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Advances in quantitative structure–activity relationship models of anti-Alzheimer’s agents

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Bibliography

  • Alzheimer’s Disease Education and Referral Center. About Alzheimer’s Disease: Alzheimer’s Basic. Available from: http://www.nia.nih.gov/alzheimers/topics/alzheimers-basics [Last accessed 10 January 2014]
  • Maurer K, Volk S, Gerbaldo H, Auguste D. Alzheimer’s disease. Lancet 1997;349(9064):1546-9
  • Minati L, Edginton T, Bruzzone MG, Giaccone G. Reviews: current concepts in alzheimer’s disease: a multidisciplinary review. Am J Alzheimers Dis Other Demen 2009;24(2):95-121
  • Wimo A, Prince MJ. World alzheimer report 2010: the global economic impact of dementia. Alzheimer’s Disease International; 2010. Available from: http://www.alz.co.uk/research/world-report-2010
  • 2009 Progress Report on Alzheimer’s Disease: Translating New Knowledge National Institute on Aging, National Institutes of Health, US Department of Health and Human Services, Publication no 10-7500,November 2010
  • Alzheimer’s Disease Education & Referral (ADEAR) Center. Alzheimer’s disease fact sheet. US Department of Health and Human Services, National Institutes of Health NIH Publication No 11- 6423 July 2011 2011
  • Francis PT, Palmer AM, Snape M, Wilcock GK. The cholinergic hypothesis of Alzheimer’s disease: a review of progress. J Neurol Neurosurg Psychiatry 1999;66(2):137-47
  • Lee VMY, Brunden KR, Hutton M, Trojanowski JQ. Developing therapeutic approaches to tau, selected kinases, and related neuronal protein targets. Cold Spring Harb Perspect Med 2011;1(1):a006437
  • Hardy J, Allsop D. Amyloid deposition as the central event in the aetiology of Alzheimer’s disease. Trends Pharmacol Sci 1991;12:383-8
  • Mendez MF. The accurate diagnosis of early-onset dementia. Int J Psychiatry Med 2006;36(4):401-12
  • Coimbra A, Williams DS, Hostetler ED. The role of MRI and PET/SPECT in Alzheimers disease. Curr Top Med Chem 2006;6(6):629-47
  • Hardy J, Selkoe DJ. The amyloid hypothesis of Alzheimer’s disease: progress and problems on the road to therapeutics. Science 2002;297(5580):353-6
  • Ono M, Saji H. SPECT imaging agents for detecting cerebral β-amyloid plaques. Int J Mol Imaging 2011;2011:543267
  • Thies W, Bleiler L. 2011 Alzheimer’s disease facts and figures. Alzheimers Dement 2011;7(2):208
  • Roy K, Mitra I. On various metrics used for validation of predictive QSAR models with applications in virtual screening and focused library design. Comb Chem High Throughput Screen 2011;14(6):450-74
  • Basak SC, Gute BD, Grunwald GD. Use of topostructural, topochemical, and geometric parameters in the prediction of vapor pressure: a hierarchical QSAR approach. J Chem Inf Comput Sci 1997;37(4):651-5
  • Verma J, Khedkar VM, Coutinho EC. 3D-QSAR in drug design-a review. Curr Top Med Chem 2010;10(1):95-115
  • Rosenbaum L, Dorr A, Bauer MR, et al. Inferring multi-target QSAR models with taxonomy-based multi-task learning. J Cheminform 2013;5(1):33
  • Prado-Prado F, Garcia-Mera X, Escobar M, et al. 3D MI-DRAGON: new model for the reconstruction of US FDA drug-target network and theoretical-experimental studies of inhibitors of rasagiline derivatives for AChE. Curr Top Med Chem 2012;12(16):1843-65
  • Speck-Planche A, V Kleandrova V, Luan F, Cordeiro NDS. Multi-target inhibitors for proteins associated with Alzheimer: in silico discovery using fragment-based descriptors. Curr Alzheimer Res 2013;10(2):117-24
  • Luan F, Cordeiro M, Alonso N, et al. 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 2013;21(7):1870-9
