Author(s): Anil Kumar Sahdev, Priya Gupta, Kanika Manral, Preeti Rana, Anita Singh

Email(s): dranitaku@gmail.com

DOI: 10.52711/0974-360X.2023.00246   

Address: Anil Kumar Sahdev1, Priya Gupta1, Kanika Manral1, Preeti Rana1, Anita Singh2*
1Research Scholar, Department of Pharmaceutical Sciences, Faculty of Technology Sir J.C. Bose Technical Campus, Bhimtal, Kumaun University Nainital, Uttarakhand, India.
2Head and Professor, Department of Pharmaceutical Sciences, Faculty of Technology Sir J.C. Bose Technical Campus, Bhimtal, Kumaun University Nainital, Uttarakhand, India.
*Corresponding Author

Published In:   Volume - 16,      Issue - 3,     Year - 2023


ABSTRACT:
The pharmacophore concept was first put forward as a useful picture of drug interactions almost a century ago, and with the rise in computational power over the last few decades, has become a well-established CADD method with numerous different applications in drug discovery. Depending on the prior knowledge of the system, pharmacophores can be used to identify derivatives of compounds, change the scaffold to new compounds with a similar target, virtual screen for novel inhibitors, profile compounds for ADME-tox, investigate possible off-targets, or just complement other molecular methods “chemical groups” or functions in a molecule were responsible for a biological effect, and molecules with similar effect had similar functions in common. The word pharmacophore was coined much later, by Schueler in his 1960 book Chemobiodynamics and Drug Design, and was defined as “a molecular framework that carries (phoros) the essential features responsible for a drug’s (Pharmacon) biological activity.


Cite this article:
Anil Kumar Sahdev, Priya Gupta, Kanika Manral, Preeti Rana, Anita Singh. An Overview on Pharmacophore: Their significance and importance for the activity of Drug Design. Research Journal of Pharmacy and Technology 2023; 16(3):1496-2. doi: 10.52711/0974-360X.2023.00246

Cite(Electronic):
Anil Kumar Sahdev, Priya Gupta, Kanika Manral, Preeti Rana, Anita Singh. An Overview on Pharmacophore: Their significance and importance for the activity of Drug Design. Research Journal of Pharmacy and Technology 2023; 16(3):1496-2. doi: 10.52711/0974-360X.2023.00246   Available on: https://www.rjptonline.org/AbstractView.aspx?PID=2023-16-3-82


