Generic placeholder image

Current Drug Discovery Technologies

Editor-in-Chief

ISSN (Print): 1570-1638
ISSN (Online): 1875-6220

Review Article

Methodologies Related to Computational Models in View of Developing Anti-Alzheimer Drugs: An Overview

Author(s): Kirtee Baheti and Mayura Kale*

Volume 16, Issue 1, 2019

Page: [66 - 73] Pages: 8

DOI: 10.2174/1570163815666180417120833

Price: $65

Abstract

Background: Since the last two decades, there has been more focus on the development strategies related to Anti-Alzheimer’s drug research. This may be attributed to the fact that most of the Alzheimer's cases are still mostly unknown except for a few cases, where genetic differences have been identified. With the progress of the disease, the symptoms involve intellectual deterioration, memory impairment, abnormal personality and behavioural patterns, confusion, aggression, mood swings, irritability Current therapies available for this disease give only symptomatic relief and do not focus on manipulations of biololecular processes.

Methods: Nearly all the therapies to treat Alzheimer's disease, target to change the amyloid cascade which is considered to be important in AD pathogenesis. New drug regimens are not able to keep pace with the ever-increasing understanding about dementia at the molecular level. Looking into these aggravated problems, we thought to put forth molecular modeling as a drug discovery approach for developing novel drugs to treat Alzheimer disease. The disease is incurable and it gets worst as it advances and finally causes death. Due to this, the design of drugs to treat this disease has become an utmost priority for research. One of the most important emerging technologies applied for this has been Computer-assisted drug design (CADD). It is a research tool that employs large-scale computing strategies in an attempt to develop a model receptor site which can be used for designing of an anti-Alzheimer drug.

Results: Various models of amyloid-based calcium channels have been computationally optimized. Docking and De novo evolution are used to design the compounds. They are further subjected to absorption, distribution, metabolism, excretion and toxicity (ADMET) studies to finally bring about active compounds that are able to cross BBB. Many novel compounds have been designed which might be promising ones for the treatment of AD.

Conclusion: The present review describes the research carried out on various heterocyclic scaffolds that can serve as lead compounds to design Anti-Alzheimer’s drugs in the future. The molecular modeling methods can thus become a better alternative for the discovery of newer Anti- Alzheimer agents. This methodology is extremely useful to design drugs in minimum time with enhanced activity keeping balanced ethical considerations. Thus, the researchers are opting for this improved process over the conventional methods hoping to achieve a sure shot way out for the sufferings of people affected by Alzheimer besides other diseases.

Keywords: Alzheimer's disease, QSAR, molecular modeling, docking, methodologies, drug research.