  • Ma XH, Shi Z, Tan C, et al. In-silico approaches to multi-target drug discovery. Pharm Res 2010;27(5):739-49
  • Pratim Roy P, Paul S, Mitra I, Roy K. On two novel parameters for validation of predictive QSAR models. Molecules 2009;14(5):1660-701
  • Roy K, Chakraborty P, Mitra I, et al. Some case studies on application of "rm2" metrics for judging quality of quantitative structure-activity relationship predictions: emphasis on scaling of response data. J Comput Chem 2013;34(12):1071-82
  • Golbraikh A, Tropsha A. Beware of q2. J Mol Graph Model 2002;20(4):269-76
  • Tropsha A. Best practices for QSAR model development, validation, and exploitation. Mol Inform 2010;29(6-7):476-88
  • Golbraikh A, Muratov EN, Fourches D, Tropsha A. Dataset modelability by QSAR. J Chem Inf Model 2013;54(1):1-4
  • Guha R, Van Drie JH. Structure-activity landscape index: identifying and quantifying activity cliffs. J Chem Inf Model 2008;48(3):646-58
  • Seebeck B, Wagener M, Rarey M. From activity cliffs to target-specific scoring models and pharmacophore hypotheses. ChemMedChem 2011;6(9):1630-9
  • Stumpfe D, Hu Y, Dimova D, Bajorath J. Recent progress in understanding activity cliffs and their utility in medicinal chemistry: miniperspective. J Med Chem 2014;57(1):18-28
  • Hu Y, Stumpfe D, Bajorath J. Advancing the activity cliff concept. F1000Res 2013;2:199
  • Koutsoukas A, Paricharak S, Galloway W, et al. How diverse are diversity assessment methods? A comparative analysis and benchmarking of molecular descriptor space. J Chem Inf Model 2013. [ Epub ahead of print]
  • Martin TM, Harten P, Young DM, et al. Does rational selection of training and test sets improve the outcome of QSAR modeling? J Chem Inf Model 2012;52(10):2570-8
  • Dearden JC, Cronin MTD, Kaiser KLE. How not to develop a quantitative structure-activity or structure-property relationship (QSAR/QSPR). SAR QSAR Environ Res 2009;20(3-4):241-66
  • Cramer RD. The inevitable QSAR renaissance. J Comput Aided Mol Des 2012;26(1):35-8
  • Cherkasov A, Muratov EN, Fourches D, et al. QSAR modeling: where have you been? Where are you going to? J Med Chem 2014. [ Epub ahead of print]
  • Greig NH, Lahiri DK, Sambamurti K. Butyrylcholinesterase: an important new target in Alzheimer’s disease therapy. Int Psychogeriatr 2002;14(S1):77-91
  • Tong W, Collantes ER, Chen Y, Welsh WJ. A comparative molecular field analysis study of N-benzylpiperidines as acetylcholinesterase inhibitors. J Med Chem 1996;39(2):380-7
  • Recanatini M, Cavalli A, Hansch C. A comparative QSAR analysis of acetylcholinesterase inhibitors currently studied for the treatment of Alzheimer’s disease. Chem Biol Interact 1997;105(3):199-228
  • Debord J, N’Diaye P, Bollinger JC, et al. Cholinesterase inhibition by derivatives of 2-amino-4, 6-dimethylpyridine. J Enzyme Inhib 1997;12(1):13-26
  • Xu R, Sim M-K, Go M-L. Synthesis and pharmacological characterization of O-alkynyloximes of tropinone and N-methylpiperidinone as muscarinic agonists. J Med Chem 1998;41(17):3220-31
  • Kaur J, Zhang MQ. Molecular modelling and QSAR of reversible acetylcholines-terase inhibitors. Curr Med Chem 2000;7(3):273-94
  • Nielsen SF, Nielsen EO, Olsen GM, et al. Novel potent ligands for the central nicotinic acetylcholine receptor: synthesis, receptor binding, and 3D-QSAR analysis. J Med Chem 2000;43(11):2217-26
  • Recanatini M, Cavalli A, Belluti F, et al. SAR of 9-amino-1, 2, 3, 4-tetrahydroacridine-based acetylcholinesterase inhibitors: synthesis, enzyme inhibitory activity, QSAR, and structure-based CoMFA of tacrine analogues. J Med Chem 2000;43(10):2007-18
  • Sippl W, Contreras J-M, Parrot I, et al. Structure-based 3D QSAR and design of novel acetylcholinesterase inhibitors. J Comput Aided Mol Des 2001;15(5):395-410