REFERENCES:
1.    Ehrlich, P., 1909. Über den jetzigen Stand der Chemotherapie. Berichte der deutschen chemischen Gesellschaft, 42(1), pp.17-47. doi.org/10.1002/cber.19090420105.
2.    Güner O, Bowen J. Setting the record straight: The origin of the pharmacophore concept. Journal of chemical information and modeling. 2014 May 27;54(5):1269-83. doi.org/10.1021/ci5000533.
3.    Wermuth C, Ganellin C, Lindberg P, Mitscher L. Glossary of terms used in medicinal chemistry (IUPAC recommendations 1998). Pure Appl Chem. 1998;70:1129–1143.doi.org/10.1351/pac199870051129.
4.    Gund P. Evolution of the pharmacophore Concept in Pharmaceutical Research. In: Güner OF, editor. Pharmacophore Perception, Development, and Use in Drug Design. La Jolla: Internat’l University Line. ISBN 0-9636817-6-1.
5.    Gregor M, Muskal S. Pharmacophore fingerprinting. 1. Application to QSAR and focused library design. J Chem Inf Comput Sci. 1999;39(3):569–574. doi: 10.1021/ci980159j.
6.    Gregor M, Muskal S. Pharmacophore fingerprinting. 2. Application to primary library design. J Chem Inf Comput Sci. 2000;40(1):117–125.doi.org/10.1021/ci990313h.
7.    Mason J, Morize I, Menard P, Cheney D, Hulme C, Labaudiniere R. New 4-point pharmacophore method for molecular similarity and diversity applications: overview of the method and applications, including a novel approach to the design of combinatorial libraries containing privileged substructures. J Med Chem. 1999;42(17):3251–3264. doi.org/10.1021/jm9806998.
8.    Geppert T, Lipsky P. Antigen presentation at the inflammatory site. Crit Rev Immunol. 1989;9(4):313–362.
9.    Sheridan R, Rusinko A 3rd, Nilakantan R, Venkataraghavan R. Searching for pharmacophores in large coordinate data bases and its use in drug design. Proc Natl Acad Sci U S A. 1989;86(20):8165–8169. doi: 10.1073/pnas.86.20.8165.
10.    Jones G, Willett P, Glen R. A genetic algorithm for flexible molecular overlay and pharmacophore elucidation. J Comput Aided Mol Des. 1995;9(6):532–549. doi.org/10.1021/acsomega.1c07144.
11.    Goto J, Kataoka R, Hirayama N. Ph4Dock: pharmacophore-based protein-ligand docking. J Med Chem. 2004;47(27):6804–6811. doi.org/10.1021/jm0493818.
12.    Wolber G, Seidel T, Bendix F, Langer T. Molecule-pharmacophore superpositioning and pattern matching in computational drug design. Drug Discov Today. 2008;13(1–2):23–29 doi.org/10.1007/978-3-030-14632-0_4. doi: 10.1007/s11030-021-10266-8.
13.    Keiser M, Roth B, Armbruster B, Ernsberger P, Irwin J, Shoichet B. Relating protein pharmacology by ligand chemistry. Nat Biotechnol. 2007;25(2):197–206. doi: 10.1038/nbt1284.
14.    Langer T, Hoffman R. Pharmacophores and Pharmacophore Searches. Mannhold R, Kubinyi H, Folkers G, editors. Hoboken: John Wiley & Sons; 2006:395.
15.    Liu X, Zhu F, Ma X, et al. Predicting targeted polypharmacology for drug repositioning and multi- target drug discovery. Curr Med Chem. 2013;20(13):1646–1661. doi: 10.2174/0929867311320130005.
16.    Thai K, Ngo T, Tran TD, Le M. Pharmacophore modeling for antitargets. Curr Top Med Chem. 2013;13(9):1002–1014.  doi: 10.2174/1568026611313090004.
17.    Luu T, Malcolm N, Nadassy K. Pharmacophore modeling methods in focused library selection – applications in the context of a new classification scheme. Comb Chem High Throughput Screen. 2011;14(6):488–499. doi: 10.2174/138620711795767820.
18.    Jose R, Voet A, Broos K, et al. An integrated fragment based screening approach for the discovery of small molecule modulators of the VWF-GPIbalpha interaction. Chem Commun (Camb). 2012;48(92):11349–11351. doi: 10.1039/c2cc35269a.
19.    Voet A, Kumar A, Berenger F, Zhang K. Combining in silico and in cerebro approaches for virtual screening and pose prediction in SAMPL4. J Comput Aided Mol Des. 2014;28(4):363-373. doi: 10.1007/s10822-013-9702-2.
20.    Wells J, McClendon C. Reaching for high-hanging fruit in drug discovery at protein-protein interfaces. Nature. 2007;450(7172):1001–1009. doi: 10.1038/nature06526.
