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In silico discovery of noteworthy multi-targeted acetylcholinesterase inhibitors for the treatment of Alzheimer’s disease

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Abstract

Alzheimer’s disease (AD) is a multifactorial and fatal neurodegenerative disorder. Memory loss and cognitive decline occur due to death of brain cells. Various important hallmarks of AD have reported like deposition of β-amyloid fibril, β-amyloid oligomers formation, hyperactive phosphorylated tau protein, oxidative stress in cell, low levels of acetylcholine, etc. Treatment of AD based on cholinesterase inhibitors is only symptomatic, its efficacy is limited. A multi-targeted ligand may enable therapeutic efficacy, because of being multifactorial nature of AD. Hence, this research has been focused on developing novel components that preferentially block cholinesterase and simultaneously bind with other targets like β-secretase, Monoamine oxidases, Glycogen synthase kinase, etc., which are directly or indirectly associated with AD to offer more efficient treatment than earlier. To select novel targets and therapeutic ligands, computational approaches have proved to be robust and reliable tools. To expose intermolecular binding mode of the compounds, molecular docking studies and molecular dynamics simulation studies performed and the results indicate their substantial interactions with the active sites of AChE and BCHE and other responsible targets. In silico ADME/T, study performed to estimate several pharmacokinetic parameters and toxicity profile of the selected compounds. Amongst the series, compounds 2-(2,2-dimethylchromen-6-yl)-5,7- dihydroxychromen-4-one (PCID5315395) and 7-(1,3-benzodioxol-5-yl)-5-hydroxy-2,2-dimethylpyrano [3,2-g] chromen-6-one (PCID5983661) are the most encouraging multi-targeted candidates which have the ability to increase memory, acetylcholine as well as other neurotransmitter levels and give the protection of the neurons against the cognitive deficit. In this study, we are proposing two new compounds from PubChem database as AChE as well as BCHE, MAO-A, MAO-B, Beta-secretase, GSK-3 and N-Methyl-D-aspartate (NMDA) inhibitors for further investigation and experimental validation.

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References

  • Ambure P, Roy K (2014) Advances in quantitative structure–activity relationship models of anti-Alzheimer’s agents. Expert Opin Drug Discov 9(6):697–723

    CAS  PubMed  Google Scholar 

  • Ames BN, Gurney EG, Miller JA, Bartsch H (1972) Carcinogens as frameshift mutagens: metabolites and derivatives of 2-acetylaminofluorene and other aromatic amine carcinogens. Proc Natl Acad Sci 69(11):3128–3132

    CAS  PubMed  Google Scholar 

  • Ames BN, Durston WE, Yamasaki E, Lee FD (1973a) Carcinogens are mutagens: a simple test system combining liver homogenates for activation and bacteria for detection. Proc Natl Acad Sci 70(8):2281–2285

    CAS  PubMed  Google Scholar 

  • Ames BN, Lee FD, Durston WE (1973b) An improved bacterial test system for the detection and classification of mutagens and carcinogens. Proc Natl Acad Sci 70(3):782–786

    CAS  PubMed  Google Scholar 

  • Balaraman Y, Limaye AR, Levey AI, Srinivasan S (2006) Glycogen synthase kinase 3β and Alzheimer’s disease: pathophysiological and therapeutic significance. Cell Mol Life Sci CMLS 63(11):1226–1235

    CAS  PubMed  Google Scholar 

  • Bartus RT (2000) On neurodegenerative diseases, models, and treatment strategies: lessons learned and lessons forgotten a generation following the cholinergic hypothesis. Exp Neurol 163(2):495–529

    CAS  PubMed  Google Scholar 

  • Belluti F, Rampa A, Piazzi L, Bisi A, Gobbi S, Bartolini M, Valenti P (2005) Cholinesterase inhibitors: Xanthostigmine derivatives blocking the acetylcholinesterase-induced β-amyloid aggregation. J Med Chem 48(13):4444–4456

    CAS  PubMed  Google Scholar 

  • Cai Z (2014) Monoamine oxidase inhibitors: promising therapeutic agents for Alzheimer’s disease. Mol Med Rep 9(5):1533–1541

    CAS  PubMed  Google Scholar 

  • Couratier PH, Lesort M, Sindou PH, Esclaire F, Yardin C, Hugon J (1996) Modifications of neuronal phosphorylated τ immunoreactivity induced by NMDA toxicity. Mol Chem Neuropathol 27(3):259–273

    CAS  PubMed  Google Scholar 

  • Cummings JL (2004) Treatment of Alzheimer’s disease: current and future therapeutic approaches. Rev Neurol Dis 1(2):60–69

    PubMed  Google Scholar 

  • Cummings JL, Morstorf T, Zhong K (2014) Alzheimer’s disease drug-development pipeline: few candidates, frequent failures. Alzheimer’s Res Ther 6(4):37

    Google Scholar 

  • Daina A, Zoete V (2016) A boiled-egg to predict gastrointestinal absorption and brain penetration of small molecules. ChemMedChem 11(11):1117–1121

    CAS  PubMed  PubMed Central  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  • Dickson CJ, Madej BD, Skjevik ÅA, Betz RM, Teigen K, Gould IR, Walker RC (2014) Lipid14: the amber lipid force field. J Chem Theor Comput 10(2):865–879

    CAS  Google Scholar 

  • Fassbender K, Masters C, Beyreuther K (2001) Alzheimer’s disease: molecular concepts and therapeutic targets. Naturwissenschaften 88(6):261–267

    CAS  PubMed  Google Scholar 

  • Feng M (2002) Assessment of blood-brain barrier penetration: in silico, in vitro and in vivo. Current Drug Metab 3(6):647–657

    CAS  Google Scholar 

  • Foster DJ, Choi DL, Conn PJ, Rook JM (2014) Activation of M1 and M4 muscarinic receptors as potential treatments for Alzheimer’s disease and schizophrenia. Neuropsychiatr Dis Treat 10:183

    CAS  PubMed  PubMed Central  Google Scholar 

  • Gottwald MD, Rozanski RI (1999) Rivastigmine, a brain-region selective acetylcholinesterase inhibitor for treating Alzheimer’s disease: review and current status. Expert Opin Investig Drugs 8(10):1673–1682

    CAS  PubMed  Google Scholar 

  • Guzior N, Wieckowska A, Panek D, Malawska B (2015) Recent development of multifunctional agents as potential drug candidates for the treatment of Alzheimer’s disease. Curr Med Chem 22(3):373–404

    CAS  PubMed  PubMed Central  Google Scholar 

  • Hansch C, Leo A (1979) Substituent constants for correlation analysis in chemistry and biology. Wiley, New York

    Google Scholar 

  • Houghton PJ, Ren Y, Howes MJ (2006) Acetylcholinesterase inhibitors from plants and fungi. Nat Prod Rep 23(2):181–199

    CAS  PubMed  Google Scholar 

  • Inestrosa NC, Alarcón R (1998) Molecular interactions of acetylcholinesterase with senile plaques. J Physiol Paris 92(5–6):341–344

    CAS  PubMed  Google Scholar 

  • Isa MA, Majumdhar RS, Haider S (2018) In silico docking and molecular dynamics simulation of 3-dehydroquinate synthase (DHQS) from Mycobacterium tuberculosis. J Mol Model 24(6):132

    PubMed  Google Scholar 

  • Jakalian A, Jack DB, Bayly CI (2002) Fast, efficient generation of high-quality atomic charges. AM1-BCC model: II. Parameterization and validation. J Comput Chem 23(16):1623–1641

    CAS  PubMed  Google Scholar 

  • Kelder J, Grootenhuis PD, Bayada DM, Delbressine LP, Ploemen JP (1999) Polar molecular surface as a dominating determinant for oral absorption and brain penetration of drugs. Pharm Res 16(10):1514–1519

    CAS  PubMed  Google Scholar 

  • Kennedy BP, Ziegler MG, Alford M, Hansen LA, Thal LJ, Masliah E (2003) Early and persistent alterations in prefrontal cortex MAO A and B in Alzheimer’s disease. J Neural Transm 110(7):789–801

    CAS  PubMed  Google Scholar 

  • Khan MTH, Orhan I, Şenol FS, Kartal M, Şener B, Dvorská M, Šlapetová T (2009) Cholinesterase inhibitory activities of some flavonoid derivatives, chosen xanthone, and their molecular docking studies. Chem Biol Interact 181(3):383–389

    CAS  PubMed  Google Scholar 

  • Krieger E, Vriend G (2015) New ways to boost molecular dynamics simulations. J Comput Chem 36(13):996–1007

    CAS  PubMed  PubMed Central  Google Scholar 

  • Krieger E, Koraimann G, Vriend G (2002) Increasing the precision of comparative models with YASARA NOVA—a self‐parameterizing force field. Proteins Struct Funct Bioinform 47(3):393–402

    CAS  Google Scholar 

  • Krieger E, Nielsen JE, Spronk CA, Vriend G (2006) Fast empirical pKa prediction by Ewald summation. J Mol Gr Modell 25(4):481–486

    CAS  Google Scholar 

  • Kryger G, Silman I, Sussman JL (1999) Structure of acetylcholinesterase complexed with E2020 (Aricept®): implications for the design of new anti-Alzheimer drugs. Structure 7(3):297–307

    CAS  PubMed  Google Scholar 

  • LaFerla FM, Oddo S (2005) Alzheimer’s disease: Aβ, tau and synaptic dysfunction. Trends Mol Med 11(4):170–176

    CAS  PubMed  Google Scholar 

  • Leeson PD, Davis AM (2004) Time-related differences in the physical property profiles of oral drugs. J Med Chem 47(25):6338–6348

    CAS  PubMed  Google Scholar 

  • Lenz, G. R. (1999). Technical problems in getting results. In: Haberman AB, Lenz GR, Vaccaro DE (eds) From data to drugs: strategies for benefiting from the new drug discovery technologies, pp 95-114

  • Maier JA, Martinez C, Kasavajhala K, Wickstrom L, Hauser KE, Simmerling C (2015) ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. J Chem Theory Comput 11(8):3696–3713

    CAS  PubMed  PubMed Central  Google Scholar 

  • Mandlik, V., & Singh, S. (2016). Molecular docking and molecular dynamics simulation study of inositol phosphorylceramide synthase–inhibitor complex in leishmaniasis: Insight into the structure based drug design. F1000Research, 5

  • Martin-Rapun R, De Matteis L, Ambrosone A, Garcia-Embid S, Gutierrez L, M de la Fuente J (2017) Targeted Nanoparticles for the Treatment of Alzheimer’s Disease. Curr Pharmaceut Des 23(13):1927–1952

    CAS  Google Scholar 

  • McCann J, Spingarn NE, Kobori J, Ames BN (1975) Detection of carcinogens as mutagens: bacterial tester strains with R factor plasmids. Proc Natl Acad Sci 72(3):979–983

    CAS  PubMed  Google Scholar 

  • Mitra S, Dash R (2018) Structural dynamics and quantum mechanical aspects of shikonin derivatives as CREBBP bromodomain inhibitors. J Mol Gr Modell 83:42–52

    CAS  Google Scholar 

  • Ottaviani G, Gosling DJ, Patissier C, Rodde S, Zhou L, Faller B (2010) What is modulating solubility in simulated intestinal fluids? Eur J Pharmaceut Sci 41(3–4):452–457

    CAS  Google Scholar 

  • Pajouhesh H, Lenz GR (2005) Medicinal chemical properties of successful central nervous system drugs. NeuroRx 2(4):541–553

    PubMed  PubMed Central  Google Scholar 

  • Pascoini AL, Federico LB, Arêas ALF, Verde BA, Freitas PG, Camps I (2019) In silico development of new acetylcholinesterase inhibitors. J Biomol Struct Dyn 37(4):1007–1021

    CAS  PubMed  Google Scholar 

  • Paul Gleeson M, Hersey A, Hannongbua S (2011) In-silico ADME models: a general assessment of their utility in drug discovery applications. Curr Top Med Chem 11(4):358–381

    PubMed  Google Scholar 

  • Piazzi L, Belluti F, Bisi A, Gobbi S, Rizzo S, Bartolini M, Rampa A (2007) Cholinesterase inhibitors: SAR and enzyme inhibitory activity of 3-[ω-(benzylmethylamino) alkoxy] xanthen-9-ones. Bioorganic Med Chem 15(1):575–585

    CAS  Google Scholar 

  • Rampa A, Bisi A, Valenti P, Recanatini M, Cavalli A, Andrisano V, Giusti P (1998) Acetylcholinesterase inhibitors: synthesis and structure − activity relationships of ω-[N-Methyl-N-(3-alkylcarbamoyloxyphenyl)-methyl] aminoalkoxyheteroaryl derivatives. J Med Chem 41(21):3976–3986

    CAS  PubMed  Google Scholar 

  • Raub JT (2004) Early preclinical evaluation in support of hit identification and lead optimization for brain exposure. In: AAPS workshop on optimization of drug-like properties during lead optimization, Parsippany, NJ, pp 19-22

  • Ritchie TJ, Macdonald SJ, Peace S, Pickett SD, Luscombe CN (2013) Increasing small molecule drug developability in sub-optimal chemical space. MedChemComm 4(4):673–680

    CAS  Google Scholar 

  • Roche D, Brackenridge D, McGuffin L (2015) Proteins and their interacting partners: an introduction to protein–ligand binding site prediction methods. Int J Mol Sci 16(12):29829–29842

    CAS  PubMed  PubMed Central  Google Scholar 

  • Skelton AA, Fenter P, Kubicki JD, Wesolowski DJ, Cummings PT (2011) Simulations of the quartz (1011)/water interface: a comparison of classical force fields, ab initio molecular dynamics, and X-ray reflectivity experiments. J Phys Chem C 115(5):2076–2088

    CAS  Google Scholar 

  • Standridge JB (2004) Pharmacotherapeutic approaches to the prevention of Alzheimer’s disease. Am J Geriatr Pharmacother 2(2):119–132

    CAS  PubMed  Google Scholar 

  • Van de Waterbeemd H, Camenisch G, Folkers G, Chretien JR, Raevsky OA (1998) Estimation of blood-brain barrier crossing of drugs using molecular size and shape, and H-bonding descriptors. J Drug Target 6(2):151–165

    PubMed  Google Scholar 

  • Vassar R (2014) BACE1 inhibitor drugs in clinical trials for Alzheimer’s disease. Alzheimer’s Res Ther 6(9):89

    Google Scholar 

  • Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) Development and testing of a general amber force field. J Comput Chem 25(9):1157–1174

    CAS  PubMed  Google Scholar 

  • Whitehouse PJ (1993) Cholinergic therapy in dementia. Acta Neurol Scandinavica 88(S149):42–45

    Google Scholar 

  • Wimo A, Prince MJ (2010) World Alzheimer report 2010: the global economic impact of dementia. Alzheimer’s Dis Int, London

    Google Scholar 

  • Zheng H, Fridkin M, Youdim M (2014) From single target to multitarget/network therapeutics in Alzheimer’s therapy. Pharmaceuticals 7(2):113–135

    PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors are grateful to the Department of Pharmacy, Jagannath University for providing research facilities and special thanks for providing access facility to Schrodinger software. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka, 1207, Bangladesh

    Sabreena Chowdhury Raka

  2. Computational Biology Research Lab, Department of Pharmacy, Jagannath University, 9-10, Chittaranjan Ave, Dhaka, 1100, Bangladesh

    Sabreena Chowdhury Raka, Rahad Ahamed, Arifur Rahman & AZM Ruhul Momen

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  1. Sabreena Chowdhury Raka
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Correspondence to Arifur Rahman.

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Arifur Rahman has no conflict of interest. Sabreena Chowdhury Raka has no conflict of interest. Rahad Ahamed has no conflict of interest. A. Z. M. Ruhul Momen has no conflict of interest.

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Raka, S.C., Ahamed, R., Rahman, A. et al. In silico discovery of noteworthy multi-targeted acetylcholinesterase inhibitors for the treatment of Alzheimer’s disease. ADV TRADIT MED (ADTM) 20, 351–366 (2020). https://doi.org/10.1007/s13596-019-00407-8

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