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Advanced Chemometric Modeling Approaches for the Design of Multitarget Drugs Against Neurodegenerative Diseases

  • Amit Kumar Halder
  • Ana S. Moura
  • M. Natália D. S. CordeiroEmail author
Protocol
Part of the Methods in Pharmacology and Toxicology book series (MIPT)

Abstract

Neurodegenerative diseases (ND), a major worldwide health problem, present a multifactorial nature. This implies that a multitargeted therapy approach can be considered more effective in such cases when comparing with “one drug-one target” based therapies. Multitarget drugs interact simultaneously with two or more therapeutic targets, thus acting synergistically to improve the disease conditions. This chapter discusses the recent advances in chemometric techniques in multitarget anti-ND drug design. After a brief introduction to the most relevant pathophysiological aspects of some common neurodegenerative diseases, it analyses not only pathophysiology versus therapeutic targets but also conventional versus novel chemometric techniques within such context. The emergence of novel and various chemometric techniques undoubtedly contributed to the design of multitarget-directed ligands (MTDLs) over the last decade, laying emphasis on the sound prospective for future therapeutics regarding diseases such as Alzheimer’s and Parkinson’s disease.

Keywords

Chemometrics Multitarget-directed ligands (MTDLs) Multitargeted therapies Neurodegenerative diseases QSAR 

Notes

Acknowledgments

This work is supported by Fundação para a Ciência e a Tecnologia (FCT/MEC) through national funds and co-financed by FEDER, under the partnership agreement PT2020 (Projects UID/QUI/50006/2013 and POCI/01/0145/FEDER/007265). To all financing sources, the authors are greatly indebted.

References

  1. 1.
    Gitler AD, Dhillon P, Shorter J (2017) Neurodegenerative disease: models, mechanisms, and a new hope. Dis Model Mech 10(5):499–502PubMedPubMedCentralGoogle Scholar
  2. 2.
    Yacoubian TA (2017) Chapter 1 – neurodegenerative disorders: why do we need new therapies? A2 – Adejare, Adeboye. In: Drug discovery approaches for the treatment of neurodegenerative disorders. Academic Press, New York, pp 1–16Google Scholar
  3. 3.
    Lardenoije R, Iatrou A, Kenis G, Kompotis K, Steinbusch HW, Mastroeni D, Coleman P, Lemere CA, Hof PR, van den Hove DL, Rutten BP (2015) The epigenetics of aging and neurodegeneration. Prog Neurobiol 131:21–64PubMedGoogle Scholar
  4. 4.
    Coleby R (2017) Medicinal chemistry advances in neurodegenerative disease therapy: part 2. Future Med Chem 9(10):951–952PubMedGoogle Scholar
  5. 5.
    Arevalo-Villalobos JI, Rosales-Mendoza S, Zarazua S (2017) Immunotherapies for neurodegenerative diseases: current status and potential of plant-made biopharmaceuticals. Expert Rev Vaccines 16(2):151–159PubMedGoogle Scholar
  6. 6.
  7. 7.
    Pen AE, Jensen UB (2017) Current status of treating neurodegenerative disease with induced pluripotent stem cells. Acta Neurol Scand 135(1):57–72PubMedGoogle Scholar
  8. 8.
    Khanam H, Ali A, Asif M, Shamsuzzaman (2016) Neurodegenerative diseases linked to misfolded proteins and their therapeutic approaches: a review. Eur J Med Chem 124:1121–1141PubMedGoogle Scholar
  9. 9.
    Cummings J, Aisen PS, DuBois B, Frolich L, Jack CR Jr, Jones RW, Morris JC, Raskin J, Dowsett SA, Scheltens P (2016) Drug development in Alzheimer’s disease: the path to 2025. Alzheimers Res Ther 8:39PubMedPubMedCentralGoogle Scholar
  10. 10.
    Mathis S, Couratier P, Julian A, Vallat JM, Corcia P, Le Masson G (2017) Management and therapeutic perspectives in amyotrophic lateral sclerosis. Expert Rev Neurother 17(3):263–276PubMedGoogle Scholar
  11. 11.
    Anighoro A, Bajorath J, Rastelli G (2014) Polypharmacology: challenges and opportunities in drug discovery. J Med Chem 57(19):7874–7887Google Scholar
  12. 12.
    Cavalli A, Bolognesi ML, Minarini A, Rosini M, Tumiatti V, Recanatini M, Melchiorre C (2008) Multi-target-directed ligands to combat neurodegenerative diseases. J Med Chem 51(3):347–372PubMedPubMedCentralGoogle Scholar
  13. 13.
    Lauria A, Bonsignore R, Bartolotta R, Perricone U, Martorana A, Gentile C (2016) Drugs polypharmacology by in silico methods: new opportunities in drug discovery. Curr Pharm Des 22(21):3073–3081Google Scholar
  14. 14.
    Satyanarayanajois SD (2011) Drug design and discovery: methods and protocols. In: Methods in molecular biology, vol vol 716. Humana Press, New YorkGoogle Scholar
  15. 15.
    Zheng Y (2012) Rational drug design: methods and protocols. In: Method in molecular biology, vol vol 928. Humana Press, Springer, New YorkGoogle Scholar
  16. 16.
    Bajorath J (2015) Pushing the boundaries of computational approaches: special focus issue on computational chemistry and computer-aided drug discovery. Future Med Chem 7(18):2415–2417PubMedGoogle Scholar
  17. 17.
    Bajorath J (2016) Computational chemistry and computer-aided drug discovery: part II. Future Med Chem 8(15):1799–1800PubMedGoogle Scholar
  18. 18.
    Jamal S, Grover A (2017) Cheminformatics approaches in modern drug discovery. In: Grover A (ed) Drug design: principles and applications. Springer Singapore, Singapore, pp 135–148Google Scholar
  19. 19.
    Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin M, Dearden J, Gramatica P, Martin YC, Todeschini R, Consonni V, Kuz’min VE, Cramer R, Benigni R, Yang C, Rathman J, Terfloth L, Gasteiger J, Richard A, Tropsha A (2014) QSAR modeling: where have you been? Where are you going to? J Med Chem 57(12):4977–5010PubMedPubMedCentralGoogle Scholar
  20. 20.