21.    Wilson A. Inhibition of protein-protein interactions using designed molecules. Chem Soc Rev. 2009;38:3289–3300. doi.org/10.1039/B807197G.
22.    Fry D. Drug-like inhibitors of protein-protein interactions: a structural examination of effective protein mimicry. Curr Protein Pept Sci. 2008;9(3):240–247. doi: 10.2174/138920308784533989.
23.    Hähnke V, Schneider G. Pharmacophore alignment search tool: influence of scoring systems on text-based similarity searching. J Comput Chem. 2011;32(8):1635–1647. doi.org/10.1002/jcc.21741.
24.    Catalyst (r). Vol San Diego: Accelrys, Inc.; 2014. Available from: http://accelrys.com/products/discovery-studio/pharmacophore-ligand-based-design.html. Accessed September 5, 2014. doi.org/10.2147/JRLCR.S46843.
25.    Sanders M, McGuire R, Roumen L, et al. From the protein’s perspective: the benefits and challenges of protein structure-based pharmacophore modeling. Med Chem Commun.2012;3:28-38. doi.org/10.1039/C1MD00210D.
26.    Wolber G, Langer T. LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J Chem Inf Model. 2005;45(1):160–169. doi.org/10.1021/ci049885e.
27.    Desaphy J, Azdimousa K, Kellenberger E, Rognan D. Comparison and druggability prediction of protein-ligand binding sites from pharmacophore-annotated cavity shapes. J Chem Inf Model. 2012;52(8):2287–2299. doi: 10.1021/ci300184x.
28.    Böhm H. The computer program LUDI: a new method for the de novo design of enzyme inhibitors. J Comput Aided Mol Des. 1992;6(1):61–78.  doi: 10.1007/bf00124387.
29.    Barillari C, Marcou G, Rognan D. Hot-spots-guided receptor-based pharmacophores (HS-Pharm): a knowledge-based approach to identify ligand-anchoring atoms in protein cavities and prioritize structure-based pharmacophores. J Chem Inf Model. 2008;48(7):1396–1410. doi: 10.1021/ci800064z
30.    Goodford P. A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J Med Chem. 1985;28(7):849–857.  doi: 10.1021/jm00145a002
31.    Tintori C, Corradi V, Magnani M, Manetti F, Botta M. Targets looking for drugs: a multistep computational protocol for the development of structure-based pharmacophores and their applications for hit discovery. J Chem Inf Model. 2008;48(11):2166–2179. doi.org/10.1021/ci800105p.
32.    Voet A, Helsen C, Zhang K, Claessens F. The discovery of novel human androgen receptor antagonist chemotypes using a combined pharmacophore screening procedure. ChemMedChem. 2013;8(4):644–651. doi: 10.1002/cmdc.201200549.
33.    Helsen C, Van den Broeck T, Voet A, et al. Androgen receptor antagonists for prostate cancer therapy. Endocr Relat Cancer. 2014;21(4): T105–T118.  doi: 10.1530/erc-13-0545.
34.    Kumar A, Zhang K. Hierarchical virtual screening approaches in small molecule drug discovery. Methods. Epub July 27, 2014. doi: 10.1016/j.ymeth.2014.07.007.
35.    Dunbar J, Smith R, Yang C, et al. CSAR benchmark exercise of 2010: selection of the protein-ligand complexes. J Chem Inf Model. 2011;51(9):2036–2046. doi.org/10.1021/ci200082t.
36.    Damm-Ganamet K, Smith R, Dunbar J, Stuckey J, Carlson H. CSAR benchmark exercise 2011-2012: evaluation of results from docking and relative ranking of blinded congeneric series. J Chem Inf Model. 2013;53(8):1853–1870 . doi.org/10.1021/ci400025f.
37.    Hindle S, Rarey M, Buning C, Lengaue T. Flexible docking under pharmacophore type constraints. J Comput Aided Mol Des. 2002;16(2):129–149. doi: 10.1023/a:1016399411208.
38.    Hu B, Lill M. Protein pharmacophore selection using hydration-site analysis. J Chem Inf Model. 2012;52(4):1046–1060. doi.org/ 10.1021/ci200620h.
39.    Hu B, Lill M. PharmDock: a pharmacophore-based docking program. J Cheminform. 2014;6:14. doi.org/10.1186/1758-2946-6-14
40.    Mobley D, Liu S, Lim N, et al. Blind prediction of HIV integrase binding from the SAMPL4 challenge. J Comput Aided Mol Des. 2014;28(4):327–345. doi.org/10.1007%2Fs10822-014-9723-5.