  • Nicolotti O, Pellegrini-Calace M, Altomare C, et al. Ligands of neuronal nicotinic acetylcholine receptor (nAChR): inferences from the Hansch and 3-D quantitative structure-activity relationship (QSAR) models. Curr Med Chem 2002;9(1):1-29
  • Nicolotti O, Altomare C, Pellegrini-Calace M, Carotti A. Neuronal nicotinic acetylcholine receptor agonists: pharmacophores, evolutionary QSAR and 3D-QSAR models. Curr Top Med Chem 2004;4(3):335-60
  • Lin G, Chen G-H, Lu C-P, Yeh S-C. QSARs for peripheral anionic site of butyrylcholinesterase with inhibitions by 4-acyloxy-biphenyl-4’-N-butylcarbamates. QSAR Comb Sci 2005;24(8):943-52
  • Shen L-L, Liu G-X, Tang Y. Molecular docking and 3D-QSAR studies of 2-substituted 1-indanone derivatives as acetylcholinesterase inhibitors. Acta Pharmacol Sin 2007;28:2053-63
  • Liu A, Guang H, Zhu L, et al. 3D-QSAR analysis of a new type of acetylcholinesterase inhibitors. Sci China C Life Sci 2007;50(6):726-30
  • Zaheer-ul H, Uddin R, Yuan H, et al. Receptor-based modeling and 3D-QSAR for a quantitative production of the butyrylcholinesterase inhibitors based on genetic algorithm. J Chem Inf Model 2008;48(5):1092-103
  • Roy KK, Dixit A, Saxena AK. An investigation of structurally diverse carbamates for acetylcholinesterase (AChE) inhibition using 3D-QSAR analysis. J Mol Graph Model 2008;27(2):197-208
  • Ul-Haq Z, Mahmood U, Jehangir B. Ligand-based 3D-QSAR Studies of Physostigmine Analogues as Acetylcholinesterase Inhibitors. Chem Biol Drug Des 2009;74(6):571-81
  • Asadabadi EB, Abdolmaleki P, Barkooie SMH, et al. A combinatorial feature selection approach to describe the QSAR of dual site inhibitors of acetylcholinesterase. Comput Biol Med 2009;39(12):1089-95
  • Solomon KA, Sundararajan S, Abirami V. QSAR studies on N-aryl derivative activity towards Alzheimer’s disease. Molecules 2009;14(4):1448-55
  • Takahashi J, Hijikuro I, Kihara T, et al. Design, synthesis, evaluation and QSAR analysis of N1-substituted norcymserine derivatives as selective butyrylcholinesterase inhibitors. Bioorg Med Chem Lett 2010;20(5):1718-20
  • Gupta S, Fallarero A, Vainio MJ, et al. Molecular docking guided comparative GFA, G/PLS, SVM and ANN models of structurally diverse dual binding site acetylcholinesterase inhibitors. Mol Inform 2011;30(8):689-706
  • Bitencourt M, Freitas MP, Rittner R. The MIA-QSAR method for the prediction of bioactivities of possible acetylcholinesterase inhibitors. Arch Pharm (Weinheim) 2012;345(9):723-8
  • Yan A, Wang K. Quantitative structure and bioactivity relationship study on human acetylcholinesterase inhibitors. Bioorg Med Chem Lett 2012;22(9):3336-42
  • Ambure P, Kar S, Roy K. Pharmacophore mapping-based virtual screening followed by molecular docking studies in search of potential acetylcholinesterase inhibitors as anti-Alzheimer’s agents. Biosystems 2014;116:10-20
  • Deb PK, Sharma A, Piplani P, Akkinepally RR. Molecular docking and receptor-specific 3D-QSAR studies of acetylcholinesterase inhibitors. Mol Divers 2012;16(4):803-23
  • de Souza SD, de Souza AMT, de Sousa ACC, et al. Hologram QSAR models of 4-[(diethylamino) methyl]-phenol inhibitors of acetyl/butyrylcholinesterase enzymes as potential anti-Alzheimer agents. Molecules 2012;17(8):9529-39
  • Chitranshi N, Gupta S, Tripathi PK, Seth PK. New molecular scaffolds for the design of Alzheimer’s acetylcholinesterase inhibitors identified using ligand-and receptor-based virtual screening. Med Chem Res 2013;22(5):2328-45
  • Debnath B, Gayen S, Basu A, et al. Quantitative structure-activity relationship study on some benzodiazepine derivatives as anti-Alzheimer agents. J Mol Model 2004;10(5-6):328-34
