Pharmacophore & QSAR Guided Design, Synthesis, Pharmacokinetics and In vitro Evaluation of Curcumin Analogs for Anticancer Activity


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Abstract

Background:As a part of our discovery of plant-based lead molecules, we provide a helpful tool, which helps in identification, designing, optimising, structural modifications, and prediction of curcumin, to discover novel analogs with enhanced bioavailability, pharmacologically safe, and anticancer potential.

Methods:QSAR (Quantitative structure-activity relationship) and pharmacophore mapping models were developed and further used to design, synthesize, pharmacokinetics, and in vitro evaluation of curcumin analogs for anticancer activity.

Results:The QSAR model yielded a high activity-descriptors relationship accuracy (r2) of 84%, a high activity prediction accuracy (rcv2) of 81%, and external set prediction accuracy of 89%. The QSAR study indicates that the five chemical descriptors were significantly correlated with anticancer activity. The important pharmacophore features identified were a hydrogen bond acceptor, a hydrophobic centre, and a negative ionizable centre. The model's predictive ability was evaluated against a set of chemically synthesized curcumin analogs. Among the tested compounds, nine curcumin analogs were found with IC50 values of 0.10 to 1.86 µg/mL. The active analogs were assessed for pharmacokinetics compliance. EGFR was identified as a potential target of synthesized active curcumin analogs through docking studies.

Conclusion:Integrating in silico design, QSAR-driven virtual screening, chemical synthesis, and experimental in vitro evaluation may lead to the early discovery of novel and promising anticancer compounds from natural sources. The developed QSAR model and common pharmacophore generation were used as a designing and predictive tool to develop novel curcumin analogs. This study may help optimize the therapeutic relationships of studied compounds for further drug development and their potential safety concerns. This study may guide compound selection and designing novel active chemical scaffolds or new combinatorial libraries of the curcumin series.

About the authors

Sarfaraz Alam

Computational Biology Unit, CSIR-Central Institute of Medicinal & Aromatic Plants

Email: info@benthamscience.net

Surjeet Verma

Medicinal Chemistry Division,, CSIR-Central Institute of Medicinal & Aromatic Plants

Email: info@benthamscience.net

Kaneez Fatima

Molecular Bioprospection Department, CSIR-Central Institute of Medicinal & Aromatic Plants

Email: info@benthamscience.net

Suaib Luqman

Molecular Bioprospection Department, CSIR-Central Institute of Medicinal & Aromatic Plants

Email: info@benthamscience.net

Santosh Srivastava

Medicinal Chemistry Division, CSIR-Central Institute of Medicinal & Aromatic Plants

Email: info@benthamscience.net

Feroz Khan

Computational Biology Unit, CSIR-Central Institute of Medicinal & Aromatic Plants