41.    Lin J, Lu A. Role of pharmacokinetics and metabolism in drug discovery and development. Pharmacol Rev. 1997;49(4):403–449.
42.    Alavijeh M, Palmer A. The pivotal role of drug metabolism and pharmacokinetics in the discovery and development of new medicines. IDrugs. 2004;7(8):755–763.
43.    Guner O, Bowen J. Pharmacophore modeling for ADME. Curr Top Med Chem. 2013;13(11):1327-1342. doi: 10.2174/15680266113139990037
44.    Yamashita F, Hashida M. In silico approaches for predicting ADME properties of drugs. Drug Metab Pharmacokinet. 2004;19(5):327–338. doi: 10.2133/dmpk.19.327
45.    Tanaka E. Clinically important pharmacokinetic drug-drug interactions: role of cytochrome P450 enzymes.  J Clin Pharm Ther. 1998; 23(6):403–416.de Groot MJ,  doi: 10.1046/j.1365-2710.1998.00086.x
46.    Ekins S. Pharmacophore modeling of cytochromes P450. Adv Drug Deliv Rev. 2002;54(3):367–383. doi.org/10.2147/JRLCR.S46843
47.    Ekins S, de Groot M, Jones J. Pharmacophore and three-dimensional quantitative structure activity relationship methods for modeling cytochrome p450 active sites. Drug Metab Dispos. 2001;29(7):936–944. doi.org/10.1124/dmd.106.013888
48.    Sorich M, Miners J, McKinnon R, Smith P. Multiple pharmacophores for the investigation of human UDP-glucuronosyltransferase isoform substrate selectivity. Mol Pharmacol. 2004;65(2):301–308. doi10.1124/mol.65.2.301.
49.    Sorich M, Smith P, McKinnon R, Miners J. Pharmacophore and quantitative structure activity relationship modelling of UDP-glucuronosyltransferase 1A1 (UGT1A1) substrates. Pharmacogenetics. 2002;12(8):635–645. doi: 10.1021/jm020397c.
50.    Koutsoukas A, Simms B, Kirchmair J, et al. From in silico target prediction to multi-target drug design: current databases, methods and applications. J Proteomics. 2011;74(12):2554–2574. doi: 10.1016/j.jprot.2011.05.011.
51.    Rollinger J, Schuster D, Danzl B, et al. In silico target fishing for rationalized ligand discovery exemplified on constituents of Ruta graveolens. Planta Med. 2009;75(3):195–204. doi: 10.1055/s-0028-1088397
52.    Hu Y, Bajorath J. Polypharmacology directed compound data mining: identification of promiscuous chemotypes with different activity profiles and comparison to approved drugs. J Chem Inf Model. 2010;50(12):2112–2118. doi: 10.1021/ci1003637
53.    Scior T, Bender A, Tresadern G, et al. Recognizing pitfalls in virtual screening: a critical review. J Chem Inf Model. 2012;52(4):867–881. doi: 10.1021/ci200528d
54.    Kirchmair J, Wolber G, Laggner C, Langer T. Comparative performance assessment of the conformational model generators omega and catalyst: a large-scale survey on the retrieval of protein-bound ligandconformations. J Chem Inf Model. 2006;46(4):1848–1861. doi: 10.1021/ci060084g
55.    Kirchmair J, Laggner C, Wolber G, Langer T. Comparative analysis of protein-bound ligand conformations with respect to catalyst’s conformational space subsampling algorithms. J Chem Inf Model. 2005;45(2):422–430. doi: 10.1021/ci049753l
56.    De Luca L, Barreca M, Ferro S, et al. Pharmacophore-based discovery of small-molecule inhibitors of protein-protein interactions between HIV-1 integrase and cellular cofactor LEDGF/p75. ChemMedChem. 2009;4(8):1311–1316. doi: 10.1002/cmdc.200900070.
57.    Christ F, Voet A, Marchand A, et al. Rational design of small-molecule inhibitors of the LEDGF/p75-integrase interaction and HIV replication. Nat Chem Biol. 2010;6(6):442–448. doi: 10.1038/nchembio.370
58.    Vancraenenbroeck R, De Raeymaecker J, Lobbestael E, et al. In silico, in vitro and cellular analysis with a kinome-wide inhibitor panel correlates cellular LRRK2 dephosphorylation to inhibitor activity on LRRK2. Front Mol Neurosci. 2014;7:51. doi: 10.3389/fnmol.2014.00051
59.    Schomburg K, Bietz S, Briem H, Henzler A, Urbaczek S, Rarey M. Facing the challenges of structure-based target prediction by inverse virtual screening. J Chem Inf Model. 2014;54(6):1676–1686.  doi: 10.1021/ci500130e.