  • Ravi Keerti A, Ashok Kumar B, Parthasarathy T, Uma V. QSAR studies potent benzodiazepine gamma-secretase inhibitors. Bioorg Med Chem 2005;13(5):1873-8
  • Al-Nadaf A, Sheikha GA, Taha MO. Elaborate ligand-based pharmacophore exploration and QSAR analysis guide the synthesis of novel pyridinium-based potent beta-secretase inhibitory leads. Bioorg Med Chem 2010;18(9):3088-115
  • Salum LB, Valadares NF. Fragment-guided approach to incorporating structural information into a CoMFA study: BACE-1 as an example. J Comput Aided Mol Des 2010;24(10):803-17
  • Manoharan P, Vijayan RSK, Ghoshal N. Rationalizing fragment based drug discovery for BACE1: insights from FB-QSAR, FB-QSSR, multi objective (MO-QSPR) and MIF studies. J Comput Aided Mol Des 2010;24(10):843-64
  • Liu S, Fu R, Cheng X, et al. Exploring the binding of BACE-1 inhibitors using comparative binding energy analysis (COMBINE). BMC Struct Biol 2012;12(1):21
  • Meek AR, Simms GA, Weaver DF. In silico search for an endogenous anti-Alzheimer’s molecule — Screening amino acid metabolic pathways. Can J Chem 2012;90(10):865-73
  • Nastase AF, Boyd DB. Simple structure-based approach for predicting the activity of inhibitors of beta-secretase (BACE1) associated with Alzheimer’s disease. J Chem Inf Model 2012;52(12):3302-7
  • Manoharan P, Ghoshal N. Rationalizing lead optimization by consensus 2D-CoMFA CoMSIA GRIND (3D) QSAR guided fragment hopping in search of beta-secretase inhibitors. Mol Divers 2012;16(3):563-77
  • Bhadoriya KS, Sharma MC, Sharma S, et al. An approach to design potent anti-Alzheimer’s agents by 3D-QSAR studies on fused 5, 6-bicyclic heterocycles as beta-secretase modulators using kNN-MFA methodology. Arab J Chem 2013; Available from: http://dx.doi.org/10.1016/j.arabjc.2013.02.002
  • Suzuki K, Hamada Y, Nguyen J-T, Kiso Y. Novel BACE1 inhibitors with a non-acidic heterocycle at the P1’ position. Bioorg Med Chem 2013;21(21):6665-73
  • Valasani KR, Hu G, Chaney MO, Yan SS. Structure-based design and synthesis of benzothiazole phosphonate analogues with inhibitors of human ABAD-amyloid beta for treatment of Alzheimer’s disease. Chem Biol Drug Des 2013;81(2):238-49
  • Li Y-P, Weng X, Ning F-X, 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
  • Huang D, Liu Y, Shi B, et al. Comprehensive 3D-QSAR and binding mode of BACE-1 inhibitors using R-group search and molecular docking. J Mol Graph Model 2013;45:65-83
  • Hossain T, Islam MA, Pal R, Saha A. Exploring structural requirement and binding interactions of β-amyloid cleavage enzyme inhibitors using molecular modeling techniques. Med Chem Res 2013;22(10):4766-74
  • Abu Hammad AM, Taha MO. Pharmacophore modeling, quantitative structure-activity relationship analysis, and shape-complemented in silico screening allow access to novel influenza neuraminidase inhibitors. J Chem Inf Model 2009;49(4):978-96
  • Jain P, Jadhav HR. Quantitative structure activity relationship analysis of aminoimidazoles as BACE-I inhibitors. Med Chem Res 2013;22(4):1740-6
  • Ajmani S, Janardhan S, Viswanadhan VN. Toward a general predictive QSAR model for gamma-secretase inhibitors. Mol Divers 2013;17:421-34
  • Aplin AE, Gibb GM, Jacobsen JS, et al. In vitro phosphorylation of the cytoplasmic domain of the amyloid precursor protein by glycogen synthase kinase-3beta. J Neurochem 1996;67(2):699-707
  • Martinez A, Alonso M, Castro A, et al. SAR and 3D-QSAR studies on thiadiazolidinone derivatives: exploration of structural requirements for glycogen synthase kinase 3 inhibitors. J Med Chem 2005;48(23):7103-12