Author for correspondence.
Email: info@benthamscience.net

References

  1. Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin., 2021, 71(3), 209-249. doi: 10.3322/caac.21660 PMID: 33538338
  2. Dikshit, R.; Gupta, P.C.; Ramasundarahettige, C.; Gajalakshmi, V.; Aleksandrowicz, L.; Badwe, R.; Kumar, R.; Roy, S.; Suraweera, W.; Bray, F.; Mallath, M.; Singh, P.K.; Sinha, D.N.; Shet, A.S.; Gelband, H.; Jha, P. Cancer mortality in India: A nationally representative survey. Lancet, 2012, 379(9828), 1807-1816. doi: 10.1016/S0140-6736(12)60358-4 PMID: 22460346
  3. Kaur, R.; Kapoor, K.; Kaur, H. Plants as a source of anticancer agents. J. Nat. Prod. Plant Resour., 2011, 1, 119-124.
  4. Chattopadhyay, I.; Biswas, K.; Bandyopadhyay, U.; Banerjee, R.K. Turmeric and curcumin: Biological actions and medicinal applications. Curr. Sci., 2004, 87, 44-53.
  5. Ammon, H.; Wahl, M. Pharmacology of Curcuma longa. Planta Med., 1991, 57(1), 1-7. doi: 10.1055/s-2006-960004 PMID: 2062949
  6. Kunnumakkara, A.B.; Anand, P.; Aggarwal, B.B. Curcumin inhibits proliferation, invasion, angiogenesis and metastasis of different cancers through interaction with multiple cell signaling proteins. Cancer Lett., 2008, 269(2), 199-225. doi: 10.1016/j.canlet.2008.03.009 PMID: 18479807
  7. Kuttan, R.; Bhanumathy, P.; Nirmala, K.; George, M.C. Potential anticancer activity of turmeric (Curcuma longa). Cancer Lett., 1985, 29(2), 197-202. doi: 10.1016/0304-3835(85)90159-4 PMID: 4075289
  8. Aggarwal, B.B.; Sundaram, C.; Malani, N.; Ichikawa, H. Curcumin: The Indian solid gold. Adv. Exp. Med. Biol., 2007, 595, 1-75. doi: 10.1007/978-0-387-46401-5_1 PMID: 17569205
  9. Kiso, Y.; Suzuki, Y.; Watanabe, N.; Oshima, Y.; Hikino, H. Antihepatotoxic principles of Curcuma longa rhizomes. Planta Med., 1983, 49(11), 185-187. doi: 10.1055/s-2007-969845 PMID: 6657788
  10. Venkatesan, N.; Punithavathi, D.; Arumugam, V. Curcumin prevents adriamycin nephrotoxicity in rats. Br. J. Pharmacol., 2000, 129(2), 231-234. doi: 10.1038/sj.bjp.0703067 PMID: 10694226
  11. Srivastava, R.; Dikshit, M.; Srimal, R.C.; Dhawan, B.N. Anti-thrombotic effect of curcumin. Thromb. Res., 1985, 40(3), 413-417. doi: 10.1016/0049-3848(85)90276-2 PMID: 4082116
  12. Nirmala, C.; Puvanakrishnan, R. Protective role of curcumin against isoproterenol induced myocardial infarction in rats. Mol. Cell. Biochem., 1996, 159(2), 85-93. doi: 10.1007/BF00420910 PMID: 8858558
  13. Mahady, G.B.; Pendland, S.L.; Yun, G.; Lu, Z.Z. Turmeric (Curcuma longa) and curcumin inhibit the growth of Helicobacter pylori, a group 1 carcinogen. Anticancer Res., 2002, 22(6C), 4179-4181. PMID: 12553052
  14. Ouassaf, M.; Daoui, O.; Alam, S.; Elkhattabi, S.; Belaidi, S.; Chtita, S. Pharmacophore-based virtual screening, molecular docking, and molecular dynamics studies for the discovery of novel FLT3 inhibitors. J. Biomol. Struct. Dyn., 2022, 1-13. doi: 10.1080/07391102.2022.2123403 PMID: 36106982
  15. Alam, S.; Khan, F. 3D-QSAR, Docking, ADME/Tox studies on Flavone analogs reveal anticancer activity through Tankyrase inhibition. Sci. Rep., 2019, 9(1), 5414. doi: 10.1038/s41598-019-41984-7 PMID: 30932078
  16. Jorgensen, W.L. The many roles of computation in drug discovery. Science, 2004, 303(5665), 1813-1818. doi: 10.1126/science.1096361 PMID: 15031495
