescher H, Koch G, Schuhmann T, Ertl P, Bussenault A, Glick M, et al. Construction of a 3D-shaped, natural product like fragment library by fragmentation and diversification of natural products. Bioorg Med Chem. 2017;25:921–5.PubMedCrossrefGoogle Scholar

  • [62]

    Rodrigues T, Reker D, Schneider P, Schneider G. Counting on natural products for drug design. Nat Chem. 2016;8:531.PubMedCrossrefGoogle Scholar

  • [63]

    Over B, Wetzel S, Grütter C, Nakai Y, Renner S, Rauh D, et al. Natural-product-derived fragments for fragment-based ligand discovery. Nat Chem. 2013;5:21.PubMedCrossrefGoogle Scholar

  • [64]

    Zaid H, Raiyn J, Nasser A, Saad B, Rayan A. Physicochemical properties of natural based products versus synthetic chemicals. Open Nutraceuticals J. 2010;3:194–202.Google Scholar

  • [65]

    Wetzel S, Schuffenhauer A, Roggo S, Ertl P, Waldmann H. Cheminformatic analysis of natural products and their chemical space. CHIMIA Int J Chem. 2007;61:355–60.CrossrefGoogle Scholar

  • [66]

    Lewell XQ, Judd DB, Watson SP, Hann MM. Recap retrosynthetic combinatorial analysis procedure: a powerful new technique for identifying privileged molecular fragments with useful applications in combinatorial chemistry. J Chem Inf Comput Sci. 1998;38:511–22.CrossrefPubMedGoogle Scholar

  • [67]

    Flaherty KT, Yasothan U, Kirkpatrick P. Vemurafenib. Nat Rev Drug Discov. 2011;10:811–12.CrossrefPubMedGoogle Scholar

  • [68]

    Irwin JJ, Sterling T, Mysinger MM, Bolstad ES, Coleman RG. ZINC: a free tool to discover chemistry for biology. J Chem Inform Model. 2012;52:1757–68.CrossrefGoogle Scholar

  • [69]

    Hann MM, Oprea T. Pursuing the leadlikeness concept in pharmaceutical research. Curr Opin Chem Biol. 2004;8:255–63.CrossrefPubMedGoogle Scholar

  • [70]

    Oprea TI, Davis AM, Teague SJ, Leeson PD. Is there a difference between leads and drugs? a historical perspective. J Chem Inf Comput Sci. 2001;41:1308–15.CrossrefGoogle Scholar

  • [71]

    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.PubMedCrossrefGoogle Scholar

  • [72]

    Quinn RJ, Carroll AR, Pham NB, Baron P, Palframan ME, Suraweera L, et al. Developing a drug-like natural product library. J Nat Prod. 2008;71:464–8.PubMedCrossrefGoogle Scholar

  • [73]

    McArdle BM, Campitelli MR, Quinn RJ. A common protein fold topology shared by flavonoid biosynthetic enzymes and therapeutic targets. J Nat Prod. 2006;69:14–7.CrossrefPubMedGoogle Scholar

  • [74]

    Kellenberger E, Hofmann A, Quinn RJ. Similar interactions of natural products with biosynthetic enzymes and therapeutic targets could explain why nature produces such a large proportion of existing drugs. Nat Prod Rep. 2011;8:1483–92.Google Scholar

  • [75]

    Saldívar-González FI, Pilón-Jiménez BA, Medina-Franco JL. Chemical space of naturally occurring compounds. Phys Sci Rev. 2018. doi:.CrossrefGoogle Scholar

  • [76]

    Benet LZ, Hosey CM, Ursu O, Oprea TI. BDDCS, the rule of 5 and drugability. Adv Drug Deliv Rev. 2016;101:89–98.CrossrefPubMedGoogle Scholar

  • [77]

    Zhang MQ, Wilkinson B. Drug discovery beyond the ‘rule-of-five’. Curr Opin Biotechnol. 2007;18:478–88.PubMedCrossrefGoogle Scholar

  • [78]

    Newman DJ. From natural products to drugs. Phys Sci Rev. 2018. doi:.CrossrefGoogle Scholar

  • [79]

    Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Delivery Rev. 1997;23:3–25.CrossrefGoogle Scholar

  • [80]

    Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev. 2001;46:3–26.CrossrefPubMedGoogle Scholar

About the article

Published Online: 2019-09-04


Citation Information: Physical Sciences Reviews, 20180101, ISSN (Online) 2365-659X, DOI: https://doi.org/10.1515/psr-2018-0101.

Export Citation

© 2019 Walter de Gruyter GmbH, Berlin/Boston.Get Permission

Comments (0)

Please log in or register to comment.
Log in