60.    Kumar A, Voet A, Zhang K. Fragment based drug design: from experimental to computational approaches. Curr Med Chem. 2012;19(30):5128–5147.  doi: 10.2174/092986712803530467
61.    Böhm H. A novel computational tool for automated structure-based drug design. J Mol Recognit. 1993;6(3):131–137. doi: 10.1002/jmr.300060305
62.    Lippert T, Schulz-Gasch T, Roche O, Guba W, Rarey M. De novo design by pharmacophore-based searches in fragment spaces. J Comput Aided Mol Des. 2011;25(10):931–945. doi: 10.1007/s10822-011-9473-6. doi: 10.1007/s10822-011-9473-6
63.    Cavalluzzo C, Voet A, Christ F, et al. De novo design of small molecule inhibitors targeting the LEDGF/p75-HIV integrase interaction. RSC Adv. 2012;2:974. doi.org/10.1039/C1RA00582K
64.    Cavalluzzo C, Christ F, Voet A, et al. Identification of small peptides inhibiting the integrase-LEDGF/p75 interaction through targeting the cellular co-factor. J Pept Sci. 2013;19(10):651–658. doi: 10.1002/psc.2543.
65.    Voet A, Berenger F, Zhang K. Electrostatic similarities between protein and small molecule ligands facilitate the design of protein-protein interaction inhibitors. PLoS One. 2013;8(10):e75762. doi.org/10.1371/journal.pone.0075762.
66.    Voet A, Zhang K. Pharmacophore modelling as a virtual screening tool for the discovery of small molecule protein-protein interaction inhibitors. Curr Pharm Des. 2012;18(30):4586–4598.  doi: 10.2174/138161212802651616
67.    Voet A, Banwell E, Sahu K, Heddle J, Zhang K. Protein interface pharmacophore mapping tools for small molecule protein: protein interaction inhibitor discovery. Curr Top Med Chem. 2013;13(9):989–1001.  doi: 10.2174/1568026611313090003.
68.    Reddy T, Li C, Fischer P, Dekker L. Three-dimensional pharmacophore design and biochemical screening identifies substituted 1,2,4-triazoles as inhibitors of the annexin A2-S100A10 protein interaction. ChemMedChem. 2012;7(8):1435–1446.  doi: 10.1002/cmdc.201200107.
69.    Voet A, Callewaert L, Ulens T, et al. Structure based discovery of small molecule suppressors targeting bacterial lysozyme inhibitors. Biochem Biophys Res Commun. 2011;405(4):527–532. doi: 10.1016/j.bbrc.2011.01.053.
70.    Mustata G, Li M, Zevola N, et al. Development of small-molecule PUMA inhibitors for mitigating radiation-induced cell death. Curr Top Med Chem. 2011;11(3):281–290. doi: 10.2174/156802611794072641.
71.    Voet A, Akihiro I, Hirohama M, et al. Discovery of small molecule inhibitors targeting the SUMO–SIM interaction using a protein interface consensus approach. Med Chem Commun. 2014;5: 783–786. doi.org/10.1039/C3MD00391D.
72.    Corradi V, Mancini M, Manetti F, Petta S, Santucci M, Botta M. Identification of the first non-peptidic small molecule inhibitor of the c-Abl/14-3-3 protein-protein interactions able to drive sensitive and Imatinib-resistant leukemia cells to apoptosis. Bioorg Med Chem Lett. 2010;20(20):6133–6137. doi: 10.1016/j.bmcl.2010.08.019.
73.    Baker D. Centenary Award and Sir Frederick Gowland Hopkins Memorial Lecture. Protein folding, structure prediction and design. Biochem Soc Trans. 2014;42(2):225–229. doi: 10.1042/bst20130055
74.    Tinberg C, Khare S, Dou J, et al. Computational design of ligand-binding proteins with high affinity and selectivity. Nature. 2013;501(7466):212–216. doi: 10.1038/nature12443.
75.    Nivón L, Moretti R, Baker D. A Pareto-optimal refinement method for protein design scaffolds. PLoS One. 2013;8:e59004. https://doi.org/10.1371/journal.pone.0059004.


Recomonded Articles:

Research Journal of Pharmacy and Technology (RJPT) is an international, peer-reviewed, multidisciplinary journal.... Read more >>>

RNI: CHHENG00387/33/1/2008-TC                     
DOI: 10.5958/0974-360X 

1.3
2021CiteScore
 
56th percentile
Powered by  Scopus


SCImago Journal & Country Rank


Recent Articles




Tags


Not Available