    Lavine BK (2005) Chemometrics and chemoinformatics, ACS symposium series, vol vol 894. American Chemical Society, Washington, DCGoogle Scholar
  21. 21.
    Begam BF, Kumar JS (2012) A study on cheminformatics and its applications on modern drug discovery. Procedia Eng 38(suppl C):1264–1275Google Scholar
  22. 22.
    Dugger BN, Dickson DW (2017) Pathology of neurodegenerative diseases. Cold Spring Harb Perspect Biol 9(7):a028035PubMedGoogle Scholar
  23. 23.
    Labzin LI, Heneka MT, Latz E (2017) Innate immunity and neurodegeneration. Annu Rev Med 69:437–449PubMedGoogle Scholar
  24. 24.
    Chetelat G, Villemagne VL, Pike KE, Ellis KA, Bourgeat P, Jones G, O'Keefe GJ, Salvado O, Szoeke C, Martins RN, Ames D, Masters CL, Rowe CC, Australian Imaging B, Lifestyle Study of Ageing Research G (2011) Independent contribution of temporal beta-amyloid deposition to memory decline in the pre-dementia phase of Alzheimer’s disease. Brain 134(Pt 3):798–807PubMedGoogle Scholar
  25. 25.
    Possin KL (2010) Visual spatial cognition in neurodegenerative disease. Neurocase 16(6):466–487PubMedPubMedCentralGoogle Scholar
  26. 26.
    Ross CA, Poirier MA (2004) Protein aggregation and neurodegenerative disease. Nat Med 10:S10PubMedGoogle Scholar
  27. 27.
    Jacobsen KT, Iverfeldt K (2009) Amyloid precursor protein and its homologues: a family of proteolysis-dependent receptors. Cell Mol Life Sci 66(14):2299–2318PubMedGoogle Scholar
  28. 28.
    Larner AJ (2013) Presenilin-1 mutations in Alzheimer’s disease: an update on genotype-phenotype relationships. J Alzheimers Dis 37(4):653–659PubMedGoogle Scholar
  29. 29.
    Adeniji AO, Adams PW, Mody VV (2017) Chapter 7 – amyloid β hypothesis in the development of therapeutic agents for Alzheimer’s disease A2 – Adejare, Adeboye. In: Drug discovery approaches for the treatment of neurodegenerative disorders. Academic Press, New York, pp 109–143Google Scholar
  30. 30.
    Kalia LV, Lang AE (2015) Parkinson’s disease. Lancet 386(9996):896–912PubMedGoogle Scholar
  31. 31.
    Ferreira M, Massano J (2017) An updated review of Parkinson’s disease genetics and clinicopathological correlations. Acta Neurol Scand 135(3):273–284PubMedGoogle Scholar
  32. 32.
    Spillantini MG, Schmidt ML, Lee VM, Trojanowski JQ, Jakes R, Goedert M (1997) Alpha-synuclein in Lewy bodies. Nature 388(6645):839–840PubMedGoogle Scholar
  33. 33.
    Dawson TM, Dawson VL (2003) Rare genetic mutations shed light on the pathogenesis of Parkinson disease. J Clin Invest 111(2):145–151PubMedPubMedCentralGoogle Scholar
  34. 34.
    Roos RA (2010) Huntington’s disease: a clinical review. Orphanet J Rare Dis 5:40PubMedPubMedCentralGoogle Scholar
  35. 35.
    Ross CA, Tabrizi SJ (2011) Huntington’s disease: from molecular pathogenesis to clinical treatment. Lancet Neurol 10(1):83–98PubMedGoogle Scholar
  36. 36.
    Moncke-Buchner E, Reich S, Mucke M, Reuter M, Messer W, Wanker EE, Kruger DH (2002) Counting CAG repeats in the Huntington’s disease gene by restriction endonuclease EcoP15I cleavage. Nucleic Acids Res 30(16):e83PubMedPubMedCentralGoogle Scholar
  37. 37.
    Witgert M, Salamone AR, Strutt AM, Jawaid A, Massman PJ, Bradshaw M, Mosnik D, Appel SH, Schulz PE (2010) Frontal-lobe mediated behavioral dysfunction in amyotrophic lateral sclerosis. Eur J Neurol 17(1):103–110PubMedGoogle Scholar
  38. 38.
    Zarei S, Carr K, Reiley L, Diaz K, Guerra O, Altamirano PF, Pagani W, Lodin D, Orozco G, Chinea A (2015) A comprehensive review of amyotrophic lateral sclerosis. Surg Neurol Int 6:171PubMedPubMedCentralGoogle Scholar
  39. 39.
    Kovacs GG, Budka H (2008) Prion diseases: from protein to cell pathology. Am J Pathol 172(3):555–565PubMedPubMedCentralGoogle Scholar
  40. 40.
    Piccardo P, Manson JC, King D, Ghetti B, Barron RM (2007) Accumulation of prion protein in the brain that is not associated with transmissible disease. Proc Natl Acad Sci U S A 104(11):4712–4717PubMedPubMedCentralGoogle Scholar
  41. 41.
    Stayte S, Vissel B (2014) Advances in non-dopaminergic treatments for Parkinson’s disease. Front Neurosci 8:113PubMedPubMedCentralGoogle Scholar
  42. 42.
    Casey DA, Antimisiaris D, O’Brien J (2010) Drugs for Alzheimer’s disease: are they effective? P T 35(4):208–211PubMedPubMedCentralGoogle Scholar
  43. 43.
    Yiannopoulou KG, Papageorgiou SG (2013) Current and future treatments for Alzheimer’s disease. Ther Adv Neurol Disord 6(1):19–33PubMedPubMedCentralGoogle Scholar
  44. 44.
    Cummings J, Lee G, Mortsdorf T, Ritter A, Zhong K (2017) Alzheimer’s disease drug development pipeline: 2017. Alzheimers Dement (N Y) 3(3):367–384Google Scholar
  45. 45.
    Miller RG, Jackson CE, Kasarskis EJ, England JD, Forshew D, Johnston W, Kalra S, Katz JS, Mitsumoto H, Rosenfeld J, Shoesmith C, Strong MJ, Woolley SC, Quality Standards Subcommittee of the American Academy of N (2009) Practice parameter update: the care of the patient with amyotrophic lateral sclerosis: drug, nutritional, and respiratory therapies (an evidence-based review): report of the quality standards Subcommittee of the American Academy of Neurology. Neurology 73(15):1218–1226PubMedPubMedCentralGoogle Scholar
  46. 46.
