Login Register Cart 0
Generic placeholder image

Current Medicinal Chemistry

Editor-in-Chief

ISSN (Print): 0929-8673
ISSN (Online): 1875-533X

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe Translate in Chinese
Review Article

Computational Strategies to Identify New Drug Candidates against Neuroinflammation

Author(s): Matteo Pavan, Davide Bassani, Giovanni Bolcato, Maicol Bissaro, Mattia Sturlese and Stefano Moro*

Volume 29, Issue 27, 2022

Published on: 30 March, 2022

Page: [4756 - 4775] Pages: 20

DOI: 10.2174/0929867329666220208095122

Price: $65

Abstract

Increasing application of computational approaches in these last decades has deeply modified the process of discovery and commercialization of new therapeutic entities. This is especially true in the field of neuroinflammation, in which both the peculiar anatomical localization and the presence of the blood-brain barrier make it mandatory to finely tune the candidates’ physicochemical properties from the early stages of the discovery pipeline. The aim of this review is, therefore, to provide a general overview of neuroinflammation to the readers, together with the most common computational strategies that can be exploited to discover and design small molecules controlling neuroinflammation, especially those based on the knowledge of the three-dimensional structure of the biological targets of therapeutic interest. The techniques used to describe the molecular recognition mechanisms, such as molecular docking and molecular dynamics, will therefore be discussed, highlighting their advantages and limitations. Finally, we report several case studies in which computational methods have been applied to drug discovery for neuroinflammation, focusing on the research conducted in the last decade.

Keywords: Neuroinflammation, drug design, molecular modeling, molecular docking, molecular dynamics, BBB permeation.

« Previous
[1]
Lucas, S-M.; Rothwell, N.J.; Gibson, R.M. The role of inflammation in CNS injury and disease. Br. J. Pharmacol., 2006, 147(Suppl. 1), S232-S240.
[http://dx.doi.org/10.1038/sj.bjp.0706400] [PMID: 16402109]
[2]
Furman, D.; Campisi, J.; Verdin, E.; Carrera-Bastos, P.; Targ, S.; Franceschi, C.; Ferrucci, L.; Gilroy, D.W.; Fasano, A.; Miller, G.W.; Miller, A.H.; Mantovani, A.; Weyand, C.M.; Barzilai, N.; Goronzy, J.J.; Rando, T.A.; Effros, R.B.; Lucia, A.; Kleinstreuer, N.; Slavich, G.M. Chronic inflammation in the etiology of disease across the life span. Nat. Med., 2019, 25(12), 1822-1832.
[http://dx.doi.org/10.1038/s41591-019-0675-0] [PMID: 31806905]
[3]
Glass, C.K.; Saijo, K.; Winner, B.; Marchetto, M.C.; Gage, F.H. Mechanisms underlying inflammation in neurodegeneration. Cell, 2010, 140(6), 918-934.
[http://dx.doi.org/10.1016/j.cell.2010.02.016] [PMID: 20303880]
[4]
Gambino, C.M.; Sasso, B.L.; Bivona, G.; Agnello, L.; Ciaccio, M. Aging and neuroinflammatory disorders: New biomarkers and therapeutic targets. Curr. Pharm. Des., 2019, 25(39), 4168-4174.
[http://dx.doi.org/10.2174/1381612825666191112093034] [PMID: 31721696]
[5]
Block, M.L.; Hong, J-S. Microglia and inflammation-mediated neurodegeneration: multiple triggers with a common mechanism. Prog. Neurobiol., 2005, 76(2), 77-98.
[http://dx.doi.org/10.1016/j.pneurobio.2005.06.004] [PMID: 16081203]
[6]
Kempuraj, D. Neuroinflammation induces neurodegeneration. J. Neurol. Neurosurg. spine, 2016, 1(1), 1003.
[7]
Woodling, N.S.; Andreasson, K.I. Untangling the web: Toxic and protective effects of neuroinflammation and PGE2 signaling in Alzheimer’s disease. ACS Chem. Neurosci., 2016, 7(4), 454-463.
[http://dx.doi.org/10.1021/acschemneuro.6b00016] [PMID: 26979823]
[8]
Hooten, K.G.; Beers, D.R.; Zhao, W.; Appel, S.H. Protective and toxic neuroinflammation in amyotrophic lateral sclerosis. Neurotherapeutics, 2015, 12(2), 364-375.
[http://dx.doi.org/10.1007/s13311-014-0329-3] [PMID: 25567201]
[9]
Obermeier, B.; Daneman, R.; Ransohoff, R.M. Development, maintenance and disruption of the blood-brain barrier. Nat. Med., 2013, 19(12), 1584-1596.
[http://dx.doi.org/10.1038/nm.3407] [PMID: 24309662]
[10]
Carson, M.J.; Thrash, J.C.; Walter, B. The cellular response in neuroinflammation: The role of leukocytes, microglia and astrocytes in neuronal death and survival. Clin. Neurosci. Res., 2006, 6(5), 237-245.
[http://dx.doi.org/10.1016/j.cnr.2006.09.004] [PMID: 19169437]
[11]
Patel, J.P.; Frey, B.N. Disruption in the blood-brain barrier: The missing link between brain and body inflammation in bipolar disorder? Neural Plast., 2015, 2015, 708306.
[http://dx.doi.org/10.1155/2015/708306] [PMID: 26075104]
[12]
Wraith, D.C.; Nicholson, L.B. The adaptive immune system in diseases of the central nervous system. J. Clin. Invest., 2012, 122(4), 1172-1179.
[http://dx.doi.org/10.1172/JCI58648] [PMID: 22466659]
[13]
Korn, T.; Kallies, A. T cell responses in the central nervous system. Nat. Rev. Immunol., 2017, 17(3), 179-194.
[http://dx.doi.org/10.1038/nri.2016.144] [PMID: 28138136]
[14]
Fields, G.B. The rebirth of matrix metalloproteinase inhibitors: Moving beyond the dogma. Cells, 2019, 8(9), 984.
[http://dx.doi.org/10.3390/cells8090984] [PMID: 31461880]
[15]
Colombo, E.; Farina, C. Astrocytes: Key regulators of neuro inflammation. Trends Immunol., 2016, 37(9), 608-620.
[http://dx.doi.org/10.1016/j.it.2016.06.006] [PMID: 27443914]
[16]
Liddelow, S.A.; Barres, B.A. Reactive astrocytes: Production, function, and therapeutic potential. Immunity, 2017, 46(6), 957-967.
[http://dx.doi.org/10.1016/j.immuni.2017.06.006] [PMID: 28636962]
[17]
Kwon, H.S.; Koh, S-H. Neuroinflammation in neurodegenerative disorders: the roles of microglia and astrocytes. Transl. Neurodegener., 2020, 9(1), 42.
[http://dx.doi.org/10.1186/s40035-020-00221-2] [PMID: 33239064]
[18]
Stephenson, J.; Nutma, E.; van der Valk, P.; Amor, S. Inflammation in CNS neurodegenerative diseases. Immunology, 2018, 154(2), 204-219.
[http://dx.doi.org/10.1111/imm.12922] [PMID: 29513402]
[19]
Sofroniew, M.V. Molecular dissection of reactive astrogliosis and glial scar formation. Trends Neurosci., 2009, 32(12), 638-647.
[http://dx.doi.org/10.1016/j.tins.2009.08.002] [PMID: 19782411]
[20]
Block, M.L.; Zecca, L.; Hong, J-S. Microglia-mediated neurotoxicity: Uncovering the molecular mechanisms. Nat. Rev. Neurosci., 2007, 8(1), 57-69.
[http://dx.doi.org/10.1038/nrn2038] [PMID: 17180163]
[21]
Nimmerjahn, A. Resting microglial cells are highly dynamic surveillants of brain parenchyma in vivo. Science (80-), 2005, 308, 1314-1318.
[22]
Fetler, L. Neuroscience: Brain under surveillance: The microglia patrol. Science (80-), 2005, 309, 392-393.
[23]
Rock, R.B.; Gekker, G.; Hu, S.; Sheng, W.S.; Cheeran, M.; Lokensgard, J.R.; Peterson, P.K. Role of microglia in central nervous system infections. Clin. Microbiol. Rev., 2004, 17(4), 942-964.
[http://dx.doi.org/10.1128/CMR.17.4.942-964.2004] [PMID: 15489356]
[24]
Polazzi, E.; Contestabile, A. Reciprocal interactions between microglia and neurons: from survival to neuropathology. Rev. Neurosci., 2002, 13(3), 221-242.
[http://dx.doi.org/10.1515/REVNEURO.2002.13.3.221] [PMID: 12405226]
[25]
Kumar, V. Toll-like receptors in the pathogenesis of neuroinflammation. J. Neuroimmunol., 2019, 332, 16-30.
[http://dx.doi.org/10.1016/j.jneuroim.2019.03.012] [PMID: 30928868]
[26]
Di Virgilio, F.; Ceruti, S.; Bramanti, P.; Abbracchio, M.P. Purinergic signalling in inflammation of the central nervous system. Trends Neurosci., 2009, 32(2), 79-87.
[http://dx.doi.org/10.1016/j.tins.2008.11.003] [PMID: 19135728]
[27]
Husemann, J.; Loike, J.D.; Anankov, R.; Febbraio, M.; Silverstein, S.C. Scavenger receptors in neurobiology and neuropathology: Their role on microglia and other cells of the nervous system. Glia, 2002, 40(2), 195-205.
[http://dx.doi.org/10.1002/glia.10148] [PMID: 12379907]
[28]
Kaminska, B.; Mota, M.; Pizzi, M. Signal transduction and epigenetic mechanisms in the control of microglia activation during neuroinflammation. Biochim. Biophys. Acta, 2016, 1862(3), 339-351.
[http://dx.doi.org/10.1016/j.bbadis.2015.10.026] [PMID: 26524636]
[29]
Liu, T.; Zhang, L.; Joo, D.; Sun, S-C. NF-κB signaling in inflammation. Signal Transduct. Target. Ther., 2017, 2017(212), 1-9.
[30]
Ji, R-R.; Xu, Z-Z.; Gao, Y-J. Emerging targets in neuroinflammation-driven chronic pain. Nat. Rev. Drug Discov., 2014, 13(7), 533-548.
[http://dx.doi.org/10.1038/nrd4334] [PMID: 24948120]
[31]
Cianciulli, A.; Porro, C.; Calvello, R.; Trotta, T.; Lofrumento, D.D.; Panaro, M.A. Microglia mediated neuroinflammation: Focus on PI3K modulation. Biomolecules, 2020, 10(1), 137.
[http://dx.doi.org/10.3390/biom10010137] [PMID: 31947676]
[32]
Karin, M. How NF-kappaB is activated: the role of the IkappaB kinase (IKK) complex. Oncogene, 1999, 18(49), 6867-6874.
[http://dx.doi.org/10.1038/sj.onc.1203219] [PMID: 10602462]
[33]
Ghosh, S.; Hayden, M.S. New regulators of NF-kappaB in inflammation. Nat. Rev. Immunol., 2008, 8(11), 837-848.
[http://dx.doi.org/10.1038/nri2423] [PMID: 18927578]
[34]
Jridi, I.; Canté-Barrett, K.; Pike-Overzet, K.; Staal, F.J.T. Inflammation and Wnt signaling: Target for immunomodulatory therapy? Front. Cell Dev. Biol., 2021, 8, 615131.
[http://dx.doi.org/10.3389/fcell.2020.615131] [PMID: 33614624]
[35]
Jia, L.; Piña-Crespo, J.; Li, Y. Restoring Wnt/β-catenin signaling is a promising therapeutic strategy for Alzheimer’s disease. Mol. Brain, 2019, 12(1), 104.
[http://dx.doi.org/10.1186/s13041-019-0525-5] [PMID: 31801553]
[36]
Stamos, J.L.; Weis, W.I. The β-catenin destruction complex. Cold Spring Harb. Perspect. Biol., 2013, 5(1), a007898.
[http://dx.doi.org/10.1101/cshperspect.a007898] [PMID: 23169527]
[37]
Becher, B.; Spath, S.; Goverman, J. Cytokine networks in neuroinflammation. Nat. Rev. Immunol., 2017, 17(1), 49-59.
[http://dx.doi.org/10.1038/nri.2016.123] [PMID: 27916979]
[38]
Ramesh, G.; MacLean, A.G.; Philipp, M.T. Cytokines and chemokines at the crossroads of neuroinflammation, neurodegeneration, and neuropathic pain. Mediators Inflamm., 2013, 2013, 480739.
[http://dx.doi.org/10.1155/2013/480739] [PMID: 23997430]
[39]
Andersen, J.K. Oxidative stress in neurodegeneration: Cause or consequence? Nat. Med., 2004, 10(Suppl.), S18-S25.
[http://dx.doi.org/10.1038/nrn1434] [PMID: 15298006]
[40]
Halliwell, B. Oxidative stress and neurodegeneration: Where are we now? J. Neurochem., 2006, 97(6), 1634-1658.
[http://dx.doi.org/10.1111/j.1471-4159.2006.03907.x] [PMID: 16805774]
[41]
Qin, H.; Niyongere, S.A.; Lee, S.J.; Baker, B.J.; Benveniste, E.N. Expression and functional significance of SOCS-1 and SOCS-3 in astrocytes. J. Immunol., 2008, 181(5), 3167-3176.
[http://dx.doi.org/10.4049/jimmunol.181.5.3167] [PMID: 18713987]
[42]
Lallier, S.W.; Graf, A.E.; Waidyarante, G.R.; Rogers, L.K. Nurr1 expression is modified by inflammation in microglia. Neuroreport, 2016, 27(15), 1120-1127.
[http://dx.doi.org/10.1097/WNR.0000000000000665] [PMID: 27532877]
[43]
Zhang, J-M.; An, J. Cytokines, inflammation, and pain. Int. Anesthesiol. Clin., 2007, 45(2), 27-37.
[http://dx.doi.org/10.1097/AIA.0b013e318034194e] [PMID: 17426506]
[44]
Tiberi, M.; Chiurchiù, V. Specialized pro-resolving lipid mediators and glial cells: Emerging candidates for brain homeostasis and repair. Front. Cell. Neurosci., 2021, 15, 673549.
[http://dx.doi.org/10.3389/fncel.2021.673549] [PMID: 33981203]
[45]
Serhan, C.N.; Chiang, N.; Van Dyke, T.E. Resolving inflammation: Dual anti-inflammatory and pro-resolution lipid mediators. Nat. Rev. Immunol., 2008, 8(5), 349-361.
[http://dx.doi.org/10.1038/nri2294] [PMID: 18437155]
[46]
Guzman-Martinez, L.; Maccioni, R.B.; Andrade, V.; Navarrete, L.P.; Pastor, M.G.; Ramos-Escobar, N. Neuroinflammation as a common feature of neurodegenerative disorders. Front. Pharmacol., 2019, 10, 1008.
[http://dx.doi.org/10.3389/fphar.2019.01008] [PMID: 31572186]
[47]
Akiyama, H. Inflammatory response in Alzheimer’s disease. Tohoku J. Exp. Med., 1994, 174(3), 295-303.
[http://dx.doi.org/10.1620/tjem.174.295] [PMID: 7539162]
[48]
Hardy, J.; Selkoe, D.J. The amyloid hypothesis of Alzheimer’s disease: progress and problems on the road to therapeutics. Science, 2002, 297(5580), 353-356.
[http://dx.doi.org/10.1126/science.1072994] [PMID: 12130773]
[49]
Heppner, F.L.; Ransohoff, R.M.; Becher, B. Immune attack: the role of inflammation in Alzheimer disease. Nat. Rev. Neurosci., 2015, 16(6), 358-372.
[http://dx.doi.org/10.1038/nrn3880] [PMID: 25991443]
[50]
Heneka, M.T.; Carson, M.J.; El Khoury, J.; Landreth, G.E.; Brosseron, F.; Feinstein, D.L.; Jacobs, A.H.; Wyss- Coray, T.; Vitorica, J.; Ransohoff, R.M.; Herrup, K.; Frautschy, S.A.; Finsen, B.; Brown, G.C.; Verkhratsky, A.; Yamanaka, K.; Koistinaho, J.; Latz, E.; Halle, A.; Petzold, G.C.; Town, T.; Morgan, D.; Shinohara, M.L.; Perry, V.H.; Holmes, C.; Bazan, N.G.; Brooks, D.J.; Hunot, S.; Joseph, B.; Deigendesch, N.; Garaschuk, O.; Boddeke, E.; Dinarello, C.A.; Breitner, J.C.; Cole, G.M.; Golenbock, D.T.; Kummer, M.P. Neuroinflammation in Alzheimer’s disease. Lancet Neurol., 2015, 14(4), 388-405.
[http://dx.doi.org/10.1016/S1474-4422(15)70016-5] [PMID: 25792098]
[51]
Jiang, T.; Yu, J.T.; Zhu, X.C.; Tan, L. TREM2 in Alzheimer’s disease. Mol. Neurobiol., 2013, 48(1), 180-185.
[http://dx.doi.org/10.1007/s12035-013-8424-8] [PMID: 23407992]
[52]
Bajramovic, J.J. Regulation of innate immune responses in the central nervous system. CNS Neurol. Disord. Drug Targets, 2011, 10(1), 4-24.
[http://dx.doi.org/10.2174/187152711794488610] [PMID: 21143142]
[53]
Takahashi, K.; Rochford, C.D.P.; Neumann, H. Clearance of apoptotic neurons without inflammation by microglial triggering receptor expressed on myeloid cells-2. J. Exp. Med., 2005, 201(4), 647-657.
[http://dx.doi.org/10.1084/jem.20041611] [PMID: 15728241]
[54]
Jay, T.R.; von Saucken, V.E.; Landreth, G.E. TREM2 in neurodegenerative diseases. Mol. Neurodegener., 2017, 12(1), 56.
[http://dx.doi.org/10.1186/s13024-017-0197-5] [PMID: 28768545]
[55]
Zhou, S.L.; Tan, C.C.; Hou, X.H.; Cao, X.P.; Tan, L.; Yu, J.T. TREM2 variants and neurodegenerative diseases: A systematic review and meta-analysis. J. Alzheimers Dis., 2019, 68(3), 1171-1184.
[http://dx.doi.org/10.3233/JAD-181038] [PMID: 30883352]
[56]
Wang, Q.; Liu, Y.; Zhou, J. Neuroinflammation in Parkinson’s disease and its potential as therapeutic target. Transl. Neurodegener., 2015, 4, 19.
[http://dx.doi.org/10.1186/s40035-015-0042-0] [PMID: 26464797]
[57]
Braak, H.; Del Tredici, K.; Rüb, U.; de Vos, R.A.; Jansen Steur, E.N.; Braak, E. Staging of brain pathology related to sporadic Parkinson’s disease. Neurobiol. Aging, 2003, 24(2), 197-211.
[http://dx.doi.org/10.1016/S0197-4580(02)00065-9] [PMID: 12498954]
[58]
Oksanen, M.; Lehtonen, S.; Jaronen, M.; Goldsteins, G.; Hämäläinen, R.H.; Koistinaho, J. Astrocyte alterations in neurodegenerative pathologies and their modeling in human induced pluripotent stem cell platforms. Cell. Mol. Life Sci., 2019, 76(14), 2739-2760.
[http://dx.doi.org/10.1007/s00018-019-03111-7] [PMID: 31016348]
[59]
Rocha, N.P.; de Miranda, A.S.; Teixeira, A.L. Insights into neuroinflammation in Parkinson’s disease: From biomarkers to anti-inflammatory based therapies. BioMed Res. Int., 2015, 2015, 628192.
[http://dx.doi.org/10.1155/2015/628192] [PMID: 26295044]
[60]
Zhang, W.; Wang, T.; Pei, Z.; Miller, D.S.; Wu, X.; Block, M.L.; Wilson, B.; Zhang, W.; Zhou, Y.; Hong, J.S.; Zhang, J. Aggregated α-synuclein activates microglia: A process leading to disease progression in Parkinson’s disease. FASEB J., 2005, 19(6), 533-542.
[http://dx.doi.org/10.1096/fj.04-2751com] [PMID: 15791003]
[61]
Rojanathammanee, L.; Murphy, E.J.; Combs, C.K. Expression of mutant alpha-synuclein modulates microglial phenotype in vitro. J. Neuroinflammation, 2011, 8, 44.
[http://dx.doi.org/10.1186/1742-2094-8-44] [PMID: 21554732]
[62]
Hirsch, E.C.; Hunot, S. Neuroinflammation in Parkinson’s disease: A target for neuroprotection? Lancet Neurol., 2009, 8(4), 382-397.
[http://dx.doi.org/10.1016/S1474-4422(09)70062-6] [PMID: 19296921]
[63]
W, H Activation of microglia by human neuromelanin is NF-kappaB dependent and involves p38 mitogen-activated protein kinase: Implications for Parkinson’s disease. FASEB J., 2003, 17, 500-502.
[64]
Wang, X-J.; Zhang, S.; Yan, Z.Q.; Zhao, Y.X.; Zhou, H.Y.; Wang, Y.; Lu, G.Q.; Zhang, J.D. Impaired CD200-CD200R-mediated microglia silencing enhances midbrain dopaminergic neurodegeneration: Roles of aging, superoxide, NADPH oxidase, and p38 MAPK. Free Radic. Biol. Med., 2011, 50(9), 1094-1106.
[http://dx.doi.org/10.1016/j.freeradbiomed.2011.01.032] [PMID: 21295135]
[65]
Sheridan, G.K.; Murphy, K.J. Neuron-glia crosstalk in health and disease: Fractalkine and CX3CR1 take centre stage. Open Biol., 2013, 3(12), 130181.
[http://dx.doi.org/10.1098/rsob.130181] [PMID: 24352739]
[66]
Decressac, M.; Volakakis, N.; Björklund, A.; Perlmann, T. NURR1 in Parkinson disease-from pathogenesis to therapeutic potential. Nat. Rev. Neurol., 2013, 9(11), 629-636.
[http://dx.doi.org/10.1038/nrneurol.2013.209] [PMID: 24126627]
[67]
Rowland, L.P.; Shneider, N.A. Amyotrophic lateral sclerosis. N. Engl. J. Med., 2001, 344(22), 1688-1700.
[http://dx.doi.org/10.1056/NEJM200105313442207] [PMID: 11386269]
[68]
Kiernan, M.C.; Vucic, S.; Cheah, B.C.; Turner, M.R.; Eisen, A.; Hardiman, O.; Burrell, J.R.; Zoing, M.C. Amyotrophic lateral sclerosis. Lancet, 2011, 377(9769), 942-955.
[http://dx.doi.org/10.1016/S0140-6736(10)61156-7] [PMID: 21296405]
[69]
N, M Ubiquitinated TDP-43 in frontotemporal lobar degeneration and amyotrophic lateral sclerosis. Science, 2006, 314, 130-133.
[70]
Van Deerlin, V.M.; Leverenz, J.B.; Bekris, L.M.; Bird, T.D.; Yuan, W.; Elman, L.B.; Clay, D.; Wood, E.M.; Chen-Plotkin, A.S.; Martinez-Lage, M.; Steinbart, E.; McCluskey, L.; Grossman, M.; Neumann, M.; Wu, I.L.; Yang, W.S.; Kalb, R.; Galasko, D.R.; Montine, T.J.; Trojanowski, J.Q.; Lee, V.M.; Schellenberg, G.D.; Yu, C.E. TARDBP mutations in amyotrophic lateral sclerosis with TDP-43 neuropathology: A genetic and histopathological analysis. Lancet Neurol., 2008, 7(5), 409-416.
[http://dx.doi.org/10.1016/S1474-4422(08)70071-1] [PMID: 18396105]
[71]
Liu, J.; Wang, F. Role of neuroinflammation in amyotrophic lateral sclerosis: Cellular mechanisms and therapeutic implications. Front. Immunol., 2017, 8, 1005.
[72]
Philips, T.; Robberecht, W. Neuroinflammation in amyotrophic lateral sclerosis: Role of glial activation in motor neuron disease. Lancet Neurol., 2011, 10(3), 253-263.
[http://dx.doi.org/10.1016/S1474-4422(11)70015-1] [PMID: 21349440]
[73]
Liu, Y.; Hao, W.; Dawson, A.; Liu, S.; Fassbender, K. Expression of amyotrophic lateral sclerosis-linked SOD1 mutant increases the neurotoxic potential of microglia via TLR2. J. Biol. Chem., 2009, 284(6), 3691-3699.
[http://dx.doi.org/10.1074/jbc.M804446200] [PMID: 19091752]
[74]
Neymotin, A.; Petri, S.; Calingasan, N.Y.; Wille, E.; Schafer, P.; Stewart, C.; Hensley, K.; Beal, M.F.; Kiaei, M. Lenalidomide (Revlimid) administration at symptom onset is neuroprotective in a mouse model of amyotrophic lateral sclerosis. Exp. Neurol., 2009, 220(1), 191-197.
[http://dx.doi.org/10.1016/j.expneurol.2009.08.028] [PMID: 19733563]
[75]
Barbeito, L.H.; Pehar, M.; Cassina, P.; Vargas, M.R.; Peluffo, H.; Viera, L.; Estévez, A.G.; Beckman, J.S. A role for astrocytes in motor neuron loss in amyotrophic lateral sclerosis. Brain Res. Brain Res. Rev., 2004, 47(1-3), 263-274.
[http://dx.doi.org/10.1016/j.brainresrev.2004.05.003] [PMID: 15572176]
[76]
Chiu, I.M.; Chen, A.; Zheng, Y.; Kosaras, B.; Tsiftsoglou, S.A.; Vartanian, T.K.; Brown, R.H., Jr; Carroll, M.C. T lymphocytes potentiate endogenous neuroprotective inflammation in a mouse model of ALS. Proc. Natl. Acad. Sci. USA, 2008, 105(46), 17913-17918.
[http://dx.doi.org/10.1073/pnas.0804610105] [PMID: 18997009]
[77]
The Huntington’s Disease Collaborative Research Group. A novel gene containing a trinucleotide repeat that is expanded and unstable on Huntington’s disease chromosomes. Cell, 1993, 72(6), 971-983.
[http://dx.doi.org/10.1016/0092-8674(93)90585-E] [PMID: 8458085]
[78]
Ross, C.A.; Tabrizi, S.J. Huntington’s disease: from molecular pathogenesis to clinical treatment. Lancet Neurol., 2011, 10(1), 83-98.
[http://dx.doi.org/10.1016/S1474-4422(10)70245-3] [PMID: 21163446]
[79]
Browne, S.E. Mitochondria and Huntington’s disease pathogenesis: insight from genetic and chemical models. Ann. N. Y. Acad. Sci., 2008, 1147, 358-382.
[http://dx.doi.org/10.1196/annals.1427.018] [PMID: 19076457]
[80]
Schilling, G.; Klevytska, A.; Tebbenkamp, A.T.; Juenemann, K.; Cooper, J.; Gonzales, V.; Slunt, H.; Poirer, M.; Ross, C.A.; Borchelt, D.R. Characterization of huntingtin pathologic fragments in human Huntington disease, transgenic mice, and cell models. J. Neuropathol. Exp. Neurol., 2007, 66(4), 313-320.
[http://dx.doi.org/10.1097/nen.0b013e318040b2c8] [PMID: 17413322]
[81]
Ross, C.A.; Poirier, M.A. Opinion: What is the role of protein aggregation in neurodegeneration? Nat. Rev. Mol. Cell Biol., 2005, 6(11), 891-898.
[http://dx.doi.org/10.1038/nrm1742] [PMID: 16167052]
[82]
Truant, R.; Atwal, R.S.; Desmond, C.; Munsie, L.; Tran, T. Huntington’s disease: Revisiting the aggregation hypothesis in polyglutamine neurodegenerative diseases. FEBS J., 2008, 275(17), 4252-4262.
[http://dx.doi.org/10.1111/j.1742-4658.2008.06561.x] [PMID: 18637947]
[83]
Tabrizi, S.J.; Langbehn, D.R.; Leavitt, B.R.; Roos, R.A.; Durr, A.; Craufurd, D.; Kennard, C.; Hicks, S.L.; Fox, N.C.; Scahill, R.I.; Borowsky, B.; Tobin, A.J.; Rosas, H.D.; Johnson, H.; Reilmann, R.; Landwehrmeyer, B.; Stout, J.C. TRACK-HD investigators. Biological and clinical manifestations of Huntington’s disease in the longitudinal TRACK-HD study: Cross-sectional analysis of baseline data. Lancet Neurol., 2009, 8(9), 791-801.
[http://dx.doi.org/10.1016/S1474-4422(09)70170-X] [PMID: 19646924]
[84]
Walker, F.O. Huntington’s disease. Lancet, 2007, 369(9557), 218-228.
[http://dx.doi.org/10.1016/S0140-6736(07)60111-1] [PMID: 17240289]
[85]
Hodges, A.; Strand, A.D.; Aragaki, A.K.; Kuhn, A.; Sengstag, T.; Hughes, G.; Elliston, L.A.; Hartog, C.; Goldstein, D.R.; Thu, D.; Hollingsworth, Z.R.; Collin, F.; Synek, B.; Holmans, P.A.; Young, A.B.; Wexler, N.S.; Delorenzi, M.; Kooperberg, C.; Augood, S.J.; Faull, R.L.; Olson, J.M.; Jones, L.; Luthi-Carter, R. Regional and cellular gene expression changes in human Huntington’s disease brain. Hum. Mol. Genet., 2006, 15(6), 965-977.
[http://dx.doi.org/10.1093/hmg/ddl013] [PMID: 16467349]
[86]
Tai, Y.F.; Pavese, N.; Gerhard, A.; Tabrizi, S.J.; Barker, R.A.; Brooks, D.J.; Piccini, P. Microglial activation in presymptomatic Huntington’s disease gene carriers. Brain, 2007, 130(Pt 7), 1759-1766.
[http://dx.doi.org/10.1093/brain/awm044] [PMID: 17400599]
[87]
Hersch, S.M.; Gevorkian, S.; Marder, K.; Moskowitz, C.; Feigin, A.; Cox, M.; Como, P.; Zimmerman, C.; Lin, M.; Zhang, L.; Ulug, A.M.; Beal, M.F.; Matson, W.; Bogdanov, M.; Ebbel, E.; Zaleta, A.; Kaneko, Y.; Jenkins, B.; Hevelone, N.; Zhang, H.; Yu, H.; Schoenfeld, D.; Ferrante, R.; Rosas, H.D. Creatine in Huntington disease is safe, tolerable, bioavailable in brain and reduces serum 8OH2'dG. Neurology, 2006, 66(2), 250-252.
[http://dx.doi.org/10.1212/01.wnl.0000194318.74946.b6] [PMID: 16434666]
[88]
Giorgini, F.; Guidetti, P.; Nguyen, Q.; Bennett, S.C.; Muchowski, P.J. A genomic screen in yeast implicates kynurenine 3-monooxygenase as a therapeutic target for Huntington disease. Nat. Genet., 2005, 37(5), 526-531.
[http://dx.doi.org/10.1038/ng1542] [PMID: 15806102]
[89]
Bosch, M.E.; Kielian, T. Neuroinflammatory paradigms in lysosomal storage diseases. Front. Neurosci., 2015, 9, 417.
[http://dx.doi.org/10.3389/fnins.2015.00417] [PMID: 26578874]
[90]
Archer, L.D.; Langford-Smith, K.J.; Bigger, B.W.; Fildes, J.E. Mucopolysaccharide diseases: a complex interplay between neuroinflammation, microglial activation and adaptive immunity. J. Inherit. Metab. Dis., 2014, 37(1), 1-12.
[http://dx.doi.org/10.1007/s10545-013-9613-3] [PMID: 23653226]
[91]
Futerman, A.H.; van Meer, G. The cell biology of lysosomal storage disorders. Nat. Rev. Mol. Cell Biol., 2004, 5(7), 554-565.
[http://dx.doi.org/10.1038/nrm1423] [PMID: 15232573]
[92]
Giugliani, R.; Federhen, A.; Rojas, M.V.; Vieira, T.; Artigalás, O.; Pinto, L.L.; Azevedo, A.C.; Acosta, A.; Bonfim, C.; Lourenço, C.M.; Kim, C.A.; Horovitz, D.; Bonfim, D.; Norato, D.; Marinho, D.; Palhares, D.; Santos, E.S.; Ribeiro, E.; Valadares, E.; Guarany, F.; de Lucca, G.R.; Pimentel, H.; de Souza, I.N.; Correa, J., Sr; Fraga, J.C.; Goes, J.E.; Cabral, J.M.; Simionato, J.; Llerena, J., Jr; Jardim, L.; Giuliani, L.; da Silva, L.C.; Santos, M.L.; Moreira, M.A.; Kerstenetzky, M.; Ribeiro, M.; Ruas, N.; Barrios, P.; Aranda, P.; Honjo, R.; Boy, R.; Costa, R.; Souza, C.; Alcantara, F.F.; Avilla, S.G.; Fagondes, S.; Martins, A.M. Mucopolysaccharidosis I, II, and VI: Brief review and guidelines for treatment. Genet. Mol. Biol., 2010, 33(4), 589-604.
[http://dx.doi.org/10.1590/S1415-47572010005000093] [PMID: 21637564]
[93]
Suarez-Guerrero, J.L.; Gómez Higuera, P.J.I.; Arias Flórez, J.S.; Contreras-García, G.A. Mucopolysaccharidosis: Clinical features, diagnosis and management. Rev. Chil. Pediatr., 2016, 87(4), 295-304.
[http://dx.doi.org/10.1016/j.rchipe.2015.10.004] [PMID: 26613630]
[94]
Valstar, M.J.; Ruijter, G.J.G.; van Diggelen, O.P.; Poorthuis, B.J.; Wijburg, F.A. Sanfilippo syndrome: a mini-review. J. Inherit. Metab. Dis., 2008, 31(2), 240-252.
[http://dx.doi.org/10.1007/s10545-008-0838-5] [PMID: 18392742]
[95]
DiRosario, J.; Divers, E.; Wang, C.; Etter, J.; Charrier, A.; Jukkola, P.; Auer, H.; Best, V.; Newsom, D.L.; McCarty, D.M.; Fu, H. Innate and adaptive immune activation in the brain of MPS IIIB mouse model. J. Neurosci. Res., 2009, 87(4), 978-990.
[http://dx.doi.org/10.1002/jnr.21912] [PMID: 18951493]
[96]
Villani, G.R.D.; Gargiulo, N.; Faraonio, R.; Castaldo, S.; Gonzalez Y Reyero, E.; Di Natale, P. Cytokines, neurotrophins, and oxidative stress in brain disease from mucopolysaccharidosis IIIB. J. Neurosci. Res., 2007, 85(3), 612-622.
[http://dx.doi.org/10.1002/jnr.21134] [PMID: 17139681]
[97]
Arfi, A.; Richard, M.; Gandolphe, C.; Bonnefont-Rousselot, D.; Thérond, P.; Scherman, D. Neuroinflammatory and oxidative stress phenomena in MPS IIIA mouse model: the positive effect of long-term aspirin treatment. Mol. Genet. Metab., 2011, 103(1), 18-25.
[http://dx.doi.org/10.1016/j.ymgme.2011.01.015] [PMID: 21353610]
[98]
Kollmann, K.; Uusi-Rauva, K.; Scifo, E.; Tyynelä, J.; Jalanko, A.; Braulke, T. Cell biology and function of neuronal ceroid lipofuscinosis-related proteins. Biochim. Biophys. Acta, 2013, 1832(11), 1866-1881.
[http://dx.doi.org/10.1016/j.bbadis.2013.01.019] [PMID: 23402926]
[99]
Haltia, M. The neuronal ceroid-lipofuscinoses. J. Neuropathol. Exp. Neurol., 2003, 62(1), 1-13.
[http://dx.doi.org/10.1093/jnen/62.1.1] [PMID: 12528813]
[100]
Mole, S.E.; Williams, R.E.; Goebel, H.H. Correlations between genotype, ultrastructural morphology and clinical phenotype in the neuronal ceroid lipofuscinoses. Neurogenetics, 2005, 6(3), 107-126.
[http://dx.doi.org/10.1007/s10048-005-0218-3] [PMID: 15965709]
[101]
Anderson, G.W.; Goebel, H.H.; Simonati, A. Human pathology in NCL. Biochim. Biophys. Acta, 2013, 1832(11), 1807-1826.
[http://dx.doi.org/10.1016/j.bbadis.2012.11.014] [PMID: 23200925]
[102]
Dolisca, S-B.; Mehta, M.; Pearce, D.A.; Mink, J.W.; Maria, B.L. Batten disease: clinical aspects, molecular mechanisms, translational science, and future directions. J. Child Neurol., 2013, 28(9), 1074-1100.
[http://dx.doi.org/10.1177/0883073813493665] [PMID: 23838031]
[103]
Mencarelli, C.; Martinez-Martinez, P. Ceramide function in the brain: when a slight tilt is enough. Cell. Mol. Life Sci., 2013, 70(2), 181-203.
[http://dx.doi.org/10.1007/s00018-012-1038-x] [PMID: 22729185]
[104]
Macauley, S.L.; Roberts, M.S.; Wong, A.M.; McSloy, F.; Reddy, A.S.; Cooper, J.D.; Sands, M.S. Synergistic effects of central nervous system-directed gene therapy and bone marrow transplantation in the murine model of infantile neuronal ceroid lipofuscinosis. Ann. Neurol., 2012, 71(6), 797-804.
[http://dx.doi.org/10.1002/ana.23545] [PMID: 22368049]
[105]
Butters, T.D. Gaucher disease. Curr. Opin. Chem. Biol., 2007, 11(4), 412-418.
[http://dx.doi.org/10.1016/j.cbpa.2007.05.035] [PMID: 17644022]
[106]
Stirnemann, J.; Belmatoug, N.; Camou, F.; Serratrice, C.; Froissart, R.; Caillaud, C.; Levade, T.; Astudillo, L.; Serratrice, J.; Brassier, A.; Rose, C.; Billette de Villemeur, T.; Berger, M.G. A review of gaucher disease pathophysiology, clinical presentation and treatments. Int. J. Mol. Sci., 2017, 18(2), 441.
[http://dx.doi.org/10.3390/ijms18020441] [PMID: 28218669]
[107]
Nagral, A. Gaucher disease. J. Clin. Exp. Hepatol., 2014, 4(1), 37-50.
[http://dx.doi.org/10.1016/j.jceh.2014.02.005] [PMID: 25755533]
[108]
Nalysnyk, L.; Rotella, P.; Simeone, J.C.; Hamed, A.; Weinreb, N. Gaucher disease epidemiology and natural history: A comprehensive review of the literature. Hematology, 2017, 22(2), 65-73.
[http://dx.doi.org/10.1080/10245332.2016.1240391] [PMID: 27762169]
[109]
Sama, D.M.; Norris, C.M. Calcium dysregulation and neuroinflammation: discrete and integrated mechanisms for age-related synaptic dysfunction. Ageing Res. Rev., 2013, 12(4), 982-995.
[http://dx.doi.org/10.1016/j.arr.2013.05.008] [PMID: 23751484]
[110]
Alobaidy, H. Recent advances in the diagnosis and treatment of niemann-pick disease type C in children: A guide to early diagnosis for the general pediatrician. Int. J. Pediatr., 2015, 2015, 816593.
[http://dx.doi.org/10.1155/2015/816593] [PMID: 25784942]
[111]
Baudry, M.; Yao, Y.; Simmons, D.; Liu, J.; Bi, X. Postnatal development of inflammation in a murine model of Niemann-Pick type C disease: Immunohistochemical observations of microglia and astroglia. Exp. Neurol., 2003, 184(2), 887-903.
[http://dx.doi.org/10.1016/S0014-4886(03)00345-5] [PMID: 14769381]
[112]
Gallala, H.D.; Breiden, B.; Sandhoff, K. Regulation of the NPC2 protein-mediated cholesterol trafficking by membrane lipids. J. Neurochem., 2011, 116(5), 702-707.
[http://dx.doi.org/10.1111/j.1471-4159.2010.07014.x] [PMID: 21214551]
[113]
Rosenbaum, A.I.; Maxfield, F.R. Niemann-Pick type C disease: Molecular mechanisms and potential therapeutic approaches. J. Neurochem., 2011, 116(5), 789-795.
[http://dx.doi.org/10.1111/j.1471-4159.2010.06976.x] [PMID: 20807315]
[114]
Patterson, M. Niemann-Pick Disease Type C. In: GeneReviews; Adam, M.P.; Ardinger, H.H.; Pagon, R.A., Eds.; University of Washington: Seattle, USA, 2020.
[115]
Vanier, M.T. Niemann-Pick disease type C. Orphanet J. Rare Dis., 2010, 5, 16.
[http://dx.doi.org/10.1186/1750-1172-5-16] [PMID: 20525256]
[116]
Wang, M.L.; Motamed, M.; Infante, R.E.; Abi-Mosleh, L.; Kwon, H.J.; Brown, M.S.; Goldstein, J.L. Identification of surface residues on Niemann-Pick C2 essential for hydrophobic handoff of cholesterol to NPC1 in lysosomes. Cell Metab., 2010, 12(2), 166-173.
[http://dx.doi.org/10.1016/j.cmet.2010.05.016] [PMID: 20674861]
[117]
Peake, K.B.; Campenot, R.B.; Vance, D.E.; Vance, J.E. Niemann-Pick Type C1 deficiency in microglia does not cause neuron death in vitro. Biochim. Biophys. Acta, 2011, 1812(9), 1121-1129.
[http://dx.doi.org/10.1016/j.bbadis.2011.06.003] [PMID: 21704157]
[118]
Smith, D.; Wallom, K-L.; Williams, I.M.; Jeyakumar, M.; Platt, F.M. Beneficial effects of anti-inflammatory therapy in a mouse model of Niemann-Pick disease type C1. Neurobiol. Dis., 2009, 36(2), 242-251.
[http://dx.doi.org/10.1016/j.nbd.2009.07.010] [PMID: 19632328]
[119]
Williams, I.M.; Wallom, K.L.; Smith, D.A.; Al Eisa, N.; Smith, C.; Platt, F.M. Improved neuroprotection using miglustat, curcumin and ibuprofen as a triple combination therapy in Niemann-Pick disease type C1 mice. Neurobiol. Dis., 2014, 67, 9-17.
[http://dx.doi.org/10.1016/j.nbd.2014.03.001] [PMID: 24631719]
[120]
Gitler, A.D.; Dhillon, P.; Shorter, J. Neurodegenerative disease: Models, mechanisms, and a new hope. Dis. Model. Mech., 2017, 10(5), 499-502.
[http://dx.doi.org/10.1242/dmm.030205] [PMID: 28468935]
[121]
Leicht, H.; König, H.H.; Stuhldreher, N.; Bachmann, C.; Bickel, H.; Fuchs, A.; Heser, K.; Jessen, F.; Köhler, M.; Luppa, M.; Mösch, E.; Pentzek, M.; Riedel-Heller, S.; Scherer, M.; Werle, J.; Weyerer, S.; Wiese, B.; Maier, W. AgeCoDe study group. Predictors of costs in dementia in a longitudinal perspective. PLoS One, 2013, 8(7), e70018.
[http://dx.doi.org/10.1371/journal.pone.0070018] [PMID: 23875017]
[122]
Durães, F.; Pinto, M.; Sousa, E. Old drugs as new treatments for neurodegenerative diseases. Pharmaceuticals (Basel), 2018, 11(2), 44.
[http://dx.doi.org/10.3390/ph11020044] [PMID: 29751602]
[123]
Gurung, A.B.; Ali, M.A.; Lee, J.; Farah, M.A.; Al-Anazi, K.M. An updated review of computer-aided drug design and its application to COVID-19. BioMed Res. Int., 2021, 2021, 8853056.
[http://dx.doi.org/10.1155/2021/8853056] [PMID: 34258282]
[124]
Yu, W.; MacKerell, A.D., Jr. Computer-aided drug design methods. Methods, 2017, 1520, 85-106.
[http://dx.doi.org/10.1007/978-1-4939-6634-9_5] [PMID: 27873247]
[125]
Lionta, E.; Spyrou, G.; Vassilatis, D.K.; Cournia, Z. Structure-based virtual screening for drug discovery: Principles, applications and recent advances. Curr. Top. Med. Chem., 2014, 14(16), 1923-1938.
[http://dx.doi.org/10.2174/1568026614666140929124445] [PMID: 25262799]
[126]
Krishnan, V.; Rupp, B. Macromolecular structure determination: Comparison of X-ray crystallography and NMR spectroscopy. In: eLS; John Wiley & Sons Ltd.: NJ, USA, 2012.
[http://dx.doi.org/10.1002/9780470015902.a0002716.pub2]
[127]
Natchiar, S.K.; Myasnikov, A.G.; Kratzat, H.; Hazemann, I.; Klaholz, B.P. Visualization of chemical modifications in the human 80S ribosome structure. Nature, 2017, 551(7681), 472-477.
[http://dx.doi.org/10.1038/nature24482] [PMID: 29143818]
[128]
Benjin, X.; Ling, L. Developments, applications, and prospects of cryo-electron microscopy. Protein Sci., 2020, 29(4), 872-882.
[http://dx.doi.org/10.1002/pro.3805] [PMID: 31854478]
[129]
Aparoy, P.; Reddy, K.K.; Reddanna, P. Structure and ligand based drug design strategies in the development of novel 5- LOX inhibitors. Curr. Med. Chem., 2012, 19(22), 3763-3778.
[http://dx.doi.org/10.2174/092986712801661112] [PMID: 22680930]
[130]
Vázquez, J.; López, M.; Gibert, E.; Herrero, E.; Luque, F.J. Merging ligand-based and structure-based methods in drug discovery: An overview of combined virtual screening approaches. Molecules, 2020, 25(20), 4723.
[http://dx.doi.org/10.3390/molecules25204723] [PMID: 33076254]
[131]
Shim, J.; Mackerell, A.D., Jr Computational ligand-based rational design: Role of conformational sampling and force fields in model development. MedChemComm, 2011, 2(5), 356-370.
[http://dx.doi.org/10.1039/c1md00044f] [PMID: 21716805]
[132]
Lavecchia, A. Machine-learning approaches in drug discovery: Methods and applications. Drug Discov. Today, 2015, 20(3), 318-331.
[http://dx.doi.org/10.1016/j.drudis.2014.10.012] [PMID: 25448759]
[133]
Alexander-Brett, J.M.; Fremont, D.H. Dual GPCR and GAG mimicry by the M3 chemokine decoy receptor. J. Exp. Med., 2007, 204(13), 3157-3172.
[http://dx.doi.org/10.1084/jem.20071677] [PMID: 18070938]
[134]
Toledo-Sherman, L.; Breccia, P.; Cachope, R.; Bate, J.R.; Angulo-Herrera, I.; Wishart, G.; Matthews, K.L.; Martin, S.L.; Cox, H.C.; McAllister, G.; Penrose, S.D.; Vater, H.; Esmieu, W.; Van de Poël, A.; Van de Bospoort, R.; Strijbosch, A.; Lamers, M.; Leonard, P.; Jarvis, R.E.; Blackaby, W.; Barnes, K.; Eznarriaga, M.; Dowler, S.; Smith, G.D.; Fischer, D.F.; Lazari, O.; Yates, D.; Rose, M.; Jang, S.W.; Muñoz-Sanjuan, I.; Dominguez, C. Optimization of potent and selective ataxia telangiectasia-mutated inhibitors suitable for a proof-of-concept study in huntington’s disease models. J. Med. Chem., 2019, 62(6), 2988-3008.
[http://dx.doi.org/10.1021/acs.jmedchem.8b01819] [PMID: 30840447]
[135]
Long, A.; Zhao, H.; Huang, X. Structural basis for the interaction between casein kinase 1 delta and a potent and selective inhibitor. J. Med. Chem., 2012, 55(2), 956-960.
[http://dx.doi.org/10.1021/jm201387s] [PMID: 22168824]
[136]
Elkins, P.A.; Ho, Y.S.; Smith, W.W.; Janson, C.A.; D’Alessio, K.J.; McQueney, M.S.; Cummings, M.D.; Romanic, A.M. Structure of the C-terminally truncated human ProMMP9, a gelatin-binding matrix metalloproteinase. Acta Crystallogr. D Biol. Crystallogr., 2002, 58(Pt 7), 1182-1192.
[http://dx.doi.org/10.1107/S0907444902007849] [PMID: 12077439]
[137]
Sastry, G.M.; Adzhigirey, M.; Day, T.; Annabhimoju, R.; Sherman, W. Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J. Comput. Aided Mol. Des., 2013, 27(3), 221-234.
[http://dx.doi.org/10.1007/s10822-013-9644-8] [PMID: 23579614]
[138]
Cavasotto, C.N.; Phatak, S.S. Homology modeling in drug discovery: Current trends and applications. Drug Discov. Today, 2009, 14(13-14), 676-683.
[http://dx.doi.org/10.1016/j.drudis.2009.04.006] [PMID: 19422931]
[139]
Xiang, Z. Advances in homology protein structure modeling. Curr. Protein Pept. Sci., 2006, 7(3), 217-227.
[http://dx.doi.org/10.2174/138920306777452312] [PMID: 16787261]
[140]
Bender, B.J.; Marlow, B.; Meiler, J. Improving homology modeling from low-sequence identity templates in Rosetta: A case study in GPCRs. PLOS Comput. Biol., 2020, 16(10), e1007597.
[http://dx.doi.org/10.1371/journal.pcbi.1007597] [PMID: 33112852]
[141]
Šali, A.; Blundell, T.L. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol., 1993, 234(3), 779-815.
[http://dx.doi.org/10.1006/jmbi.1993.1626] [PMID: 8254673]
[142]
Jacobson, M. P. A hierarchical approach to all-atom protein loop prediction. Proteins Struct. Funct. Bioinforma., 2004, 55, 351-367.
[143]
Chemical Computing Group. Molecular Operating Environment (MOE). 2021. Available from: https://www.chemcomp.com/Research-Citing_MOE.htm
[144]
Waterhouse, A.; Bertoni, M.; Bienert, S.; Studer, G.; Tauriello, G.; Gumienny, R.; Heer, F.T.; de Beer, T.A.P.; Rempfer, C.; Bordoli, L.; Lepore, R.; Schwede, T. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res., 2018, 46(W1), W296-W303.
[http://dx.doi.org/10.1093/nar/gky427] [PMID: 29788355]
[145]
Muhammed, M.T.; Aki-Yalcin, E. Homology modeling in drug discovery: Overview, current applications, and future perspectives. Chem. Biol. Drug Des., 2019, 93(1), 12-20.
[http://dx.doi.org/10.1111/cbdd.13388] [PMID: 30187647]
[146]
Service, R. The game has changed. AI triumphs at solving protein structures. Science (80-. ), 2020.
[http://dx.doi.org/10.1126/science.abf9367]
[147]
Jumper, J.; Evans, R.; Pritzel, A.; Green, T.; Figurnov, M.; Ronneberger, O.; Tunyasuvunakool, K.; Bates, R.; Žídek, A.; Potapenko, A.; Bridgland, A.; Meyer, C.; Kohl, S.A.A.; Ballard, A.J.; Cowie, A.; Romera-Paredes, B.; Nikolov, S.; Jain, R.; Adler, J.; Back, T.; Petersen, S.; Reiman, D.; Clancy, E.; Zielinski, M.; Steinegger, M.; Pacholska, M.; Berghammer, T.; Bodenstein, S.; Silver, D.; Vinyals, O.; Senior, A.W.; Kavukcuoglu, K.; Kohli, P.; Hassabis, D. Highly accurate protein structure prediction with AlphaFold. Nature, 2021, 596(7873), 583-589.
[http://dx.doi.org/10.1038/s41586-021-03819-2] [PMID: 34265844]
[148]
Callaway, E. ‘It will change everything’: DeepMind’s AI makes gigantic leap in solving protein structures. Nature, 2020, 588(7837), 203-204.
[http://dx.doi.org/10.1038/d41586-020-03348-4] [PMID: 33257889]
[149]
Senior, A.W.; Evans, R.; Jumper, J.; Kirkpatrick, J.; Sifre, L.; Green, T.; Qin, C.; Žídek, A.; Nelson, A.W.R.; Bridgland, A.; Penedones, H.; Petersen, S.; Simonyan, K.; Crossan, S.; Kohli, P.; Jones, D.T.; Silver, D.; Kavukcuoglu, K.; Hassabis, D. Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13). Proteins, 2019, 87(12), 1141-1148.
[http://dx.doi.org/10.1002/prot.25834] [PMID: 31602685]
[150]
Kryshtafovych, A.; Moult, J.; Billings, W.M.; Della Corte, D.; Fidelis, K.; Kwon, S.; Olechnovič, K.; Seok, C.; Venclovas, Č.; Won, J. CASP-COVID participants. Modeling SARS-CoV-2 proteins in the CASP-commons experiment. Proteins, 2021, 89(12), 1987-1996.
[http://dx.doi.org/10.1002/prot.26231] [PMID: 34462960]
[151]
Mullard, A. What does AlphaFold mean for drug discovery? Nat. Rev. Drug Discov., 2021, 20(10), 725-727.
[http://dx.doi.org/10.1038/d41573-021-00161-0] [PMID: 34522032]
[152]
Fan, H.; Mark, A.E. Refinement of homology-based protein structures by molecular dynamics simulation techniques. Protein Sci., 2004, 13(1), 211-220.
[http://dx.doi.org/10.1110/ps.03381404] [PMID: 14691236]
[153]
Bhargavi, M.; Sivan, S.K.; Potlapally, S.R. Identification of novel anti cancer agents by applying in silico methods for inhibition of TSPO protein. Comput. Biol. Chem., 2017, 68, 43-55.
[http://dx.doi.org/10.1016/j.compbiolchem.2016.12.016] [PMID: 28235666]
[154]
Lai, H.T.T.; Nguyen, T.T. Construction of dimeric hTSPO protein model using homology modeling and molecular dynamics. J. Phys. Conf. Ser., 2021, 1932
[http://dx.doi.org/10.1088/1742-6596/1932/1/012016]
[155]
Leelananda, S.P.; Lindert, S. Computational methods in drug discovery. Beilstein J. Org. Chem., 2016, 12, 2694-2718.
[http://dx.doi.org/10.3762/bjoc.12.267] [PMID: 28144341]
[156]
Wang, G.; Zhu, W. Molecular docking for drug discovery and development: a widely used approach but far from perfect. Future Med. Chem., 2016, 8(14), 1707-1710.
[http://dx.doi.org/10.4155/fmc-2016-0143] [PMID: 27578269]
[157]
Halperin, I.; Ma, B.; Wolfson, H.; Nussinov, R. Principles of docking: An overview of search algorithms and a guide to scoring functions. Proteins, 2002, 47(4), 409-443.
[http://dx.doi.org/10.1002/prot.10115] [PMID: 12001221]
[158]
Jones, G.; Willett, P.; Glen, R.C.; Leach, A.R.; Taylor, R. Development and validation of a genetic algorithm for flexible docking. J. Mol. Biol., 2002, 267, 727-748.
[159]
Morris, G.M.; Huey, R.; Lindstrom, W.; Sanner, M.F.; Belew, R.K.; Goodsell, D.S.; Olson, A.J. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J. Comput. Chem., 2009, 30(16), 2785-2791.
[http://dx.doi.org/10.1002/jcc.21256] [PMID: 19399780]
[160]
Friesner, R.A.; Banks, J.L.; Murphy, R.B.; Halgren, T.A.; Klicic, J.J.; Mainz, D.T.; Repasky, M.P.; Knoll, E.H.; Shelley, M.; Perry, J.K.; Shaw, D.E.; Francis, P.; Shenkin, P.S. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J. Med. Chem., 2004, 47(7), 1739-1749.
[http://dx.doi.org/10.1021/jm0306430] [PMID: 15027865]
[161]
Korb, O.; Stützle, T.; Exner, T.E. PLANTS application of ant colony optimization to structure-based. ANTS. Lecture Notes in Computer Science, 2006, 4150
[http://dx.doi.org/10.1007/11839088_22]
[162]
Gagnon, J.K.; Law, S.M.; Brooks, C.L., III Flexible CDOCKER: Development and application of a pseudo-explicit structure-based docking method within CHARMM. J. Comput. Chem., 2016, 37(8), 753-762.
[http://dx.doi.org/10.1002/jcc.24259] [PMID: 26691274]
[163]
Cross, J.B.; Thompson, D.C.; Rai, B.K.; Baber, J.C.; Fan, K.Y.; Hu, Y.; Humblet, C. Comparison of several molecular docking programs: Pose prediction and virtual screening accuracy. J. Chem. Inf. Model., 2009, 49(6), 1455-1474.
[http://dx.doi.org/10.1021/ci900056c] [PMID: 19476350]
[164]
Wang, Z.; Sun, H.; Yao, X.; Li, D.; Xu, L.; Li, Y.; Tian, S.; Hou, T. Comprehensive evaluation of ten docking programs on a diverse set of protein-ligand complexes: the prediction accuracy of sampling power and scoring power. Phys. Chem. Chem. Phys., 2016, 18(18), 12964-12975.
[http://dx.doi.org/10.1039/C6CP01555G] [PMID: 27108770]
[165]
Lapillo, M.; Tuccinardi, T.; Martinelli, A.; Macchia, M.; Giordano, A.; Poli, G. Extensive reliability evaluation of docking-based target-fishing strategies. Int. J. Mol. Sci., 2019, 20(5), 1023.
[http://dx.doi.org/10.3390/ijms20051023] [PMID: 30818741]
[166]
Cuzzolin, A.; Sturlese, M.; Malvacio, I.; Ciancetta, A.; Moro, S. DockBench: An integrated informatic platform bridging the gap between the robust validation of docking protocols and virtual screening simulations. Molecules, 2015, 20(6), 9977-9993.
[http://dx.doi.org/10.3390/molecules20069977] [PMID: 26035098]
[167]
Warren, G.L.; Andrews, C.W.; Capelli, A.M.; Clarke, B.; LaLonde, J.; Lambert, M.H.; Lindvall, M.; Nevins, N.; Semus, S.F.; Senger, S.; Tedesco, G.; Wall, I.D.; Woolven, J.M.; Peishoff, C.E.; Head, M.S. A critical assessment of docking programs and scoring functions. J. Med. Chem., 2006, 49(20), 5912-5931.
[http://dx.doi.org/10.1021/jm050362n] [PMID: 17004707]
[168]
Peach, M.L.; Nicklaus, M.C. Combining docking with pharmacophore filtering for improved virtual screening. J. Cheminform., 2009, 1(1), 6.
[http://dx.doi.org/10.1186/1758-2946-1-6] [PMID: 20298524]
[169]
Dixon, S.L.; Smondyrev, A.M.; Rao, S.N. PHASE: A novel approach to pharmacophore modeling and 3D database searching. Chem. Biol. Drug Des., 2006, 67, 370-372.
[170]
Rastelli, G.; Pinzi, L. Refinement and rescoring of virtual screening results. Front. Chem., 2019, 7, 498.
[http://dx.doi.org/10.3389/fchem.2019.00498] [PMID: 31355188]
[171]
Meng, X-Y.; Zhang, H-X.; Mezei, M.; Cui, M. Molecular docking: A powerful approach for structure-based drug discovery. Curr. Comput. Aided-Drug Des., 2012, 7, 146-157.
[http://dx.doi.org/10.2174/157340911795677602]
[172]
Gorgulla, C.; Boeszoermenyi, A.; Wang, Z.F.; Fischer, P.D.; Coote, P.W.; Padmanabha Das, K.M.; Malets, Y.S.; Radchenko, D.S.; Moroz, Y.S.; Scott, D.A.; Fackeldey, K.; Hoffmann, M.; Iavniuk, I.; Wagner, G.; Arthanari, H. An open-source drug discovery platform enables ultra-large virtual screens. Nature, 2020, 580(7805), 663-668.
[http://dx.doi.org/10.1038/s41586-020-2117-z] [PMID: 32152607]
[173]
Bolcato, G.; Cuzzolin, A.; Bissaro, M.; Moro, S.; Sturlese, M. Can we still trust docking results? an extension of the applicability of DockBench on PDBbind database. Int. J. Mol. Sci., 2019, 20(14), 3558.
[http://dx.doi.org/10.3390/ijms20143558] [PMID: 31330841]
[174]
Cuzzolin, A.; Deganutti, G.; Salmaso, V.; Sturlese, M.; Moro, S. AquaMMapS: An alternative tool to monitor the role of water molecules during protein-ligand association. ChemMedChem, 2018, 13(6), 522-531.
[http://dx.doi.org/10.1002/cmdc.201700564] [PMID: 29193885]
[175]
Roberts, B.C.; Mancera, R.L. Ligand-protein docking with water molecules. J. Chem. Inf. Model., 2008, 48(2), 397-408.
[http://dx.doi.org/10.1021/ci700285e] [PMID: 18211049]
[176]
Houston, D.R.; Walkinshaw, M.D. Consensus docking: Improving the reliability of docking in a virtual screening context. J. Chem. Inf. Model., 2013, 53(2), 384-390.
[http://dx.doi.org/10.1021/ci300399w] [PMID: 23351099]
[177]
Tuccinardi, T.; Poli, G.; Romboli, V.; Giordano, A.; Martinelli, A. Extensive consensus docking evaluation for ligand pose prediction and virtual screening studies. J. Chem. Inf. Model., 2014, 54(10), 2980-2986.
[http://dx.doi.org/10.1021/ci500424n] [PMID: 25211541]
[178]
Cheng, B.; Lin, Y.; Kuang, M.; Fang, S.; Gu, Q.; Xu, J.; Wang, L. Synthesis and anti-neuroinflammatory activity of lactone benzoyl hydrazine and 2-nitro-1-phenyl-1h-indole derivatives as p38α MAPK inhibitors. Chem. Biol. Drug Des., 2015, 86(5), 1121-1130.
[http://dx.doi.org/10.1111/cbdd.12581] [PMID: 25960125]
[179]
Rippin, I.; Khazanov, N.; Kudinov, T.; Berent, E.; Arciniegas Ruiz, S.M.; Marciano, D.; Levy, L.; Gruzman, A.; Senderowitz, H.; Eldar-Finkelman, H.; Joseph, S.B. Discovery and design of novel small molecule GSK-3 inhibitors targeting the substrate binding site. Int. J. Mol. Sci., 2020, 21(22), 8709.
[http://dx.doi.org/10.3390/ijms21228709] [PMID: 33218072]
[180]
Cescon, E.; Bolcato, G.; Federico, S.; Bissaro, M.; Valentini, A.; Ferlin, M.G.; Spalluto, G.; Sturlese, M.; Moro, S. Scaffold repurposing of in-house chemical library toward the identification of new Casein Kinase 1 δ inhibitors. ACS Med. Chem. Lett., 2020, 11(6), 1168-1174.
[http://dx.doi.org/10.1021/acsmedchemlett.0c00028] [PMID: 32550997]
[181]
Redenti, S.; Marcovich, I.; De Vita, T.; Pérez, C.; De Zorzi, R.; Demitri, N.; Perez, D.I.; Bottegoni, G.; Bisignano, P.; Bissaro, M.; Moro, S.; Martinez, A.; Storici, P.; Spalluto, G.; Cavalli, A.; Federico, S. A Triazolotriazine-based dual GSK-3β/CK-1δ ligand as a potential neuroprotective agent presenting two different mechanisms of enzymatic inhibition. ChemMedChem, 2019, 14(3), 310-314.
[http://dx.doi.org/10.1002/cmdc.201800778] [PMID: 30548443]
[182]
Tandon, A.; Sinha, S. Structural insights into the binding of MMP9 inhibitors. Bioinformation, 2011, 5(8), 310-314.
[http://dx.doi.org/10.6026/97320630005310] [PMID: 21383916]
[183]
Razak, S.; Afsar, T.; Bibi, N.; Abulmeaty, M.; Qamar, W.; Almajwal, A.; Inam, A.; Al Disi, D.; Shabbir, M.; Bhat, M.A. Molecular docking, pharmacokinetic studies, and in vivo pharmacological study of indole derivative 2-(5-methoxy-2-methyl-1H-indole-3-yl)-N′-[(E)-(3-nitrophenyl) methylidene] acetohydrazide as a promising chemoprotective agent against cisplatin induced organ damage. Sci. Rep., 2021, 11(1), 6245.
[http://dx.doi.org/10.1038/s41598-021-84748-y] [PMID: 33737575]
[184]
Liu, L-J.; Leung, K.H.; Chan, D.S.; Wang, Y.T.; Ma, D.L.; Leung, C.H. Identification of a natural product-like STAT3 dimerization inhibitor by structure-based virtual screening. Cell Death Dis., 2014, 5, e1293.
[http://dx.doi.org/10.1038/cddis.2014.250] [PMID: 24922077]
[185]
Ray, S.S.; Nowak, R.J.; Brown, R.H., Jr.; Lansbury, P.T., Jr. Small-molecule-mediated stabilization of familial amyotrophic lateral sclerosis-linked superoxide dismutase mutants against unfolding and aggregation. Proc. Natl. Acad. Sci. USA, 2005, 102(10), 3639-3644.
[http://dx.doi.org/10.1073/pnas.0408277102] [PMID: 15738401]
[186]
Durai, P.; Shin, H.J.; Achek, A.; Kwon, H.K.; Govindaraj, R.G.; Panneerselvam, S.; Yesudhas, D.; Choi, J.; No, K.T.; Choi, S. Toll-like receptor 2 antagonists identified through virtual screening and experimental validation. FEBS J., 2017, 284(14), 2264-2283.
[http://dx.doi.org/10.1111/febs.14124] [PMID: 28570013]
[187]
Mahita, J.; Harini, K.; Rao Pichika, M.; Sowdhamini, R. An in silico approach towards the identification of novel inhibitors of the TLR-4 signaling pathway. J. Biomol. Struct. Dyn., 2016, 34(6), 1345-1362.
[http://dx.doi.org/10.1080/07391102.2015.1079243] [PMID: 26264972]
[188]
Yilmazer, B.; Yagci, Z.B.; Bakar, E.; Ozden, B.; Ulgen, K.; Ozkirimli, E. Investigation of novel pharmacological chaperones for Gaucher Disease. J. Mol. Graph. Model., 2017, 76, 364-378.
[http://dx.doi.org/10.1016/j.jmgm.2017.07.014] [PMID: 28763689]
[189]
El-Zohairy, M.A.; Zlotos, D.P.; Berger, M.R.; Adwan, H.H.; Mandour, Y.M. Discovery of novel CCR5 ligands as anticolorectal cancer agents by sequential virtual screening. ACS Omega, 2021, 6(16), 10921-10935.
[http://dx.doi.org/10.1021/acsomega.1c00681] [PMID: 34056245]
[190]
Ahmad, K.; Balaramnavar, V.M.; Chaturvedi, N.; Khan, S.; Haque, S.; Lee, Y.H.; Choi, I. Targeting Caspase 8: Using structural and ligand-based approaches to identify potential leads for the treatment of multi-neurodegenerative diseases. Molecules, 2019, 24(9), 1827.
[http://dx.doi.org/10.3390/molecules24091827] [PMID: 31083628]
[191]
García-Aranda, M.I.; Gonzalez-Padilla, J.E.; Gómez-Castro, C.Z.; Gómez-Gómez, Y.M.; Rosales-Hernández, M.C.; García-Báez, E.V.; Franco-Hernández, M.O.; Castrejón-Flores, J.L.; Padilla-Martínez, I.I. Anti-inflammatory effect and inhibition of nitric oxide production by targeting COXs and iNOS enzymes with the 1,2-diphenylbenzimidazole pharmacophore. Bioorg. Med. Chem., 2020, 28(9), 115427.
[http://dx.doi.org/10.1016/j.bmc.2020.115427] [PMID: 32205045]
[192]
Martinez-Rosell, G.; Harvey, M.J.; De Fabritiis, G. Molecular-simulation-driven fragment screening for the discovery of new CXCL12 inhibitors. J. Chem. Inf. Model., 2018, 58(3), 683-691.
[http://dx.doi.org/10.1021/acs.jcim.7b00625] [PMID: 29481075]
[193]
De Vivo, M.; Masetti, M.; Bottegoni, G.; Cavalli, A. Role of molecular dynamics and related methods in drug discovery. J. Med. Chem., 2016, 59(9), 4035-4061.
[http://dx.doi.org/10.1021/acs.jmedchem.5b01684] [PMID: 26807648]
[194]
Hollingsworth, S.A.; Dror, R.O. Molecular dynamics simulation for all. Neuron, 2018, 99(6), 1129-1143.
[http://dx.doi.org/10.1016/j.neuron.2018.08.011] [PMID: 30236283]
[195]
Case, D.A. Amber 2021; University of California: San Francisco, 2021.
[196]
Berendsen, H.J.C.; van der Spoel, D.; van Drunen, R. GROMACS: A message-passing parallel molecular dynamics implementation. Comput. Phys. Commun., 1995, 91, 43-56.
[http://dx.doi.org/10.1016/0010-4655(95)00042-E]
[197]
Brooks, B.R.; Brooks, C.L., III; Mackerell, A.D., Jr; Nilsson, L.; Petrella, R.J.; Roux, B.; Won, Y.; Archontis, G.; Bartels, C.; Boresch, S.; Caflisch, A.; Caves, L.; Cui, Q.; Dinner, A.R.; Feig, M.; Fischer, S.; Gao, J.; Hodoscek, M.; Im, W.; Kuczera, K.; Lazaridis, T.; Ma, J.; Ovchinnikov, V.; Paci, E.; Pastor, R.W.; Post, C.B.; Pu, J.Z.; Schaefer, M.; Tidor, B.; Venable, R.M.; Woodcock, H.L.; Wu, X.; Yang, W.; York, D.M.; Karplus, M. CHARMM: The biomolecular simulation program. J. Comput. Chem., 2009, 30(10), 1545-1614.
[http://dx.doi.org/10.1002/jcc.21287] [PMID: 19444816]
[198]
Bowers, K.J. Molecular dynamics-Scalable algorithms for molecular dynamics simulations on commodity clusters. In: Proceedings of the 2006 ACM/IEEE Conference on Supercomputing, Tampa, FL, USA, 2006
[http://dx.doi.org/10.1145/1188455.1188544]
[199]
Lodola, A.; De Vivo, M. The increasing role of QM/MM in drug discovery Adv. Protein Chem. Struct. Biol., 2012, 87, 337-362.
[http://dx.doi.org/10.1016/B978-0-12-398312-1.00011-1]
[200]
Bunker, A.; Róg, T. Mechanistic understanding from molecular dynamics simulation in pharmaceutical research 1: Drug delivery. Front. Mol. Biosci., 2020, 7, 604770.
[http://dx.doi.org/10.3389/fmolb.2020.604770] [PMID: 33330633]
[201]
Salmas, R.E.; Yurtsever, M.; Durdagi, S. Investigation of inhibition mechanism of chemokine receptor CCR5 by micro-second molecular dynamics simulations. Sci. Rep., 2015, 5, 13180.
[http://dx.doi.org/10.1038/srep13180] [PMID: 26299310]
[202]
Banu, H.; Joseph, M.C.; Nisar, M.N. In-silico approach to investigate death domains associated with nano-particle- mediated cellular responses. Comput. Biol. Chem., 2018, 75, 11-23.
[http://dx.doi.org/10.1016/j.compbiolchem.2018.04.013] [PMID: 29723693]
[203]
Tanwar, H.; Kumar, D.T.; Doss, C.G.P.; Zayed, H. Bioinformatics classification of mutations in patients with Mucopolysaccharidosis IIIA. Metab. Brain Dis., 2019, 34(6), 1577-1594.
[http://dx.doi.org/10.1007/s11011-019-00465-6] [PMID: 31385193]
[204]
Hodošček, M.; Elghobashi-Meinhardt, N. Simulations of NPC1(NTD):NPC2 protein complex reveal cholesterol transfer pathways. Int. J. Mol. Sci., 2018, 19(9), 2623.
[http://dx.doi.org/10.3390/ijms19092623] [PMID: 30181526]
[205]
Czeleń, P.; Szefler, B. The oxindole derivatives, new promising GSK-3β inhibitors as one of the potential treatments for Alzheimer’s disease-A molecular dynamics approach. Biology (Basel), 2021, 10(4), 332.
[http://dx.doi.org/10.3390/biology10040332] [PMID: 33920768]
[206]
Kalva, S.; Agrawal, N.; Skelton, A.A.; Saleena, L.M. Identification of novel selective MMP-9 inhibitors as potential anti-metastatic lead using structure-based hierarchical virtual screening and molecular dynamics simulation. Mol. Biosyst., 2016, 12(8), 2519-2531.
[http://dx.doi.org/10.1039/C6MB00066E] [PMID: 27250644]
[207]
Özkılıç, Y.; Tüzün, N.Ş. in silico methods predict new blood-brain barrier permeable structure for the inhibition of kynurenine 3-monooxygenase. J. Mol. Graph. Model., 2020, 100, 107701.
[http://dx.doi.org/10.1016/j.jmgm.2020.107701] [PMID: 32805560]
[208]
Jamal, S.; Grover, A.; Grover, S. Machine learning from molecular dynamics trajectories to predict Caspase-8 inhibitors against Alzheimer’s Disease. Front. Pharmacol., 2019, 10, 780.
[http://dx.doi.org/10.3389/fphar.2019.00780] [PMID: 31354494]
[209]
Löscher, W.; Potschka, H. Blood-brain barrier active efflux transporters: ATP-binding cassette gene family. NeuroRx, 2005, 2(1), 86-98.
[http://dx.doi.org/10.1602/neurorx.2.1.86] [PMID: 15717060]
[210]
Daneman, R.; Prat, A. The blood-brain barrier. Cold Spring Harb. Perspect. Biol., 2015, 7(1), a020412.
[http://dx.doi.org/10.1101/cshperspect.a020412] [PMID: 25561720]
[211]
Upton, R.N. Cerebral uptake of drugs in humans. Clin. Exp. Pharmacol. Physiol., 2007, 34(8), 695-701.
[http://dx.doi.org/10.1111/j.1440-1681.2007.04649.x] [PMID: 17600543]
[212]
Benet, L.Z.; Hosey, C.M.; Ursu, O.; Oprea, T.I. BDDCS, the Rule of 5 and drugability. Adv. Drug Deliv. Rev., 2016, 101, 89-98.
[http://dx.doi.org/10.1016/j.addr.2016.05.007] [PMID: 27182629]
[213]
Wang, Y.; Xing, J.; Xu, Y.; Zhou, N.; Peng, J.; Xiong, Z.; Liu, X.; Luo, X.; Luo, C.; Chen, K.; Zheng, M.; Jiang, H. in silico ADME/T modelling for rational drug design. Q. Rev. Biophys., 2015, 48(4), 488-515.
[http://dx.doi.org/10.1017/S0033583515000190] [PMID: 26328949]
[214]
Bhhatarai, B.; Walters, W.P.; Hop, C.E.C.A.; Lanza, G.; Ekins, S. Opportunities and challenges using artificial intelligence in ADME/Tox. Nat. Mater., 2019, 18(5), 418-422.
[http://dx.doi.org/10.1038/s41563-019-0332-5] [PMID: 31000801]
[215]
Göller, A.H.; Kuhnke, L.; Montanari, F.; Bonin, A.; Schneckener, S.; Ter Laak, A.; Wichard, J.; Lobell, M.; Hillisch, A. Bayer’s in silico ADMET platform: A journey of machine learning over the past two decades. Drug Discov. Today, 2020, 25(9), 1702-1709.
[http://dx.doi.org/10.1016/j.drudis.2020.07.001] [PMID: 32652309]
[216]
Jorgensen, W.L.; Duffy, E.M. Prediction of drug solubility from structure. Adv. Drug Deliv. Rev., 2002, 54(3), 355-366.
[http://dx.doi.org/10.1016/S0169-409X(02)00008-X] [PMID: 11922952]
[217]
Cruciani, G.; Carosati, E.; De Boeck, B.; Ethirajulu, K.; Mackie, C.; Howe, T.; vianello, R. MetaSite: understanding metabolism in human cytochromes from the perspective of the chemist. J. Med. Chem., 2005, 48(22), 6970-6979.
[http://dx.doi.org/10.1021/jm050529c] [PMID: 16250655]
[218]
Daina, A.; Michielin, O.; Zoete, V. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci. Rep., 2017, 7, 42717.
[http://dx.doi.org/10.1038/srep42717] [PMID: 28256516]
[219]
Peach, M.L.; Zakharov, A.V.; Liu, R.; Pugliese, A.; Tawa, G.; Wallqvist, A.; Nicklaus, M.C. Computational tools and resources for metabolism-related property predictions. 1. Overview of publicly available (free and commercial) databases and software. Future Med. Chem., 2012, 4(15), 1907-1932.
[http://dx.doi.org/10.4155/fmc.12.150] [PMID: 23088273]
[220]
Kadioglu, O.; Efferth, T. A machine learning-based prediction platform for P-Glycoprotein modulators and its validation by molecular docking. Cells, 2019, 8(10), 1286.
[http://dx.doi.org/10.3390/cells8101286] [PMID: 31640190]
[221]
Watanabe, R.; Esaki, T.; Ohashi, R.; Kuroda, M.; Kawashima, H.; Komura, H.; Natsume-Kitatani, Y.; Mizuguchi, K. Development of an in silico prediction model for P-glycoprotein efflux potential in brain capillary endothelial cells toward the prediction of brain penetration. J. Med. Chem., 2021, 64(5), 2725-2738.
[http://dx.doi.org/10.1021/acs.jmedchem.0c02011] [PMID: 33619967]
[222]
Montanari, F.; Ecker, G.F. Prediction of drug-ABC-transporter interaction-Recent advances and future challenges. Adv. Drug Deliv. Rev., 2015, 86, 17-26.
[http://dx.doi.org/10.1016/j.addr.2015.03.001] [PMID: 25769815]
[223]
Yang, N. J.; Hinner, M. J. Getting across the cell membrane: An overview for small molecules, peptides, and proteins. Methods Mol. Biol., 2015, 1266, 29-53.
[http://dx.doi.org/10.1007/978-1-4939-2272-7_3]
[224]
Madden, J.C.; Enoch, S.J.; Paini, A.; Cronin, M.T.D. A review of in silico tools as alternatives to animal testing: Principles, resources and applications. Altern. Lab. Anim., 2020, 48(4), 146-172.
[http://dx.doi.org/10.1177/0261192920965977] [PMID: 33119417]
[225]
Hemmateenejad, B.; Miri, R.; Safarpour, M.A.; Mehdipour, A.R. Accurate prediction of the blood-brain partitioning of a large set of solutes using ab initio calculations and genetic neural network modeling. J. Comput. Chem., 2006, 27(11), 1125-1135.
[http://dx.doi.org/10.1002/jcc.20437] [PMID: 16721721]
[226]
Muehlbacher, M.; Spitzer, G.M.; Liedl, K.R.; Kornhuber, J. Qualitative prediction of blood-brain barrier permeability on a large and refined dataset. J. Comput. Aided Mol. Des., 2011, 25(12), 1095-1106.
[http://dx.doi.org/10.1007/s10822-011-9478-1] [PMID: 22109848]
[227]
Liu, L.; Zhang, L.; Feng, H.; Li, S.; Liu, M.; Zhao, J.; Liu, H. Prediction of the Blood-Brain Barrier (BBB) permeability of chemicals based on Machine-Learning and Ensemble Methods. Chem. Res. Toxicol., 2021, 34(6), 1456-1467.
[http://dx.doi.org/10.1021/acs.chemrestox.0c00343] [PMID: 34047182]
[228]
Shahbazi, S.; Kaur, J.; Singh, S.; Achary, K.G.; Wani, S.; Jema, S.; Akhtar, J.; Sobti, R.C. Impact of novel N-aryl piperamide NO donors on NF-κB translocation in neuroinflammation: Rational drug-designing synthesis and biological evaluation. Innate Immun., 2018, 24(1), 24-39.
[http://dx.doi.org/10.1177/1753425917740727] [PMID: 29145791]
[229]
Dileep, K.V.; Remya, C.; Tintu, I.; Sadasivan, C. Designing of multi-target-directed ligands against the enzymes associated with neuroinflammation: An in silico approach. Front. Life Sci., 2013, 7(3-4), 174-185.
[http://dx.doi.org/10.1080/21553769.2014.901924]
[230]
Elrayess, R.; Elgawish, M.S.; Elewa, M.; Nafie, M.S.; Elhady, S.S.; Yassen, A.S.A. Synthesis, 3D-QSAR, and molecular modeling studies of triazole bearing compounds as a promising scaffold for Cyclooxygenase-2 inhibition. Pharmaceuticals (Basel), 2020, 13(11), 370.
[http://dx.doi.org/10.3390/ph13110370] [PMID: 33172102]

Rights & Permissions Print Cite
Article Metrics
37
1
World Chagas Disease
© 2024 Bentham Science Publishers | Privacy Policy