  17. Kalyaanamoorthy, S.; Chen, Y.P.P. Structure-based drug design to augment hit discovery. Drug Discov. Today, 2011, 16(17-18), 831-839. doi: 10.1016/j.drudis.2011.07.006 PMID: 21810482
  18. Mendez, D.; Gaulton, A.; Bento, A.P.; Chambers, J.; De Veij, M.; Félix, E.; Magariños, M.P.; Mosquera, J.F.; Mutowo, P.; Nowotka, M.; Gordillo-Marañón, M.; Hunter, F.; Junco, L.; Mugumbate, G.; Rodriguez-Lopez, M.; Atkinson, F.; Bosc, N.; Radoux, C.J.; Segura-Cabrera, A.; Hersey, A.; Leach, A.R. ChEMBL: Towards direct deposition of bioassay data. Nucleic Acids Res., 2019, 47(D1), D930-D940. doi: 10.1093/nar/gky1075 PMID: 30398643
  19. Sahu, N.K.; Bari, S.B.; Kohli, D.V. Molecular modeling studies of some substituted chalcone derivatives as cysteine protease inhibitors. Med. Chem. Res., 2012, 21(11), 3835-3847. doi: 10.1007/s00044-011-9900-1
  20. Alam, S.; Nasreen, S.; Ahmad, A.; Darokar, M.P.; Khan, F. Detection of natural inhibitors against human liver cancer cell lines through QSAR, molecular docking and ADMET studies. Curr. Top. Med. Chem., 2021, 21(8), 686-695. doi: 10.2174/18734294MTEyjMDcb1 PMID: 33280598
  21. Gobbi, A.; Lee, M.L. DISE: Directed sphere exclusion. J. Chem. Inf. Comput. Sci., 2003, 43(1), 317-323. doi: 10.1021/ci025554v PMID: 12546567
  22. Hudson, B.D.; Hyde, R.M.; Rahr, E.; Wood, J.; Osman, J. Parameter based methods for compound selection from chemical databases. Quant. Struct.-Act. Relationsh., 1996, 15(4), 285-289. doi: 10.1002/qsar.19960150402
  23. Validation of (Q)SAR Models - OECD. Available from: https://www.oecd.org/chemicalsafety/risk-assessment/validationofqsarmodels.htm (accessed February 3, 2022).
  24. de Haas, E.M.; Eikelboom, T.; Bouwman, T. Internal and external validation of the long-term QSARs for neutral organics to fish from ECOSAR™. SAR QSAR Environ. Res., 2011, 22(5-6), 545-559. doi: 10.1080/1062936X.2011.569949 PMID: 21732893
  25. Alam, S.; Khan, F. Virtual screening, docking, ADMET and system pharmacology studies on Garcinia caged Xanthone derivatives for anticancer activity. Sci. Rep., 2018, 8(1), 5524. doi: 10.1038/s41598-018-23768-7 PMID: 29615704
  26. Alam, S.; Khan, F. QSAR, docking, ADMET, and system pharmacology studies on tormentic acid derivatives for anticancer activity. J. Biomol. Struct. Dyn., 2018, 36(9), 2373-2390. doi: 10.1080/07391102.2017.1355846 PMID: 28705120
  27. Szklarczyk, D.; Santos, A.; von Mering, C.; Jensen, L.J.; Bork, P.; Kuhn, M. STITCH 5: Augmenting protein–chemical interaction networks with tissue and affinity data. Nucleic Acids Res., 2016, 44(D1), D380-D384. doi: 10.1093/nar/gkv1277 PMID: 26590256
  28. Bhukya, B.; Alam, S.; Chaturvedi, V.; Trivedi, P.; Kumar, S.; Khan, F. Brevifoliol and its analogs: A new class of antitubercular agents. Curr. Top. Med. Chem., 2021, 21(9), 767-776. doi: 10.2174/1568026620666200528155236 PMID: 32484109
  29. Feroz Khan, F.; Alam, S. QSAR and docking studies on xanthone derivatives for anticancer activity targeting DNA topoisomerase IIα. Drug Des. Devel. Ther., 2014, 8, 183-195. doi: 10.2147/DDDT.S51577 PMID: 24516330