    Paleacu D (2007) Tetrabenazine in the treatment of Huntington’s disease. Neuropsychiatr Dis Treat 3(5):545–551PubMedPubMedCentralGoogle Scholar
  47. 47.
    Mencher SK, Wang LG (2005) Promiscuous drugs compared to selective drugs (promiscuity can be a virtue). BMC Clin Pharmacol 5:3PubMedPubMedCentralGoogle Scholar
  48. 48.
    Lu JJ, Pan W, Hu YJ, Wang YT (2012) Multi-target drugs: the trend of drug research and development. PLoS One 7(6):e40262PubMedPubMedCentralGoogle Scholar
  49. 49.
    Iyengar R (2013) Complex diseases require complex therapies. EMBO Rep 14(12):1039–1042PubMedPubMedCentralGoogle Scholar
  50. 50.
    Morphy R, Rankovic Z (2005) Designed multiple ligands. An emerging drug discovery paradigm. J Med Chem 48(21):6523–6543PubMedPubMedCentralGoogle Scholar
  51. 51.
    Bolognesi ML (2013) Polypharmacology in a single drug: multitarget drugs. Curr Med Chem 20(13):1639–1645PubMedGoogle Scholar
  52. 52.
    Keith CT, Borisy AA, Stockwell BR (2005) Multicomponent therapeutics for networked systems. Nat Rev Drug Discov 4(1):71–78Google Scholar
  53. 53.
    Smid P, Coolen HK, Keizer HG, van Hes R, de Moes JP, den Hartog AP, Stork B, Plekkenpol RH, Niemann LC, Stroomer CN, Tulp MT, van Stuivenberg HH, McCreary AC, Hesselink MB, Herremans AH, Kruse CG (2005) Synthesis, structure-activity relationships, and biological properties of 1-heteroaryl-4-[omega-(1H-indol-3-yl)alkyl]piperazines, novel potential antipsychotics combining potent dopamine D2 receptor antagonism with potent serotonin reuptake inhibition. J Med Chem 48(22):6855–6869PubMedGoogle Scholar
  54. 54.
    Tsuji S (2010) Genetics of neurodegenerative diseases: insights from high-throughput resequencing. Hum Mol Genet 19(R1):R65–R70PubMedPubMedCentralGoogle Scholar
  55. 55.
    Van der Schyf CJ (2011) The use of multi-target drugs in the treatment of neurodegenerative diseases. Expert Rev Clin Pharmacol 4(3):293–298PubMedGoogle Scholar
  56. 56.
    Weinreb O, Amit T, Bar-Am O, Yogev-Falach M, Youdim MB (2008) The neuroprotective mechanism of action of the multimodal drug ladostigil. Front Biosci 13:5131–5137PubMedGoogle Scholar
  57. 57.
    Prati F, Uliassi E, Bolognesi ML (2014) Two diseases, one approach: multitarget drug discovery in Alzheimer’s and neglected tropical diseases. MedChemComm 5(7):853–861Google Scholar
  58. 58.
    Beebe KR, Pell RJ, Seasholtz MB (1998) Chemometrics: A practical guide. John Wiley & Sons, Inc., New YorkGoogle Scholar
  59. 59.
    Gad HA, El-Ahmady SH, Abou-Shoer MI, Al-Azizi MM (2013) Application of chemometrics in authentication of herbal medicines: a review. Phytochem Anal 24(1):1–24Google Scholar
  60. 60.
    Huang H-J, Yu HW, Chen C-Y, Hsu C-H, Chen H-Y, Lee K-J, Tsai F-J, Chen CY-C (2010) Current developments of computer-aided drug design. J Taiwan Inst Chem Eng 41(6):623–635Google Scholar
  61. 61.
    Christofferson AJ, Huang N (2012) How to benchmark methods for structure-based virtual screening of large compound libraries. Methods Mol Biol 819:187–195PubMedGoogle Scholar
  62. 62.
    Halder AK, Saha A, Jha T (2013) The role of 3D pharmacophore mapping based virtual screening for identification of novel anticancer agents: an overview. Curr Top Med Chem 13(9):1098–1126PubMedGoogle Scholar
  63. 63.
    Lipinski CA (2004) Lead- and drug-like compounds: the rule-of-five revolution. Drug Discov Today Technol 1(4):337–341Google Scholar
  64. 64.
    Hansch C, Maloney PP, Fujita T, Muir RM (1962) Correlation of biological activity of phenoxyacetic acids with Hammett substituent constants and partition coefficients. Nature 194:178Google Scholar
  65. 65.
    Cramer RD (2012) The inevitable QSAR renaissance. J Comput Aided Mol Des 26(1):35–38Google Scholar
  66. 66.
    Todeschini R, Consonni V (2000) Handbook of molecular descriptors, vol 11. Wiley VCH, WeinheimGoogle Scholar
  67. 67.
    Quintero FA, Patel SJ, Muñoz F, Sam Mannan M (2012) Review of existing QSAR/QSPR models developed for properties used in hazardous chemicals classification system. Ind Eng Chem Res 51(49):16101–16115Google Scholar
  68. 68.
    Lewis RA, Wood D (2014) Modern 2D QSAR for drug discovery. Wiley Interdiscip Rev Comput Mol Sci 4(6):505–522Google Scholar
  69. 69.
    Cramer RD, Patterson DE, Bunce JD (1988) Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc 110(18):5959–5967Google Scholar
  70. 70.
    Klebe G, Abraham U (1999) Comparative molecular similarity index analysis (CoMSIA) to study hydrogen-bonding properties and to score combinatorial libraries. J Comput Aided Mol Des 13(1):1–10PubMedGoogle Scholar
  71. 71.
    Robinson DD, Winn PJ, Lyne PD, Richards WG (1999) Self-organizing molecular field analysis: a tool for structure-activity studies. J Med Chem 42(4):573–583PubMedGoogle Scholar
  72. 72.
    Ajmani S, Jadhav K, Kulkarni SA (2006) Three-dimensional QSAR using the k-nearest neighbor method and its interpretation. J Chem Inf Model 46(1):24–31PubMedGoogle Scholar
  73. 73.
    Cramer RD (2003) Topomer CoMFA: a design methodology for rapid lead optimization. J Med Chem 46(3):374–388PubMedGoogle Scholar
  74. 74.
    Tosco P, Balle T (2011) Open3DQSAR: a new open-source software aimed at high-throughput chemometric analysis of molecular interaction fields. J Mol Model 17(1):201–208PubMedGoogle Scholar
  75. 75.
    Hechinger M, Leonhard K, Marquardt W (2012) What is wrong with quantitative structure-property relations models based on three-dimensional descriptors? J Chem Inf Model 52(8):1984–1993PubMedGoogle Scholar
  76. 76.
    Wang T, Wu MB, Lin JP, Yang LR (2015) Quantitative structure-activity relationship: promising advances in drug discovery platforms. Expert Opin Drug Discov 10(12):1283–1300PubMedGoogle Scholar
  77. 77.
    Pastor M, Cruciani G, McLay I, Pickett S, Clementi S (2000) GRid-INdependent descriptors (GRIND): a novel class of alignment-independent three-dimensional molecular descriptors. J Med Chem 43(17):3233–3243PubMedGoogle Scholar
  78. 78.
    Fang J, Li Y, Liu R, Pang X, Li C, Yang R, He Y, Lian W, Liu A-L, Du G-H (2015) Discovery of multitarget-directed ligands against Alzheimer’s disease through systematic prediction of chemical–protein interactions. J Chem Inf Model 55(1):149–164PubMedGoogle Scholar
  79. 79.
    Speck-Planche A, Cordeiro MNDS (2017) Advanced in silico approaches for drug discovery: mining information from multiple biological and chemical data through mtk-QSBER and pt-QSPR strategies. Curr Med Chem 24(16):1687–1704PubMedPubMedCentralGoogle Scholar
  80. 80.
    Speck-Planche A, Cordeiro MNDS (2015) Multitasking models for quantitative structure-biological effect relationships: current status and future perspectives to speed up drug discovery. Expert Opin Drug Discov 10(3):245–256Google Scholar
  81. 81.
    Speck-Planche A, Kleandrova VV, Ruso JM, Cordeiro MNDS (2016) First multitarget chemo-bioinformatic model to enable the discovery of antibacterial peptides against multiple gram-positive pathogens. J Chem Inf Model 56(3):588–598PubMedGoogle Scholar
  82. 82.
    Antanasijevic D, Antanasijevic J, Trisovic N, Uscumlic G, Pocajt V (2017) From classification to regression multitasking QSAR modeling using a novel modular neural network: simultaneous prediction of anticonvulsant activity and neurotoxicity of succinimides. Mol Pharm 14(12):4476–4484PubMedGoogle Scholar
  83. 83.
    Casanola-Martin GM, Le-Thi-Thu H, Perez-Gimenez F, Marrero-Ponce Y, Merino-Sanjuan M, Abad C, Gonzalez-Diaz H (2016) Multi-output model with box-Jenkins operators of quadratic indices for prediction of malaria and cancer inhibitors targeting ubiquitin-proteasome pathway (UPP) proteins. Curr Protein Pept Sci 17(3):220–227PubMedGoogle Scholar
  84. 84.
    Casanola-Martin GM, Le-Thi-Thu H, Perez-Gimenez F, Marrero-Ponce Y, Merino-Sanjuan M, Abad C, Gonzalez-Diaz H (2015) Multi-output model with box-Jenkins operators of linear indices to predict multi-target inhibitors of ubiquitin-proteasome pathway. Mol Divers 19(2):347–356PubMedGoogle Scholar
  85. 85.
    Wathieu H, Issa NT, Byers SW, Dakshanamurthy S (2016) Harnessing polypharmacology with computer-aided drug design and systems biology. Curr Pharm Des 22(21):3097–3108PubMedGoogle Scholar
  86. 86.
    Matter H (1997) Selecting optimally diverse compounds from structure databases: a validation study of two-dimensional and three-dimensional molecular descriptors. J Med Chem 40(8):1219–1229PubMedGoogle Scholar
  87. 87.
    Bajusz D, Rácz A, Héberger K (2015) Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations? J Cheminform 7(1):20PubMedPubMedCentralGoogle Scholar
  88. 88.
    Keiser MJ, Roth BL, Armbruster BN, Ernsberger P, Irwin JJ, Shoichet BK (2007) Relating protein pharmacology by ligand chemistry. Nat Biotechnol 25(2):197–206PubMedGoogle Scholar
  89. 89.
    Huang T, Mi H, Lin CY, Zhao L, Zhong LL, Liu FB, Zhang G, Lu AP, Bian ZX, for MG (2017) MOST: most-similar ligand based approach to target prediction. BMC Bioinformatics 18(1):165PubMedPubMedCentralGoogle Scholar
  90. 90.
    Yang SY (2010) Pharmacophore modeling and applications in drug discovery: challenges and recent advances. Drug Discov Today 15(11–12):444–450Google Scholar
  91. 91.
    Mason JS, Good AC, Martin EJ (2001) 3-D pharmacophores in drug discovery. Curr Pharm Des 7(7):567–597PubMedGoogle Scholar
  92. 92.
    Halder AK, Mallick S, Shikha D, Saha A, Saha KD, Jha T (2015) Design of dual MMP-2/HDAC-8 inhibitors by pharmacophore mapping, molecular docking, synthesis and biological activity. RSC Adv 5(88):72373–72386Google Scholar
  93. 93.
  94. 94.
    Richmond NJ, Abrams CA, Wolohan PR, Abrahamian E, Willett P, Clark RD (2006) GALAHAD: 1. Pharmacophore identification by hypermolecular alignment of ligands in 3D. J Comput Aided Mol Des 20(9):567–587PubMedGoogle Scholar
  95. 95.
    Van Drie JH, Weininger D, Martin YC (1989) ALADDIN: an integrated tool for computer-assisted molecular design and pharmacophore recognition from geometric, steric, and substructure searching of three-dimensional molecular structures. J Comput Aided Mol Des 3(3):225–251PubMedGoogle Scholar
  96. 96.
    Van Drie JH (1996) An inequality for 3D database searching and its use in evaluating the treatment of conformational flexibility. J Comput Aided Mol Des 10(6):623–630PubMedGoogle Scholar
  97. 97.