  • Sivaprakasam P, Xie A, Doerksen RJ. Probing the physicochemical and structural requirements for glycogen synthase kinase-3α inhibition: 2D-QSAR for 3-anilino-4-phenylmaleimides. Bioorg Med Chem 2006;14(24):8210-18
  • Dessalew N, Patel DS, Bharatam PV. 3D-QSAR and molecular docking studies on pyrazolopyrimidine derivatives as glycogen synthase kinase-3beta inhibitors. J Mol Graph Model 2007;25(6):885-95
  • Lather V, Kristam R, Saini JS, et al. QSAR models for prediction of glycogen synthase kinase-3beta inhibitory activity of indirubin derivatives. QSAR Comb Sci 2008;27(6):718-28
  • Prasanna S, Daga PR, Xie A, Doerksen RJ. Glycogen synthase kinase-3 inhibition by 3-anilino-4-phenylmaleimides: insights from 3D-QSAR and docking. J Comput Aided Mol Des 2009;23(2):113-27
  • Park HR, Kim MK, Kim DW, et al. 3D QSAR CoMFA Study on Phenylthiazolylhydrazide (PTH) Derivataives as Tau Protein Aggregation Inhibitors. Bull Korean Chem Soc 2010;31(12):3838-41
  • Fang J, Huang D, Zhao W, et al. A new protocol for predicting novel GSK-3β ATP competitive inhibitors. J Chem Inf Model 2011;51(6):1431-8
  • García I, Fall Y, Gomez G, Gonzalez-Diaz H. First computational chemistry multi-target model for anti-Alzheimer, anti-parasitic, anti-fungi, and anti-bacterial activity of GSK-3 inhibitors in vitro, in vivo, and in different cellular lines. Mol Divers 2010;15(2):561-7
  • Haq ZU, Uddin R, Wai LK, et al. Docking and 3D-QSAR modeling of cyclin-dependent kinase 5/p25 inhibitors. J Mol Model 2011;17(5):1149-61
  • Ambure P, Roy K. Exploring structural requirements of leads for improving activity and selectivity against CDK5/p25 in Alzheimer’s disease: an in silico approach. RSC Adv 2014;4(13):6702-9
  • Efange SMN, Garland EM, Staley JK, et al. Vesicular acetylcholine transporter density and Alzheimer’s disease. Neurobiol Aging 1997;18(4):407-13
  • Wang W, Zhang J, Liu B. QSAR study of 125 I-labeled 2-(4-aminophenyl) benzothiazole derivatives as imaging agents for β-amyloid in the brain with Alzheimer’s disease. J Radioanal Nucl Chem 2005;266(1):107-11
  • Chen X. QSAR and primary docking studies of trans-stilbene (TSB) series of imaging agents for beta-amyloid plaques. J Mol Struct 2006;763(1):83-9
  • Kim MK, Choo IH, Lee HS, et al. 3D-QSAR of PET agents for imaging beta-amyloid in Alzheimer’s disease. Bull Korean Chem Soc 2007;28(7):1231
  • Kovac M, Mavel S, Deuther-Conrad W, et al. 3D QSAR study, synthesis, and in vitro evaluation of (+)-5-FBVM as potential PET radioligand for the vesicular acetylcholine transporter (VAChT). Bioorg Med Chem 2010;18(21):7659-67
  • Yang Y, Zhu L, Chen X, Zhang H. Binding research on flavones as ligands of beta-amyloid aggregates by fluorescence and their 3D-QSAR, docking studies. J Mol Graph Model 2010;29(4):538-45
  • Cisek K, Kuret J. QSAR studies for prediction of cross-beta sheet aggregate binding affinity and selectivity. Bioorg Med Chem 2012;20(4):1434-41
  • Lam YA, Pickart CM, Alban A, et al. Inhibition of the ubiquitin-proteasome system in Alzheimer’s disease. Proc Natl Acad Sci USA 2000;97(18):9902-6
  • Zhu Y-Q, Pei J-F, Liu Z-M, et al. 3D-QSAR studies on tripeptide aldehyde inhibitors of proteasome using CoMFA and CoMSIA methods. Bioorg Med Chem 2006;14(5):1483-96
  • Ishiura S, Tsukahara T, Tabira T, et al. Identification of a putative amyloid A4-generating enzyme as a prolyl endopeptidase. FEBS Lett 1990;260(1):131-4
  • Pripp AH. Quantitative structure-activity relationship of prolyl oligopeptidase inhibitory peptides derived from β-casein using simple amino acid descriptors. J Agric Food Chem 2006;54(1):224-8
  • Brioni JD, Esbenshade TA, Garrison TR, et al. Discovery of histamine H3 antagonists for the treatment of cognitive disorders and Alzheimer’s disease. J Pharmacol Exp Ther 2011;336(1):38-46
  • Dastmalchi S, Hamzeh-Mivehroud M, Ghafourian T, Hamzeiy H. Molecular modeling of histamine H3 receptor and QSAR studies on arylbenzofuran derived H3 antagonists. J Mol Graph Model 2008;26(5):834-44
  • Riederer P, Danielczyk W, Grunblatt E. Monoamine oxidase-B inhibition in Alzheimer’s disease. Neurotoxicology 2004;25(1):271-7
  • Speck-Planche A, Kleandrova VV. 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 2012;12(16):1734-47
  • Firoozpour L, Sadatnezhad K, Dehghani S, et al. An efficient piecewise linear model for predicting activity of caspase-3 inhibitors. DARU J Pharm Sci 2012;20(1):31
  • Hajjo R, Setola V, Roth BL, Tropsha A. Chemocentric informatics approach to drug discovery: identification and experimental validation of selective estrogen receptor modulators as ligands of 5-hydroxytryptamine-6 receptors and as potential cognition enhancers. J Med Chem 2012;55(12):5704-19
  • Makhaeva GF, Radchenko EV, Baskin II, et al. Combined QSAR studies of inhibitor properties of O-phosphorylated oximes toward serine esterases involved in neurotoxicity, drug metabolism and Alzheimer’s disease. SAR QSAR Environ Res 2012;23(7-8):627-47
  • Fresqui MAC, Ferreira MM, Trsic M. The influence of R and S configurations of a series of amphetamine derivatives on QSAR models. Anal Chim Acta 2013;759:43-52
  • Ryoo S-R, Cho H-J, Lee H-W, et al. Dual-specificity tyrosine (Y)-phosphorylation regulated kinase 1-mediated phosphorylation of amyloid precursor protein: evidence for a functional link between Down syndrome and Alzheimer’s disease. J Neurochem 2008;104(5):1333-44
  • Bharate SB, Yadav RR, Vishwakarma RA. QSAR and pharmacophore study of Dyrk1A inhibitory meridianin analogs as potential agents for treatment of neurodegenerative diseases. Med Chem 2013;9(1):152-61
  • Nayana MRS, Sekhar YN, Kumari NS, Mahmood S. 3D-QSAR CoMFA study on human glutaminyl cyclase inhibitors. Internet Electron J Mol Des 2007;6:320-30
  • Diniz C, Borges F, Santana L, et al. Ligands and therapeutic perspectives of adenosine A2A receptors. Curr Pharm Des 2008;14(17):1698-722
  • Geldenhuys WJ, Van der Schyf CJ. Serotonin 5-HT6 receptor antagonists for the treatment of Alzheimers disease. Curr Top Med Chem 2008;8(12):1035-48
  • Sun A, Liu M, Nguyen XV, Bing G. P38 MAP kinase is activated at early stages in Alzheimer’s disease brain. Exp Neurol 2003;183(2):394-405
  • Femminella GD, Rengo G, Pagano G, et al. Beta-adrenergic receptors and G protein-coupled receptor kinase-2 in Alzheimer’s disease: a new paradigm for prognosis and therapy? J Alzheimers Dis 2013;34(2):341-7
  • Thathiah A, Horre K, Snellinx A, et al. [beta]-arrestin 2 regulates A [beta] generation and [gamma]-secretase activity in Alzheimer’s disease. Nat med 2013;19(1):43-9
  • Tang Z, Bereczki E, Zhang H, et al. Mammalian target of rapamycin (mTor) mediates tau protein dyshomeostasis implication for Alzheimer disease. J Biol Chem 2013;288(22):15556-70
  • Albani D, Polito L, Forloni G. Sirtuins as novel targets for Alzheimer’s disease and other neurodegenerative disorders: experimental and genetic evidence. J Alzheimers Dis 2010;19(1):11-26
  • Rosen C, Hansson O, Blennow K, Zetterberg H. Fluid biomarkers in Alzheimer’s disease-current concepts. Mol Neurodegener 2013;8:20
  • Bekris LM, Lutz F, Montine TJ, et al. MicroRNA in Alzheimer’s disease: an exploratory study in brain, cerebrospinal fluid and plasma. Biomarkers 2013;18(5):455-66
  • Podlesniy P, Figueiro-Silva J, Llado A, et al. Low cerebrospinal fluid concentration of mitochondrial DNA in preclinical Alzheimer disease. Ann neurol 2013;74(5):655-68

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