Graphical Abstract
[1]
Copani A, Guccione S, Giurato L, et al. The cell cycle molecules behind neurodegeneration in Alzheimers disease: Perspectives for drug development. Curr Med Chem 2013; 15(24): 2420-32.
[2]
Yoo KY, Park SY. Terpenoids as potential anti-alzheimer’s disease therapeutics. Molecules 2012; 17: 3524-38.
[3]
Sandra S, Lorenzini L, Giuliani A, et al. Multi-target action of the novel anti-Alzheimer compound CHF5074: In vivo study of long term treatment in Tg2576 mice. BMC Neurosci 2013; 14: 44.
[4]
Wang YJ, Zhou HD, Zhou XF. Clearance of amyloid-beta in Alzheimer’s disease: progress, problems and perspectives. Drug Discov Today 2006; 11: 19-20.
[5]
Aguero-Torres H, Winblad B. Alzheimer’s disease and vascular dementia. Some points of confluence. Ann N Y Acad Sci 2000; 903: 547-52.
[6]
Woodward MC. Drug treatments in development for Alzheimer’s disease. J Pharm Pract Res 2012; 58(42): 1.
[7]
Keri RS, Quintanova C, Marques SM, Esteves AR, Cardoso SM, Santos MA. Design, synthesis and neuroprotective evaluation of novel tacrinee-benzothiazole hybrids as multi-targeted compounds against Alzheimer’s disease. Bioorg Med Chem 2013; 21(15): 4559-69.
[8]
Abdelwahab SI. In vitro inhibitory effect of Boeserngin A on human acetylcholinestrase: Understanding its potential using in silico ADMET studies. J Appl Pharm Sci 2013; 3(03): 30-5.
[9]
Lushington GH, Guo JX, Hurley MM. Acetylcholinesterase: molecular modeling with the whole toolkit. Curr Top Med Chem 2006; 6: 57-73.
[10]
Solomon KA, Sundararajan S, Abirami V. QSAR studies on N-aryl derivative activity towards Alzheimer’s disease. Molecules 2009; 14: 1448-55.
[11]
Arispe N, Pollard HB, Rojas E. Giant Maltilevel cation channels formed by Alzheimer disease amyloid P-protein [ABP-(1-40)] in bilayer membranes. Proc Acad Sci 1993; 90: 10573-7.
[12]
Arispe N, Rojas E, Pollard HB. Alzheimer disease amyloid P-protein forms calcium channels in bilayer membranes: Blockade by tromethamine and duminum. Proc Natl Acad Sci 1993; 90: 567-71.
[13]
Etchebemgaray R, Ito E, Oka K, Tofel-Grehl B, Gibson GE, Alkon D. Potassium channel dysfunction in fibroblasts identifies patients with Alzheimer disease. Proc Natl Acad Sci 1993; 90: 8209-13.
[14]
Weiner P, Kollman PJ. AMBER: Assisted mode1 building with energy refinement. A general program for modeling molecules and their interactions. J Comput Chem 1981; 2: 287-303.
[15]
Prado-Prado F, Cubiella ME, Xerardo GM. Review of bioinformatics and QSAR studies of β-secretase inhibitors. Curr Bioinform 2011; 6: 3-15.
[16]
Prado FJ, Garcia MX, Gonzalez DH. Multi-target spectral moment QSAR versus ANN for anti- parasitic drugs against different parasite species. Bioorg Med Chem 2010; 18: 2225-31.
[17]
Fernandez M, Caballero J, Tundidor CA. Linear and nonlinear QSAR study of N-hydroxy-2-[(phenylsulfonyl)amino] acetamide derivatives as matrix metalloproteinase inhibitors. Bioorg Med Chem 2006; 14: 4137-50.
[18]
Chen PY, Tsai CT, Ou CY, et al. Computational analysis of novel drugs designed for use as acetylcholinesterase inhibitors and histamine H3 receptor antagonists for Alzheimer’s disease by docking, scoring and de novo evolution. Mol Med Rep 2012; 5: 1043-8.
[19]
Borra NK, Kuna Y. Evolution of toxic properties of anti Alzheimer’s drugs through Lipinski’s rule of five. Int J Pure App Biosci 2013; 1(4): 28-36.
[20]
Garcia I, Fall Y, Gomez G, Humberto GD. QSAR, docking, and CoMFA studies of GSK3 inhibitors. Curr Pharm Des 2010; 16: 2666-75.
[21]
Woodgett JR. Molecular cloning and expression of glycogen synthase kinase-3/factor A. EMBO J 1990; 9(8): 2431-8.
[22]
Stahura FL, Godden JW, Bajorath J. Differential Shannon entropy analysis identifies molecular property descriptors that predict aqueous solubility of synthetic compounds with high accuracy in binary QSAR calculations. J Chem Inf Comput Sci 2002; 42(3): 550-8.
[23]
Stahura FL, Godden JW, Bajorath J. Differential Shannon entropy analysis identifies molecular property descriptors that predict aqueous solubility of synthetic compounds with high accuracy in binary QSAR calculations. J Chem Inf Comput Sci 2000; 40(5): 1245-52.
[24]
Gonzalez DH, Prado PF, Ubeira FM. Predicting antimicrobial drugs and targets with the MARCH-INSIDE approach. Curr Top Med Chem 2008; 8(18): 1676-90.