    Martin YC, Bures MG, Danaher EA, DeLazzer J, Lico I, Pavlik PA (1993) A fast new approach to pharmacophore mapping and its application to dopaminergic and benzodiazepine agonists. J Comput Aided Mol Des 7(1):83–102PubMedGoogle Scholar
  98. 98.
    Salam NK, Nuti R, Sherman W (2009) Novel method for generating structure-based pharmacophores using energetic analysis. J Chem Inf Model 49(10):2356–2368PubMedGoogle Scholar
  99. 99.
    Jones G, Willett P, Glen RC (1995) A genetic algorithm for flexible molecular overlay and pharmacophore elucidation. J Comput Aided Mol Des 9(6):532–549PubMedGoogle Scholar
  100. 100.
    Barnum D, Greene J, Smellie A, Sprague P (1996) Identification of common functional configurations among molecules. J Chem Inf Comput Sci 36(3):563–571PubMedGoogle Scholar
  101. 101.
    Li H, Sutter J, Hoffman R (2000) HypoGen: an automated system for generating 3D predictive pharmacophore models. In: Guner O (ed) Pharmacophore perception, development and use in drug design. International University Line, La Jolla, CAGoogle Scholar
  102. 102.
    Wolber G, Langer T (2005) LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J Chem Inf Model 45(1):160–169PubMedGoogle Scholar
  103. 103.
    Molecular Operating Environment (MOE). Chemical Computing Group. www.chemcomp.com
  104. 104.
    Holliday JD, Willett P (1997) Using a genetic algorithm to identify common structural features in sets of ligands. J Mol Graph Model 15(4):221–232PubMedGoogle Scholar
  105. 105.
    Dixon SL, Smondyrev AM, Knoll EH, Rao SN, Shaw DE, Friesner RA (2006) PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results. J Comput Aided Mol Des 20(10–11):647–671Google Scholar
  106. 106.
    Chen YC (2015) Beware of docking! Trends Pharmacol Sci 36(2):78–95Google Scholar
  107. 107.
    Gabel J, Desaphy J, Rognan D (2014) Beware of machine learning-based scoring functions-on the danger of developing black boxes. J Chem Inf Model 54(10):2807–2815PubMedGoogle Scholar
  108. 108.
    Koebel MR, Schmadeke G, Posner RG, Sirimulla S (2016) AutoDock VinaXB: implementation of XBSF, new empirical halogen bond scoring function, into AutoDock Vina. J Cheminform 8:27PubMedPubMedCentralGoogle Scholar
  109. 109.
    Lang PT, Brozell SR, Mukherjee S, Pettersen EF, Meng EC, Thomas V, Rizzo RC, Case DA, James TL, Kuntz ID (2009) DOCK 6: combining techniques to model RNA-small molecule complexes. RNA 15(6):1219–1230PubMedPubMedCentralGoogle Scholar
  110. 110.
    Rarey M, Kramer B, Lengauer T, Klebe G (1996) A fast flexible docking method using an incremental construction algorithm. J Mol Biol 261(3):470–489PubMedGoogle Scholar
  111. 111.
    McGann M (2012) FRED and HYBRID docking performance on standardized datasets. J Comput Aided Mol Des 26(8):897–906PubMedGoogle Scholar
  112. 112.
    Schrodinger release 2017-1: Glide. Schrodinger, LLC, New York, NY, 2017Google Scholar
  113. 113.
    Verdonk ML, Cole JC, Hartshorn MJ, Murray CW, Taylor RD (2003) Improved protein-ligand docking using GOLD. Proteins 52(4):609–623PubMedGoogle Scholar
  114. 114.
    Venkatachalam CM, Jiang X, Oldfield T, Waldman M (2003) LigandFit: a novel method for the shape-directed rapid docking of ligands to protein active sites. J Mol Graph Model 21(4):289–307PubMedGoogle Scholar
  115. 115.
    Jain AN (2007) Surflex-Dock 2.1: robust performance from ligand energetic modeling, ring flexibility, and knowledge-based search. J Comput Aided Mol Des 21(5):281–306PubMedGoogle Scholar
  116. 116.
    VLifeMDS: molecular design suite. VLife Sciences Technologies Pvt. Ltd., Pune, India, 2010Google Scholar
  117. 117.
  118. 118.
    Ngan CH, Bohnuud T, Mottarella SE, Beglov D, Villar EA, Hall DR, Kozakov D, Vajda S (2012) FTMAP: extended protein mapping with user-selected probe molecules. Nucleic Acids Res 40(web server issue):W271–W275Google Scholar
  119. 119.
    Lionta E, Spyrou G, Vassilatis DK, Cournia Z (2014) Structure-based virtual screening for drug discovery: principles, applications and recent advances. Curr Top Med Chem 14(16):1923–1938PubMedPubMedCentralGoogle Scholar
  120. 120.
    Le Guilloux V, Schmidtke P, Tuffery P (2009) Fpocket: an open source platform for ligand pocket detection. BMC Bioinformatics 10:168PubMedPubMedCentralGoogle Scholar
  121. 121.
    Amaro RE, Li WW (2010) Emerging methods for ensemble-based virtual screening. Curr Top Med Chem 10(1):3–13PubMedPubMedCentralGoogle Scholar
  122. 122.
    Csermely P, Agoston V, Pongor S (2005) The efficiency of multi-target drugs: the network approach might help drug design. Trends Pharmacol Sci 26(4):178–182PubMedGoogle Scholar
  123. 123.
    Durrant JD, McCammon JA (2011) Molecular dynamics simulations and drug discovery. BMC Biol 9:71PubMedPubMedCentralGoogle Scholar
  124. 124.
  125. 125.
    Wang JC, Chu PY, Chen CM, Lin JH (2012) idTarget: a web server for identifying protein targets of small chemical molecules with robust scoring functions and a divide-and-conquer docking approach. Nucleic Acids Res 40(web server issue):W393–W399Google Scholar
  126. 126.
    Chen YZ, Ung CY (2002) Computer automated prediction of potential therapeutic and toxicity protein targets of bioactive compounds from Chinese medicinal plants. Am J Chin Med 30(1):139–154PubMedGoogle Scholar
  127. 127.
    Chaudhari R, Tan Z, Huang B, Zhang S (2017) Computational polypharmacology: a new paradigm for drug discovery. Expert Opin Drug Discov 12(3):279–291Google Scholar
  128. 128.
    Chartier M, Adriansen E, Najmanovich R (2016) IsoMIF finder: online detection of binding site molecular interaction field similarities. Bioinformatics 32(4):621–623PubMedGoogle Scholar
  129. 129.
    Awale M, Reymond JL (2017) The polypharmacology browser: a web-based multi-fingerprint target prediction tool using ChEMBL bioactivity data. J Cheminform 9:11PubMedPubMedCentralGoogle Scholar
  130. 130.
  131. 131.
  132. 132.
  133. 133.
  134. 134.
  135. 135.
  136. 136.
  137. 137.
  138. 138.
  139. 139.
  140. 140.
  141. 141.
  142. 142.
  143. 143.
  144. 144.
    Bitam S, Hamadache M, Hanini S (2017) QSAR model for prediction of the therapeutic potency of N-benzylpiperidine derivatives as AChE inhibitors. SAR QSAR Environ Res 28(6):471–489PubMedGoogle Scholar
  145. 145.
    Wong KY, Mercader AG, Saavedra LM, Honarparvar B, Romanelli GP, Duchowicz PR (2014) QSAR analysis on tacrine-related acetylcholinesterase inhibitors. J Biomed Sci 21:84PubMedPubMedCentralGoogle Scholar
  146. 146.
    Vats C, Dhanjal JK, Goyal S, Bharadvaja N, Grover A (2015) Computational design of novel flavonoid analogues as potential AChE inhibitors: analysis using group-based QSAR, molecular docking and molecular dynamics simulations. Struct Chem 26(2):467–476Google Scholar
  147. 147.
    Mahmoodabadi N, Ajloo D (2016) QSAR, docking, and molecular dynamic studies on the polyphenolic as inhibitors of β-amyloid aggregation. Med Chem Res 25(10):2104–2118Google Scholar
  148. 148.
    Ambure P, Roy K (2016) Understanding the structural requirements of cyclic sulfone hydroxyethylamines as hBACE1 inhibitors against A[small beta] plaques in Alzheimer’s disease: a predictive QSAR approach. RSC Adv 6(34):28171–28186Google Scholar
  149. 149.
    Toropova MA, Toropov AA, Raska I Jr, Raskova M (2015) Searching therapeutic agents for treatment of Alzheimer disease using the Monte Carlo method. Comput Biol Med 64:148–154PubMedGoogle Scholar
  150. 150.
    Niu B, Zhao M, Su Q, Zhang M, Lv W, Chen Q, Chen F, Chu D, Du D, Zhang Y (2017) 2D-SAR and 3D-QSAR analyses for acetylcholinesterase inhibitors. Mol Divers 21(2):413–426PubMedGoogle Scholar
  151. 151.
    Ambure P, Roy K (2014) Exploring structural requirements of leads for improving activity and selectivity against CDK5/p25 in Alzheimer’s disease: an in silico approach. RSC Adv 4(13):6702–6709Google Scholar
  152. 152.
    Jain P, Jadhav HR (2013) Quantitative structure activity relationship analysis of aminoimidazoles as BACE-I inhibitors. Med Chem Res 22(4):1740–1746Google Scholar
  153. 153.
    Cai C, Wu Q, Luo Y, Ma H, Shen J, Zhang Y, Yang L, Chen Y, Wen Z, Wang Q (2017) In silico prediction of ROCK II inhibitors by different classification approaches. Mol Divers 21(4):791–807PubMedGoogle Scholar
  154. 154.
    Gharaghani S, Khayamian T, Ebrahimi M (2013) Molecular dynamics simulation study and molecular docking descriptors in structure-based QSAR on acetylcholinesterase (AChE) inhibitors. SAR QSAR Environ Res 24(9):773–794PubMedGoogle Scholar
  155. 155.
    Helguera AM, Perez-Garrido A, Gaspar A, Reis J, Cagide F, Vina D, Cordeiro MNDS, Borges F (2013) Combining QSAR classification models for predictive modeling of human monoamine oxidase inhibitors. Eur J Med Chem 59:75–90PubMedGoogle Scholar
  156. 156.
    Lu P, Wei X, Zhang R, Yuan Y, Gong Z (2011) Prediction of the binding affinities of adenosine A2A receptor antagonists based on the heuristic method and support vector machine. Med Chem Res 20(8):1220–1228Google Scholar
  157. 157.
    Karolidis DA, Agatonovic-Kustrin S, Morton DW (2010) Artificial neural network (ANN) based modelling for D1 like and D2 like dopamine receptor affinity and selectivity. Med Chem 6(5):259–270PubMedGoogle Scholar
  158. 158.
    Speck-Planche A, Kleandrova VV (2012) QSAR and molecular docking techniques for the discovery of potent monoamine oxidase B inhibitors: computer-aided generation of new rasagiline bioisosteres. Curr Top Med Chem 12(16):1734–1747PubMedGoogle Scholar
  159. 159.
    Kahn I, Lomaka A, Karelson M (2014) Topological fingerprints as an aid in finding structural patterns for LRRK2 inhibition. Mol Inform 33(4):269–275PubMedGoogle Scholar
  160. 160.
    Zambre VP, Hambarde VA, Petkar NN, Patel CN, Sawant SD (2015) Structural investigations by in silico modeling for designing NR2B subunit selective NMDA receptor antagonists. RSC Adv 5(30):23922–23940Google Scholar
  161. 161.
    Amin SA, Adhikari N, Jha T, Gayen S (2016) First molecular modeling report on novel arylpyrimidine kynurenine monooxygenase inhibitors through multi-QSAR analysis against Huntington’s disease: a proposal to chemists! Bioorg Med Chem Lett 26(23):5712–5718PubMedGoogle Scholar
  162. 162.
    Joshi K, Goyal S, Grover S, Jamal S, Singh A, Dhar P, Grover A (2016) Novel group-based QSAR and combinatorial design of CK-1delta inhibitors as neuroprotective agents. BMC Bioinformatics 17(suppl 19):515PubMedPubMedCentralGoogle Scholar
  163. 163.
    Amirhamzeh A, Vosoughi M, Shafiee A, Amini M (2013) Synthesis and docking study of diaryl-isothiazole and 1,2,3-thiadiazole derivatives as potential neuroprotective agents. Med Chem Res 22(3):1212–1223Google Scholar
  164. 164.
    Fang J, Pang X, Yan R, Lian W, Li C, Wang Q, Liu A-L, Du G-H (2016) Discovery of neuroprotective compounds by machine learning approaches. RSC Adv 6(12):9857–9871Google Scholar
  165. 165.
    Durant JL, Leland BA, Henry DR, Nourse JG (2002) Reoptimization of MDL keys for use in drug discovery. J Chem Inf Comput Sci 42(6):1273–1280Google Scholar
  166. 166.
    Rogers D, Hahn M (2010) Extended-connectivity fingerprints. J Chem Inf Model 50(5):742–754Google Scholar
  167. 167.
    Besnard J, Ruda GF, Setola V, Abecassis K, Rodriguiz RM, Huang XP, Norval S, Sassano MF, Shin AI, Webster LA, Simeons FR, Stojanovski L, Prat A, Seidah NG, Constam DB, Bickerton GR, Read KD, Wetsel WC, Gilbert IH, Roth BL, Hopkins AL (2012) Automated design of ligands to polypharmacological profiles. Nature 492(7428):215–220PubMedGoogle Scholar
  168. 168.
    Speck-Planche A, Kleandrova VV, Luan F, Cordeiro MNDS (2013) Multi-target inhibitors for proteins associated with Alzheimer: in silico discovery using fragment-based descriptors. Curr Alzheimer Res 10(2):117–124Google Scholar
  169. 169.
    Luan F, Cordeiro MNDS, Alonso N, Garcia-Mera X, Caamano O, Romero-Duran FJ, Yanez M, Gonzalez-Diaz H (2013) 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 21(7):1870–1879PubMedGoogle Scholar
  170. 170.
    Alonso N, Caamano O, Romero-Duran FJ, Luan F, Cordeiro MNDS, Yanez M, Gonzalez-Diaz H, Garcia-Mera X (2013) Model for high-throughput screening of multitarget drugs in chemical neurosciences: synthesis, assay, and theoretic study of rasagiline carbamates. ACS Chem Neurosci 4(10):1393–1403PubMedPubMedCentralGoogle Scholar
  171. 171.
    Romero Duran FJ, Alonso N, Caamano O, Garcia-Mera X, Yanez M, Prado-Prado FJ, Gonzalez-Diaz H (2014) Prediction of multi-target networks of neuroprotective compounds with entropy indices and synthesis, assay, and theoretical study of new asymmetric 1,2-rasagiline carbamates. Int J Mol Sci 15(9):17035–17064PubMedPubMedCentralGoogle Scholar
  172. 172.
    Bautista-Aguilera OM, Esteban G, Bolea I, Nikolic K, Agbaba D, Moraleda I, Iriepa I, Samadi A, Soriano E, Unzeta M, Marco-Contelles J (2014) Design, synthesis, pharmacological evaluation, QSAR analysis, molecular modeling and ADMET of novel donepezil-indolyl hybrids as multipotent cholinesterase/monoamine oxidase inhibitors for the potential treatment of Alzheimer’s disease. Eur J Med Chem 75:82–95PubMedGoogle Scholar
  173. 173.
    Bautista-Aguilera OM, Esteban G, Chioua M, Nikolic K, Agbaba D, Moraleda I, Iriepa I, Soriano E, Samadi A, Unzeta M, Marco-Contelles J (2014) Multipotent cholinesterase/monoamine oxidase inhibitors for the treatment of Alzheimer’s disease: design, synthesis, biochemical evaluation, ADMET, molecular modeling, and QSAR analysis of novel donepezil-pyridyl hybrids. Drug Des Devel Ther 8:1893–1910PubMedPubMedCentralGoogle Scholar
  174. 174.
  175. 175.
    Nikolic K, Agbaba D, Stark H (2015) Pharmacophore modeling, drug design and virtual screening on multi-targeting procognitive agents approaching histaminergic pathways. J Taiwan Inst Chem Eng 46(suppl C):15–29Google Scholar
  176. 176.
    Nikolic K, Mavridis L, Bautista-Aguilera OM, Marco-Contelles J, Stark H, do Carmo Carreiras M, Rossi I, Massarelli P, Agbaba D, Ramsay RR, Mitchell JB (2015) Predicting targets of compounds against neurological diseases using cheminformatic methodology. J Comput Aided Mol Des 29(2):183–198PubMedGoogle Scholar
  177. 177.
    Huang W, Yu H, Sheng R, Li J, Hu Y (2008) Identification of pharmacophore model, synthesis and biological evaluation of N-phenyl-1-arylamide and N-phenylbenzenesulfonamide derivatives as BACE 1 inhibitors. Bioorg Med Chem 16(24):10190–10197PubMedGoogle Scholar
  178. 178.
    Huang W, Lv D, Yu H, Sheng R, Kim SC, Wu P, Luo K, Li J, Hu Y (2010) Dual-target-directed 1,3-diphenylurea derivatives: BACE 1 inhibitor and metal chelator against Alzheimer’s disease. Bioorg Med Chem 18(15):5610–5615PubMedGoogle Scholar
  179. 179.
    Huang W, Tang L, Shi Y, Huang S, Xu L, Sheng R, Wu P, Li J, Zhou N, Hu Y (2011) Searching for the multi-target-directed ligands against Alzheimer’s disease: discovery of quinoxaline-based hybrid compounds with AChE, H(3)R and BACE 1 inhibitory activities. Bioorg Med Chem 19(23):7158–7167PubMedGoogle Scholar
  180. 180.
    Xie Q, Zheng Z, Shao B, Fu W, Xia Z, Li W, Sun J, Zheng W, Zhang W, Sheng W, Zhang Q, Chen H, Wang H, Qiu Z (2017) Pharmacophore-based design and discovery of (−)-meptazinol carbamates as dual modulators of cholinesterase and amyloidogenesis. J Enzyme Inhib Med Chem 32(1):659–671PubMedPubMedCentralGoogle Scholar
  181. 181.
    Bhayye SS, Roy K, Saha A (2016) Pharmacophore generation, atom-based 3D-QSAR, HQSAR and activity cliff analyses of benzothiazine and deazaxanthine derivatives as dual A2A antagonists/MAOB inhibitors. SAR QSAR Environ Res:1–20Google Scholar
  182. 182.
    Heritage TW, Lowis DR (1999) Molecular hologram QSAR. In: Rational drug design, ACS symposium series, vol vol 719. American Chemical Society, Washington, DC, pp 212–225Google Scholar
  183. 183.
    Inestrosa NC, Alvarez A, Calderon F (1996) Acetylcholinesterase is a senile plaque component that promotes assembly of amyloid beta-peptide into Alzheimer’s filaments. Mol Psychiatry 1(5):359–361PubMedGoogle Scholar
  184. 184.
    Bartolini M, Bertucci C, Cavrini V, Andrisano V (2003) beta-Amyloid aggregation induced by human acetylcholinesterase: inhibition studies. Biochem Pharmacol 65(3):407–416PubMedGoogle Scholar
  185. 185.
    Ismaili L, Refouvelet B, Benchekroun M, Brogi S, Brindisi M, Gemma S, Campiani G, Filipic S, Agbaba D, Esteban G, Unzeta M, Nikolic K, Butini S, Marco-Contelles J (2017) Multitarget compounds bearing tacrine- and donepezil-like structural and functional motifs for the potential treatment of Alzheimer’s disease. Prog Neurobiol 151:4–34PubMedGoogle Scholar
  186. 186.
    Xie SS, Lan JS, Wang X, Wang ZM, Jiang N, Li F, Wu JJ, Wang J, Kong LY (2016) Design, synthesis and biological evaluation of novel donepezil-coumarin hybrids as multi-target agents for the treatment of Alzheimer’s disease. Bioorg Med Chem 24(7):1528–1539PubMedGoogle Scholar
  187. 187.
    Li SY, Jiang N, Xie SS, Wang KD, Wang XB, Kong LY (2014) Design, synthesis and evaluation of novel tacrine-rhein hybrids as multifunctional agents for the treatment of Alzheimer’s disease. Org Biomol Chem 12(5):801–814PubMedGoogle Scholar
  188. 188.
    Xie SS, Wang X, Jiang N, Yu W, Wang KD, Lan JS, Li ZR, Kong LY (2015) Multi-target tacrine-coumarin hybrids: cholinesterase and monoamine oxidase B inhibition properties against Alzheimer’s disease. Eur J Med Chem 95:153–165PubMedGoogle Scholar
  189. 189.
    Prati F, De Simone A, Bisignano P, Armirotti A, Summa M, Pizzirani D, Scarpelli R, Perez DI, Andrisano V, Perez-Castillo A, Monti B, Massenzio F, Polito L, Racchi M, Favia AD, Bottegoni G, Martinez A, Bolognesi ML, Cavalli A (2015) Multitarget drug discovery for Alzheimer’s disease: triazinones as BACE-1 and GSK-3beta inhibitors. Angew Chem Int Ed Engl 54(5):1578–1582PubMedGoogle Scholar
  190. 190.
    Carradori S, Ortuso F, Petzer A, Bagetta D, De Monte C, Secci D, De Vita D, Guglielmi P, Zengin G, Aktumsek A, Alcaro S, Petzer JP (2018) Design, synthesis and biochemical evaluation of novel multi-target inhibitors as potential anti-Parkinson agents. Eur J Med Chem 143:1543–1552PubMedGoogle Scholar
  191. 191.
    Marco-Contelles J, Leon R, de los Rios C, Samadi A, Bartolini M, Andrisano V, Huertas O, Barril X, Luque FJ, Rodriguez-Franco MI, Lopez B, Lopez MG, Garcia AG, Carreiras Mdo C, Villarroya M (2009) Tacripyrines, the first tacrine-dihydropyridine hybrids, as multitarget-directed ligands for the treatment of Alzheimer’s disease. J Med Chem 52(9):2724–2732PubMedGoogle Scholar
  192. 192.
    Cui Z, Sheng Z, Yan X, Cao Z, Tang K (2016) In silico insight into potential anti-Alzheimer’s disease mechanisms of icariin. Int J Mol Sci 17(1).  https://doi.org/10.3390/ijms17010113PubMedCentralGoogle Scholar
  193. 193.
  194. 194.
    Liu H, Wang L, Lv M, Pei R, Li P, Pei Z, Wang Y, Su W, Xie XQ (2014) AlzPlatform: an Alzheimer’s disease domain-specific chemogenomics knowledgebase for polypharmacology and target identification research. J Chem Inf Model 54(4):1050–1060PubMedPubMedCentralGoogle Scholar
  195. 195.
  196. 196.
  197. 197.
  198. 198.
  199. 199.
  200. 200.
  201. 201.
  202. 202.
  203. 203.
    Fang J, Wang L, Li Y, Lian W, Pang X, Wang H, Yuan D, Wang Q, Liu AL, Du GH (2017) AlzhCPI: a knowledge base for predicting chemical-protein interactions towards Alzheimer’s disease. PLoS One 12(5):e0178347PubMedPubMedCentralGoogle Scholar
  204. 204.
    Qiu T, Qiu J, Feng J, Wu D, Yang Y, Tang K, Cao Z, Zhu R (2017) The recent progress in proteochemometric modelling: focusing on target descriptors, cross-term descriptors and application scope. Brief Bioinform 18(1):125–136PubMedGoogle Scholar
  205. 205.
    Barneh F, Jafari M, Mirzaie M (2016) Updates on drug-target network; facilitating polypharmacology and data integration by growth of DrugBank database. Brief Bioinform 17(6):1070–1080PubMedGoogle Scholar
  206. 206.
    Chen C, He Y, Wu J, Zhou J (2015) Creation of a free, internet-accessible database: the multiple target ligand database. J Cheminform 7:14PubMedPubMedCentralGoogle Scholar
  207. 207.
    Alqahtani S (2017) In silico ADME-Tox modeling: progress and prospects. Expert Opin Drug Metab Toxicol 13(11):1147–1158PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Amit Kumar Halder
    • 1
  • Ana S. Moura
    • 1
  • M. Natália D. S. Cordeiro
    • 1
    Email author
  1. 1.LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of SciencesUniversity of PortoPortoPortugal

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