  30. Iqbal, H.; Verma, A.K.; Yadav, P.; Alam, S.; Shafiq, M.; Mishra, D.; Khan, F.; Hanif, K.; Negi, A.S.; Chanda, D. Antihypertensive effect of a novel angiotensin II receptor blocker fluorophenyl benzimidazole: Contribution of cGMP, voltage-dependent calcium channels, and BKCa channels to vasorelaxant mechanisms. Front. Pharmacol., 2021, 12, 611109. doi: 10.3389/fphar.2021.611109 PMID: 33859561
  31. 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 1PII of original article: S0169-409X(96)00423-1. The article was originally published in advanced drug delivery reviews 23 (1997) 3–25. 1. Adv. Drug Deliv. Rev., 2001, 46(1-3), 3-26. doi: 10.1016/S0169-409X(00)00129-0 PMID: 11259830
  32. Ghose, A.K.; Viswanadhan, V.N.; Wendoloski, J.J. 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(1), 55-68. doi: 10.1021/cc9800071 PMID: 10746014
  33. Veber, D.F.; Johnson, S.R.; Cheng, H.Y.; Smith, B.R.; Ward, K.W.; Kopple, K.D. Molecular properties that influence the oral bioavailability of drug candidates. J. Med. Chem., 2002, 45(12), 2615-2623. doi: 10.1021/jm020017n PMID: 12036371
  34. Egan, W.J.; Merz, K.M., Jr; Baldwin, J.J. Prediction of drug absorption using multivariate statistics. J. Med. Chem., 2000, 43(21), 3867-3877. doi: 10.1021/jm000292e PMID: 11052792
  35. Muegge, I.; Heald, S.L.; Brittelli, D. Simple selection criteria for drug-like chemical matter. J. Med. Chem., 2001, 44(12), 1841-1846. doi: 10.1021/jm015507e PMID: 11384230
  36. Martin, Y.C. A bioavailability score. J. Med. Chem., 2005, 48(9), 3164-3170. doi: 10.1021/jm0492002 PMID: 15857122
  37. Baell, J.B.; Holloway, G.A. New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. J. Med. Chem., 2010, 53(7), 2719-2740. doi: 10.1021/jm901137j PMID: 20131845
  38. Brenk, R.; Schipani, A.; James, D.; Krasowski, A.; Gilbert, I.H.; Frearson, J.; Wyatt, P.G. Lessons learnt from assembling screening libraries for drug discovery for neglected diseases. ChemMedChem, 2008, 3(3), 435-444. doi: 10.1002/cmdc.200700139 PMID: 18064617
  39. Teague, S.J.; Davis, A.M.; Leeson, P.D.; Oprea, T. The design of leadlike combinatorial libraries. Angew. Chem. Int. Ed., 1999, 38(24), 3743-3748. doi: 10.1002/(SICI)1521-3773(19991216)38:243.0.CO;2-U PMID: 10649345
  40. Boda, K.; Seidel, T.; Gasteiger, J. Structure and reaction based evaluation of synthetic accessibility. J. Comput. Aided Mol. Des., 2007, 21(6), 311-325. doi: 10.1007/s10822-006-9099-2 PMID: 17294248
  41. Alam, S.; Khan, F. 3D-QSAR studies on maslinic acid analogs for anticancer activity against breast cancer cell line MCF-7. Sci. Rep., 2017, 7(1), 6019. doi: 10.1038/s41598-017-06131-0 PMID: 28729623
  42. Doyle, A.; Griffiths, J.B. Mammalian cell culture: Essential techniques; Wiley, 1997.
  43. Wang, M.; Zhang, Y.; Wang, T.; Zhang, J.; Zhou, Z.; Sun, Y.; Wang, S.; Shi, Y.; Luan, X.; Zhang, Y.; Wang, Y.; Wang, Y.; Zou, Z.; Kang, L.; Liu, H. The USP7 inhibitor P5091 induces cell death in ovarian cancers with different P53 status. Cell. Physiol. Biochem., 2017, 43(5), 1755-1766. doi: 10.1159/000484062 PMID: 29049989
  44. Kulkarni, P.S.; Kondhare, D.D.; Varala, R.; Zubaidha, P.K. Calcium hydroxide: An efficient and mild base for one-pot synthesis of curcumin and it’s analogues. Acta Chim. Slov., 2013, 6(1), 150-156. doi: 10.2478/acs-2013-0023

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