[25]
Desouza SD, Desouza AM, Desouza CC, et al. Hologram QSAR models of 4-[(Diethylamino)methyl]-phenol inhibitors of acetyl/butyrylcholinesterase enzymes as potential anti-alzheimer agents. Molecules 2012; 17: 9529-39.
[26]
Camps P, Formosa X, Galdeano C, et al. Pyrano [3,2-c]quinoline-6-chlorotacrinee Hybrids as a Novel Family of Acetylcholinesterase- and beta-Amyloid-Directed Anti-Alzheimer Compounds. J Med Chem 2009; 52: 5365-79.
[27]
Marym A, Mahmoud S, Mehdi N, et al. A quantitative structure–activity relationship study on histamine receptor antagonists using the genetic algorithm–multi-parameter linear regression method. J Serb Chem Soc 2012; 77(5): 639-50.
[28]
Shin HL, Josephine WW, Hsuan LL, et al. The discovery of potential acetylcholinesterase inhibitors: A combination of pharmacophore modeling, virtual screening, and molecular docking studies. J Biomed Sci 2011; 18: 8-10.
[29]
Cunha EF, Resende JE, Franca TC, et al. Molecular modeling studies of piperidine derivatives as new acetylcholinesterase inhibitors against neurodegenerative diseases. J Chem 2013; 7.
[30]
Farani AA, Nadri H, Aliabadi A. Synthesis, docking and acetylcholinesterase inhibitory assessment of 2-(2-(4-Benzylpiperazin-1-yl)ethyl)isoindoline-1,3-dione derivatives with potential anti-Alzheimer effects. DARU J Pharm Sci 2013; 21: 47.
[31]
Kuna Y, Sake N, Kutagolla P, Kukkarasapalli P. To design novel lead molecules for the enzyme, AChE associated with Alzheimer’s disease. Int J Pharm Sci Rev Res 2013; 22(2): 296-302.
[32]
Inestrosa NC, Alvarez A, Perez CA, et al. Acetylcholinesterase accelerates assembly of amyloid-beta-peptides into Alzheimer’s fibrils: possible role of the peripheral site of the enzyme. Neuron 1996; 16(4): 881-91.
[33]
Alvarez A, Opazo C, Alarcon R, Garrido J, Inestrosa NC. amyloid–cholinesterase interactions implications for Alzheimer’s disease. J Mol Biol 1997; 272: 348-61.
[34]
Ismail MM, Kamel MM, Mohamed LW, Faggal SI. Synthesis of new indole derivatives structurally related to donepezil and their biological evaluation as acetylcholinesterase inhibitors. Molecules 2012; 17: 4811-23.
[35]
Pakaski M, Kalman J. Interactions between amyloid and cholinergic mechanisms in Alzheimer’s disease. Neurochem Int 2008; 53: 103-11.
[36]
Zhou X, Wang XB, Wang T, Kong LY. Design, synthesis and acetylcholinesterase inhibitory activity of novel coumarin analogues. Bioorg Med Chem 2008; 16: 8011-21.
[37]
Liu SJ, Cui L, Xu L, Wang T. Design, synthesis, and biological evaluation of 7H-thiazolo [3,2-b]-1,2,4-triazin-7-one derivatives as dual binding site acetylcholinesterase inhibitors. Heterocycles 2013; 87(12): 2607-14.
[38]
Carlos HT, Silva DP, Campo VL, Carvalho I, Taft A. Molecular modeling, docking and ADMET studies applied to the design of a novel Hybrid for treatment of Alzheimer’s disease. J Mol Graph Model 2006; 25(2): 169-75.
[39]
Lemmin T, Bovigny C, Lançon D, Dal Peraro M. Cardiolipin models for molecular simulations of bacterial and mitochondrial membranes. J Chem Theory Comput 2013; 9(1): 670-8.
[40]
Chaney MO, Webster SD, Kuo YM, Roher AR. Molecular modeling of the Aβ1-42 peptide from Alzheimer disease. Protein Eng 1998; 11(9): 761-2.
[41]
Li YP, Weng X, Ning FX, et al. 3D-QSAR studies of azaoxoisoaporphine, oxoaporphine, and oxoisoaporphine derivatives as anti-AChE and anti-AD agents by the CoMFA method. J Mol Graph Model 2013; 41: 61-7.
[42]
Zhang D, Mattila MP, Shahsavani M, Falk A, Teixeira AI, Herl A. A 3D Alzheimer’s disease culture model and the induction of P21- activated kinase mediated sensing in iPSC derived neuronsequences. Biomaterials 2014; 35: 1420-8.
[43]
Ozkay UD, Can OD, Ozkay Y, Ozturk Y. Effect of benzothiazole/piperazine derivative on intracerebroventricular streptozotocin-induced cognitive deficits. Pharmacol Rep 2012; 64: 834-47.
[44]
Pinton S, da Rocha JT, Zeni G, Nogueira CW. Organo selenium improves memory decline in mice: Involvement of acetylcholinesterase activity. Neurosci Lett 2010; 472: 56-60.
[45]
Ogura H, Kosasa T, Kuriya Y, Yamanishi Y. Donepezil, a centrally acting acetylcholinesterase inhibitor, alleviates learning deficits in hypocholinergic models in rats. Methods Find Exp Clin Pharmacol 2000; 22: 89-95.

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy