In Silico Repositioning of Dopamine Modulators with Possible Application to Schizophrenia: Pharmacophore Mapping, Molecular Docking and Molecular Dynamics Analysis

We have performed theoretical calculations with 70 drugs that have been considered in 231 clinical trials as possible candidates to repurpose drugs for schizophrenia based on their interactions with the dopaminergic system. A hypotheis of shared pharmacophore features was formulated to support our c...

Full description

Autores:
Mejia-Gutierrez, Melissa
Tipo de recurso:
Fecha de publicación:
2020
Institución:
Universidad del Atlántico
Repositorio:
Repositorio Uniatlantico
Idioma:
eng
OAI Identifier:
oai:repositorio.uniatlantico.edu.co:20.500.12834/1158
Acceso en línea:
https://hdl.handle.net/20.500.12834/1158
Palabra clave:
Rights
openAccess
License
http://creativecommons.org/licenses/by-nc/4.0/
id UNIATLANT2_8ffbf8a0e701b74a34b800798c32e206
oai_identifier_str oai:repositorio.uniatlantico.edu.co:20.500.12834/1158
network_acronym_str UNIATLANT2
network_name_str Repositorio Uniatlantico
repository_id_str
dc.title.spa.fl_str_mv In Silico Repositioning of Dopamine Modulators with Possible Application to Schizophrenia: Pharmacophore Mapping, Molecular Docking and Molecular Dynamics Analysis
dc.title.alternative.spa.fl_str_mv In Silico Repositioning of Dopamine Modulators with Possible Application to Schizophrenia: Pharmacophore Mapping, Molecular Docking and Molecular Dynamics Analysis
title In Silico Repositioning of Dopamine Modulators with Possible Application to Schizophrenia: Pharmacophore Mapping, Molecular Docking and Molecular Dynamics Analysis
spellingShingle In Silico Repositioning of Dopamine Modulators with Possible Application to Schizophrenia: Pharmacophore Mapping, Molecular Docking and Molecular Dynamics Analysis
title_short In Silico Repositioning of Dopamine Modulators with Possible Application to Schizophrenia: Pharmacophore Mapping, Molecular Docking and Molecular Dynamics Analysis
title_full In Silico Repositioning of Dopamine Modulators with Possible Application to Schizophrenia: Pharmacophore Mapping, Molecular Docking and Molecular Dynamics Analysis
title_fullStr In Silico Repositioning of Dopamine Modulators with Possible Application to Schizophrenia: Pharmacophore Mapping, Molecular Docking and Molecular Dynamics Analysis
title_full_unstemmed In Silico Repositioning of Dopamine Modulators with Possible Application to Schizophrenia: Pharmacophore Mapping, Molecular Docking and Molecular Dynamics Analysis
title_sort In Silico Repositioning of Dopamine Modulators with Possible Application to Schizophrenia: Pharmacophore Mapping, Molecular Docking and Molecular Dynamics Analysis
dc.creator.fl_str_mv Mejia-Gutierrez, Melissa
dc.contributor.author.none.fl_str_mv Mejia-Gutierrez, Melissa
dc.contributor.other.none.fl_str_mv Vásquez-Paz, Bryan D
Fierro, Leonardo
Julio R. Maza, Julio
description We have performed theoretical calculations with 70 drugs that have been considered in 231 clinical trials as possible candidates to repurpose drugs for schizophrenia based on their interactions with the dopaminergic system. A hypotheis of shared pharmacophore features was formulated to support our calculations. To do so, we have used the crystal structure of the D2-like dopamine receptor in complex with risperidone, eticlopride, and nemonapride. Linagliptin, citalopram, flunarizine, sildenafil, minocycline, and duloxetine were the drugs that best fit with our model. Molecular docking calculations, molecular dynamics outcomes, blood-brain barrier penetration, and human intestinal absorption were studied and compared with the results. From the six drugs selected in the shared pharmacophore features input, flunarizine showed the best docking score with D2, D3, and D4 dopamine receptors and had high stability during molecular dynamics simulations. Flunarizine is a frequently used medication to treat migraines and vertigo. However, its antipsychotic properties have been previously hypothesized, particularly because of its possible ability to block the D2 dopamine receptors.
publishDate 2020
dc.date.submitted.none.fl_str_mv 2020-12-09
dc.date.issued.none.fl_str_mv 2021-03-30
dc.date.accessioned.none.fl_str_mv 2022-12-20T19:13:30Z
dc.date.available.none.fl_str_mv 2022-12-20T19:13:30Z
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
dc.type.hasVersion.spa.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.spa.spa.fl_str_mv Artículo
status_str publishedVersion
dc.identifier.citation.spa.fl_str_mv Mejia-Gutierrez, M., Vásquez-Paz, B. D., Fierro, L., & Maza, J. R. (2021). In Silico Repositioning of Dopamine Modulators with Possible Application to Schizophrenia: Pharmacophore Mapping, Molecular Docking and Molecular Dynamics Analysis. ACS omega, 6(23), 14748–14764. https://doi.org/10.1021/acsomega.0c05984
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12834/1158
dc.identifier.doi.none.fl_str_mv 10.1021/acsomega.0c05984
dc.identifier.instname.spa.fl_str_mv Universidad del Atlántico
dc.identifier.reponame.spa.fl_str_mv Repositorio Universidad del Atlántico
identifier_str_mv Mejia-Gutierrez, M., Vásquez-Paz, B. D., Fierro, L., & Maza, J. R. (2021). In Silico Repositioning of Dopamine Modulators with Possible Application to Schizophrenia: Pharmacophore Mapping, Molecular Docking and Molecular Dynamics Analysis. ACS omega, 6(23), 14748–14764. https://doi.org/10.1021/acsomega.0c05984
10.1021/acsomega.0c05984
Universidad del Atlántico
Repositorio Universidad del Atlántico
url https://hdl.handle.net/20.500.12834/1158
dc.language.iso.spa.fl_str_mv eng
language eng
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.uri.*.fl_str_mv http://creativecommons.org/licenses/by-nc/4.0/
dc.rights.cc.*.fl_str_mv Attribution-NonCommercial 4.0 International
dc.rights.accessRights.spa.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc/4.0/
Attribution-NonCommercial 4.0 International
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.place.spa.fl_str_mv Barranquilla
dc.publisher.discipline.spa.fl_str_mv Química
dc.publisher.sede.spa.fl_str_mv Sede Norte
dc.source.spa.fl_str_mv ACS Omega
institution Universidad del Atlántico
bitstream.url.fl_str_mv https://repositorio.uniatlantico.edu.co/bitstream/20.500.12834/1158/1/In_Silico_Repositioning_of_Dopamine_Modulators_wit.pdf
https://repositorio.uniatlantico.edu.co/bitstream/20.500.12834/1158/2/license_rdf
https://repositorio.uniatlantico.edu.co/bitstream/20.500.12834/1158/3/license.txt
bitstream.checksum.fl_str_mv 69eea69653b7566cc82f5236c24c2cfa
24013099e9e6abb1575dc6ce0855efd5
67e239713705720ef0b79c50b2ececca
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
repository.name.fl_str_mv DSpace de la Universidad de Atlántico
repository.mail.fl_str_mv sysadmin@mail.uniatlantico.edu.co
_version_ 1814203418516914176
spelling Mejia-Gutierrez, Melissa1a1f0994-ff8f-47a2-b3d1-1864bace2176Vásquez-Paz, Bryan DFierro, LeonardoJulio R. Maza, Julio2022-12-20T19:13:30Z2022-12-20T19:13:30Z2021-03-302020-12-09Mejia-Gutierrez, M., Vásquez-Paz, B. D., Fierro, L., & Maza, J. R. (2021). In Silico Repositioning of Dopamine Modulators with Possible Application to Schizophrenia: Pharmacophore Mapping, Molecular Docking and Molecular Dynamics Analysis. ACS omega, 6(23), 14748–14764. https://doi.org/10.1021/acsomega.0c05984https://hdl.handle.net/20.500.12834/115810.1021/acsomega.0c05984Universidad del AtlánticoRepositorio Universidad del AtlánticoWe have performed theoretical calculations with 70 drugs that have been considered in 231 clinical trials as possible candidates to repurpose drugs for schizophrenia based on their interactions with the dopaminergic system. A hypotheis of shared pharmacophore features was formulated to support our calculations. To do so, we have used the crystal structure of the D2-like dopamine receptor in complex with risperidone, eticlopride, and nemonapride. Linagliptin, citalopram, flunarizine, sildenafil, minocycline, and duloxetine were the drugs that best fit with our model. Molecular docking calculations, molecular dynamics outcomes, blood-brain barrier penetration, and human intestinal absorption were studied and compared with the results. From the six drugs selected in the shared pharmacophore features input, flunarizine showed the best docking score with D2, D3, and D4 dopamine receptors and had high stability during molecular dynamics simulations. Flunarizine is a frequently used medication to treat migraines and vertigo. However, its antipsychotic properties have been previously hypothesized, particularly because of its possible ability to block the D2 dopamine receptors.application/pdfenghttp://creativecommons.org/licenses/by-nc/4.0/Attribution-NonCommercial 4.0 Internationalinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2ACS OmegaIn Silico Repositioning of Dopamine Modulators with Possible Application to Schizophrenia: Pharmacophore Mapping, Molecular Docking and Molecular Dynamics AnalysisIn Silico Repositioning of Dopamine Modulators with Possible Application to Schizophrenia: Pharmacophore Mapping, Molecular Docking and Molecular Dynamics AnalysisPúblico generalinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1BarranquillaQuímicaSede NorteLago, S. G.; Bahn, S. Clinical Trials and Therapeutic Rationale for Drug Repurposing in Schizophrenia. ACS Chem. Neurosci. 2019, 10, 58−78.Saldívar-González, F.; Prieto-Martínez, F. D.; Medina-Franco, J. L. Descubrimiento y Desarrollo de Farmacos: Un Enfoque ́ Computacional. Educ. Quim. 2017, 28, 51−58Saldívar-González, F.; Prieto-Martínez, F. D.; Medina-Franco, J. L. Descubrimiento y Desarrollo de Farmacos: Un Enfoque ́ Computacional. Educ. Quim. 2017, 28, 51−58Lau, C.-I.; Wang, H.-C.; Hsu, J.-L.; Liu, M.-E. Does the Dopamine Hypothesis Explain Schizophrenia? Rev. Neurosci. 2013, 24, 389−400.Beaulieu, J.-M.; Gainetdinov, R. R. The Physiology, Signaling, and Pharmacology of Dopamine Receptors. Pharmacol. Rev. 2011, 63, 182−217Parsons, M. J.; Mata, I.; Beperet, M.; Iribarren-Iriso, F.; Arroyo, B.; Sainz, R.; Arranz, M. J.; Kerwin, R. A Dopamine D2 Receptor Gene-Related Polymorphism Is Associated with Schizophrenia in a Spanish Population Isolate. Psychiatr. Genet. 2007, 17, 159−163.Bertram L. Genetic Research in Schizophrenia: New Tools and Future Perspectives. Schizophr. Bull. 2008, 34, 806–812. 10.1093/schbul/sbn079. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Rondou P.; Haegeman G.; Van Craenenbroeck K. The Dopamine D4 Receptor: Biochemical and Signalling Properties. Cell. Mol. Life Sci. 2010, 67, 1971–1986. 10.1007/s00018-010-0293-y. [PubMed] [CrossRef] [Google Scholar]Gómez-Restrepo C. Guía de práctica clínica para el diagnóstico, tratamiento e inicio de la rehabilitación psicosocial de los adultos con esquizofrenia. Rev. Colomb. Psiquiatr. 2014, 44, 1–2. 10.1016/j.rcp.2015.05.014. [PubMed] [CrossRef] [Google Scholar]Goto A.; Mouri A.; Nagai T.; Yoshimi A.; Ukigai M.; Tsubai T.; Hida H.; Ozaki N.; Noda Y. Involvement of the Histamine H4 Receptor in Clozapine-Induced Hematopoietic Toxicity: Vulnerability under Granulocytic Differentiation of HL-60 Cells. Toxicol. Appl. Pharmacol. 2016, 306, 8–16. 10.1016/j.taap.2016.06.028. [PubMed] [CrossRef] [Google Scholar]Seeman P.; Weinshenker D.; Quirion R.; Srivastava L. K.; Bhardwaj S. K.; Grandy D. K.; Premont R. T.; Sotnikova T. D.; Boksa P.; El-Ghundi M.; et al. Dopamine Supersensitivity Correlates with D2High States, Implying Many Paths to Psychosis. Proc. Natl. Acad. Sci. U. S. A. 2005, 102, 3513–3518. 10.1073/pnas.0409766102. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Seeman P.; Ko F.; Jack E.; Greenstein R.; Dean B. Consistent with Dopamine Supersensitivity, RGS9 Expression Is Diminished in the Amphetamine-Treated Animal Model of Schizophrenia and in Postmortem Schizophrenia Brain. Synapse 2007, 61, 303–309. 10.1002/syn.20368. [PubMed] [CrossRef] [Google Scholar]Gaspar H. A.; Breen G. Drug Enrichment and Discovery from Schizophrenia Genome-Wide Association Results: An Analysis and Visualisation Approach. Sci. Rep. 2017, 7, 12460. 10.1038/s41598-017-12325-3. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Zhao K.; So H.-C. Drug Repositioning for Schizophrenia and Depression/Anxiety Disorders: A Machine Learning Approach Leveraging Expression Data. IEEE J. Biomed. Health Inf. 2019, 23, 1304–1315. 10.1109/JBHI.2018.2856535. [PubMed] [CrossRef] [Google Scholar]Karunakaran K. B.; Chaparala S.; Ganapathiraju M. K. Potentially Repurposable Drugs for Schizophrenia Identified from Its Interactome. Sci. Rep. 2019, 9, 12682. 10.1038/s41598-019-48307-w. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Lee J. H.; Cho S. J.; Kim M.-H. Discovery of CNS-Like D3R-Selective Antagonists Using 3D Pharmacophore Guided Virtual Screening. Molecules 2018, 23, 2452. 10.3390/molecules23102452. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Lemos A.; Melo R.; Preto A. J.; Almeida J. G.; Moreira I. S.; Dias Soeiro Cordeiro M. N. In Silico Studies Targeting G-Protein Coupled Receptors for Drug Research Against Parkinson’s Disease. Curr. Neuropharmacol. 2018, 16, 786–848. 10.2174/1570159X16666180308161642. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Ishiki H. M.; Filho J. M. B.; da Silva M. S.; Scotti M. T.; Scotti L. Computer-Aided Drug Design Applied to Parkinson Targets. Curr. Neuropharmacol. 2018, 16, 865–880. 10.2174/1570159X15666171128145423. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Zhao Y.; Lu X.; Yang C.-Y.; Huang Z.; Fu W.; Hou T.; Zhang J. Computational Modeling toward Understanding Agonist Binding on Dopamine 3. J. Chem. Inf. Model. 2010, 50, 1633–1643. 10.1021/ci1002119. [PubMed] [CrossRef] [Google Scholar]Kaserer T.; Beck K. R.; Akram M.; Odermatt A.; Schuster D. Pharmacophore Models and Pharmacophore-Based Virtual Screening: Concepts and Applications Exemplified on Hydroxysteroid Dehydrogenases. Molecules 2015, 20, 22799–22832. 10.3390/molecules201219880. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Ferraro M.; Decherchi S.; De Simone A.; Recanatini M.; Cavalli A.; Bottegoni G. Multi-Target Dopamine D3 Receptor Modulators: Actionable Knowledge for Drug Design from Molecular Dynamics and Machine Learning. Eur. J. Med. Chem. 2020, 188, 111975. 10.1016/j.ejmech.2019.111975. [PubMed] [CrossRef] [Google Scholar]Bhargava K.; Nath R.; Seth P. K.; Pant K. K.; Dixit R. K. Molecular Docking Studies of D2 Dopamine Receptor with Risperidone Derivatives. Bioinformation 2014, 10, 8–12. 10.6026/97320630010008. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Bueschbell B.; Barreto C. A. V.; Preto A. J.; Schiedel A. C.; Moreira I. S. A Complete Assessment of Dopamine Receptor- Ligand Interactions through Computational Methods. Molecules 2019, 24, 1196. 10.3390/molecules24071196. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Trott O.; Olson A. J. AutoDock Vina: Improving the Speed and Accuracy of Docking with a New Scoring Function, Efficient Optimization, and Multithreading. J. Comput. Chem. 2010, 31, 455–461. 10.1002/jcc.21334. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Krautscheid Y.; Senning C. J. Å.; Sartori S. B.; Singewald N.; Schuster D.; Stuppner H. Pharmacophore Modeling, Virtual Screening, and in Vitro Testing Reveal Haloperidol, Eprazinone, and Fenbutrazate as Neurokinin Receptors Ligands. J. Chem. Inf. Model. 2014, 54, 1747–1757. 10.1021/ci500106z. [PubMed] [CrossRef] [Google Scholar]Patel C. N.; Georrge J. J.; Modi K. M.; Narechania M. B.; Patel D. P.; Gonzalez F. J.; Pandya H. A. Pharmacophore-Based Virtual Screening of Catechol-o-Methyltransferase (COMT) Inhibitors to Combat Alzheimer’s Disease. J. Biomol. Struct. Dyn. 2018, 36, 3938–3957. 10.1080/07391102.2017.1404931. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Floresta G.; Crocetti L.; Giovannoni M. P.; Biagini P.; Cilibrizzi A. Repurposing Strategies on Pyridazinone-Based Series by Pharmacophore- and Structure-Driven Screening. J. Enzyme Inhib. Med. Chem. 2020, 35, 1137–1144. 10.1080/14756366.2020.1760261. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Kagami L. P.; das Neves G. M.; Rodrigues R. P.; da Silva V. B.; Eifler-Lima V. L.; Kawano D. F. Identification of a Novel Putative Inhibitor of the Plasmodium Falciparum Purine Nucleoside Phosphorylase: Exploring the Purine Salvage Pathway to Design New Antimalarial Drugs. Mol. Diversity 2017, 21, 677–695. 10.1007/s11030-017-9745-8. [PubMed] [CrossRef] [Google Scholar]Kumar S.; Chowdhury S.; Kumar S. In Silico Repurposing of Antipsychotic Drugs for Alzheimer’s Disease. BMC Neurosci. 2017, 18, 76. 10.1186/s12868-017-0394-8. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Dilly S.; Fotso Fotso A.; Lejal N.; Zedda G.; Chebbo M.; Rahman F.; Companys S.; Bertrand H. C.; Vidic J.; Noiray M.; et al. From Naproxen Repurposing to Naproxen Analogues and Their Antiviral Activity against Influenza A Virus. J. Med. Chem. 2018, 61, 7202–7217. 10.1021/acs.jmedchem.8b00557. [PubMed] [CrossRef] [Google Scholar]Teli M. K.; Rajanikant G. K. Computational Repositioning and Experimental Validation of Approved Drugs for HIF-Prolyl Hydroxylase Inhibition. J. Chem. Inf. Model. 2013, 53, 1818–1824. 10.1021/ci400254a. [PubMed] [CrossRef] [Google Scholar]Macalino S. J. Y.; Gosu V.; Hong S.; et al. Role of computer-aided drug design in modern drug discovery. Arch. Pharmacal Res. 2015, 38, 1686–1701. 10.1007/s12272-015-0640-5. [PubMed] [CrossRef] [Google Scholar]Liu X.; Shi D.; Zhou S.; Liu H.; Liu H.; Yao X. Molecular Dynamics Simulations and Novel Drug Discovery. Expert Opin. Drug Discovery 2018, 13, 23–37. 10.1080/17460441.2018.1403419. [PubMed] [CrossRef] [Google Scholar]Do P.-C.; Lee E. H.; Le L. Steered Molecular Dynamics Simulation in Rational Drug Design. J. Chem. Inf. Model. 2018, 58, 1473–1482. 10.1021/acs.jcim.8b00261. [PubMed] [CrossRef] [Google Scholar]Hodos R. A.; Kidd B. A.; Shameer K.; Readhead B. P.; Dudley J. T. In Silico Methods for Drug Repurposing and Pharmacology. Wiley Interdiscip. Rev.: Syst. Biol. Med. 2016, 8, 186–210. 10.1002/wsbm.1337. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Daina A.; Zoete V. A BOILED-Egg To Predict Gastrointestinal Absorption and Brain Penetration of Small Molecules. ChemMedChem 2016, 11, 1117–1121. 10.1002/cmdc.201600182. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Tandon R.; Keshavan M. S.; Nasrallah H. A. Schizophrenia, “Just the Facts”: What We Know in 2008 Part 1: Overview. Schizophr. Res. 2008, 100, 4–19. 10.1016/j.schres.2008.01.022. [PubMed] [CrossRef] [Google Scholar]Gaebel W.; Zielasek J. Schizophrenia in 2020: Trends in Diagnosis and Therapy. Psychiatry Clin. Neurosci. 2015, 69, 661–673. 10.1111/pcn.12322. [PubMed] [CrossRef] [Google Scholar]Seeman P.; Kapur S. Schizophrenia: More Dopamine, More D2 Receptors. Proc. Natl. Acad. Sci. U. S. A. 2000, 97, 7673–7675. 10.1073/pnas.97.14.7673. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Lee T.; Seeman P.. Brain Dopamine Receptors In Schizophrenia; Usdin E., Hanin I. B. T.-B. M. in P. and N., Eds.; Pergamon, 1982; pp. 219–226, 10.1016/B978-0-08-027987-9.50027-5. [CrossRef] [Google Scholar]Putnam D. K.; Sun J.; Zhao Z. Exploring Schizophrenia Drug-Gene Interactions through Molecular Network and Pathway Modeling. AMIA . Annual Symposium proceedings. AMIA Annu. Symp. Proc. 2011, 2011, 1127–1133. [PMC free article] [PubMed] [Google Scholar]Kesby J. P.; Eyles D. W.; McGrath J. J.; Scott J. G. Dopamine, Psychosis and Schizophrenia: The Widening Gap between Basic and Clinical Neuroscience. Transl. Psychiatry 2018, 8, 30. 10.1038/s41398-017-0071-9. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Laboratories K. KEEG https://www.genome.jp/kegg/ (accessed Oct 26, 2020).Sibley D. R.; Neve K.Dopamine Receptors; Enna S. J., Bylund D. B. B. T. T. C. P. R., Eds.; Elsevier: New York, 2007; pp. 1–4, 10.1016/B978-008055232-3.60151-5. [CrossRef] [Google Scholar]Neve K. A.Dopamine Receptors; Lennarz W. J., Lane M. D. B. T.-E. of B. C. (Second E., Eds.; Academic Press: Waltham, 2013; pp. 169–173, 10.1016/B978-0-12-378630-2.00326-1. [CrossRef] [Google Scholar]Wang S.; Che T.; Levit A.; Shoichet B. K.; Wacker D.; Roth B. L. Structure of the D2 Dopamine Receptor Bound to the Atypical Antipsychotic Drug Risperidone. Nature 2018, 555, 269–273. 10.1038/nature25758. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Corena-McLeod M. Comparative Pharmacology of Risperidone and Paliperidone. Drugs R&D 2015, 15, 163–174. 10.1007/s40268-015-0092-x. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Martelle J. L.; Nader M. A. A Review of the Discovery, Pharmacological Characterization, and Behavioral Effects of the Dopamine D2-like Receptor Antagonist Eticlopride. CNS Neurosci. Ther. 2008, 14, 248–262. 10.1111/j.1755-5949.2008.00047.x. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Miyamoto S.Nemonapride BT - Encyclopedia of Psychopharmacology; Stolerman I. P., Ed.; Springer Berlin Heidelberg: Berlin, Heidelberg, 2010; p 823, 10.1007/978-3-540-68706-1_1840. [CrossRef] [Google Scholar]Platania C. B. M.; Salomone S.; Leggio G. M.; Drago F.; Bucolo C. Homology Modeling of Dopamine D2 and D3 Receptors: Molecular Dynamics Refinement and Docking Evaluation. PLoS One 2012, 7, e44316–e44316. 10.1371/journal.pone.0044316. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Chien E. Y. T.; Liu W.; Zhao Q.; Katritch V.; Han G. W.; Hanson M. A.; Shi L.; Newman A. H.; Javitch J. A.; Cherezov V.; Stevens R. C. Structure of the human dopamine D3 receptor in complex with a D2/D3 selective antagonist. Science 2010, 330, 1091–1095. 10.1126/science.1197410. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Ferreira de Freitas R.; Schapira M. A Systematic Analysis of Atomic Protein–Ligand Interactions in the PDB. MedChemComm 2017, 8, 1970–1981. 10.1039/C7MD00381A. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Wang S.; Wacker D.; Levit A.; Che T.; Betz R. M.; McCorvy J. D.; Venkatakrishnan A. J.; Huang X.-P.; Dror R. O.; Shoichet B. K.; Roth B. L. D4 Dopamine Receptor High-Resolution Structures Enable the Discovery of Selective Agonists. Science 2017, 358, 381–386. 10.1126/science.aan5468. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Vanommeslaeghe K.; Hatcher E.; Acharya C.; Kundu S.; Zhong S.; Shim J.; Darian E.; Guvench O.; Lopes P.; Vorobyov I.; Mackerell A. D. Jr. CHARMM General Force Field: A Force Field for Drug-like Molecules Compatible with the CHARMM All-Atom Additive Biological Force Fields. J. Comput. Chem. 2010, 31, 671–690. 10.1002/jcc.21367. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Jean B.; Surratt C. K.; Madura J. D. Molecular Dynamics of Conformation-Specific Dopamine Transporter-Inhibitor Complexes. J. Mol. Graphics Modell. 2017, 76, 143–151. 10.1016/j.jmgm.2017.07.003. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Huang J.; Rauscher S.; Nawrocki G.; Ran T.; Feig M.; de Groot B. L.; Grubmüller H.; MacKerell A. D. Jr. CHARMM36m: An Improved Force Field for Folded and Intrinsically Disordered Proteins. Nat. Methods 2017, 14, 71–73. 10.1038/nmeth.4067. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Nielsen A. K.; Möller I. R.; Wang Y.; Rasmussen S. G. F.; Lindorff-Larsen K.; Rand K. D.; Loland C. J. Substrate-Induced Conformational Dynamics of the Dopamine Transporter. Nat. Commun. 2019, 10, 2714. 10.1038/s41467-019-10449-w. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Klauda J. B.; Venable R. M.; Freites J. A.; O’Connor J. W.; Tobias D. J.; Mondragon-Ramirez C.; Vorobyov I.; MacKerell A. D. Jr.; Pastor R. W. Update of the CHARMM All-Atom Additive Force Field for Lipids: Validation on Six Lipid Types. J. Phys. Chem. B 2010, 114, 7830–7843. 10.1021/jp101759q. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Vanommeslaeghe K.; MacKerell A. D. Jr. Automation of the CHARMM General Force Field (CGenFF) I: Bond Perception and Atom Typing. J. Chem. Inf. Model. 2012, 52, 3144–3154. 10.1021/ci300363c. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Best R. B.; Zhu X.; Shim J.; Lopes P. E. M.; Mittal J.; Feig M.; Mackerell A. D. Jr. Optimization of the Additive CHARMM All-Atom Protein Force Field Targeting Improved Sampling of the Backbone φ, ψ and Side-Chain χ(1) and χ(2) Dihedral Angles. J. Chem. Theory Comput. 2012, 8, 3257–3273. 10.1021/ct300400x. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Lane J. R.; Abramyan A. M.; Adhikari P.; Keen A. C.; Lee K.-H.; Sanchez J.; Verma R. K.; Lim H. D.; Yano H.; Javitch J. A.; Shi L. Distinct Inactive Conformations of the Dopamine D2 and D3 Receptors Correspond to Different Extents of Inverse Agonism. eLife 2020, 9, e52189 10.7554/eLife.52189. [PMC free article] [PubMed] [CrossRef] [Google Scholar]MacKerell A. D. Jr.; Feig M.; Brooks C. L. Improved Treatment of the Protein Backbone in Empirical Force Fields. J. Am. Chem. Soc. 2004, 126, 698–699. 10.1021/ja036959e. [PubMed] [CrossRef] [Google Scholar]Jo J. C.; Kim B. C. Determination of Proper Time Step for Molecular Dynamics Simulation. Bull. Korean Chem. Soc. 2000, 21, 419. [Google Scholar]Hollingsworth S. A.; Dror R. O. Molecular Dynamics Simulation for All. Neuron 2018, 99, 1129–1143. 10.1016/j.neuron.2018.08.011. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Lobanov M. Y.; Bogatyreva N. S.; Galzitskaya O. V. Radius of Gyration as an Indicator of Protein Structure Compactness. Mol. Biol. 2008, 42, 623–628. 10.1134/S0026893308040195. [CrossRef] [Google Scholar]Falsafi-Zadeh S.; Karimi Z.; Galehdari H. VMD DisRg: New User-Friendly Implement for Calculation Distance and Radius of Gyration in VMD Program. Bioinformation 2012, 8, 341–343. 10.6026/97320630008341. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Shao J.; Tanner S. W.; Thompson N.; Cheatham T. E. Clustering Molecular Dynamics Trajectories: 1. Characterizing the Performance of Different Clustering Algorithms. J. Chem. Theory Comput. 2007, 3, 2312–2334. 10.1021/ct700119m. [PubMed] [CrossRef] [Google Scholar]Wu X.; Wang S. Self-Guided Molecular Dynamics Simulation for Efficient Conformational Search. J. Phys. Chem. B 1998, 102, 7238–7250. 10.1021/jp9817372. [CrossRef] [Google Scholar]Wu X.; Brooks B. R. Beta-Hairpin Folding Mechanism of a Nine-Residue Peptide Revealed from Molecular Dynamics Simulations in Explicit Water. Biophys. J. 2004, 86, 1946–1958. 10.1016/S0006-3495(04)74258-7. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Wu X.; Wang S. Helix Folding of an Alanine-Based Peptide in Explicit Water. J. Phys. Chem. B 2001, 105, 2227–2235. 10.1021/jp004048a. [CrossRef] [Google Scholar]Bisol L. W.; Brunstein M. G.; Ottoni G. L.; Ramos F. L. P.; Borba D. L.; Daltio C. S.; de Oliveira R. V.; Paz G. E. G.; de Souza S. E.; Bressan R. A.; Lara D. R. Is Flunarizine a Long-Acting Oral Atypical Antipsychotic? A Randomized Clinical Trial versus Haloperidol for the Treatment of Schizophrenia. J. Clin. Psychiatry 2008, 69, 1572–1579. 10.4088/jcp.v69n1007. [PubMed] [CrossRef] [Google Scholar]Ambrosio C.; Stefanini E. Interaction of Flunarizine with Dopamine D2 and D1 Receptors. Eur. J. Pharmacol. 1991, 197, 221–223. 10.1016/0014-2999(91)90526-v. [PubMed] [CrossRef] [Google Scholar]Wöber C.; Brücke T.; Wöber-Bingöl C.; Asenbaum S.; Wessely P.; Podreka I. Dopamine D2 Receptor Blockade and Antimigraine Action of Flunarizine. Cephalalgia 1994, 14, 235–240. 10.1046/j.1468-2982.1994.014003235.x. [PubMed] [CrossRef] [Google Scholar]Yan Y.; Pan J.; Chen Y.; Xing W.; Li Q.; Wang D.; Zhou X.; Xie J.; Miao C.; Yuan Y.; Zeng W.; Chen D. Increased Dopamine and Its Receptor Dopamine Receptor D1 Promote Tumor Growth in Human Hepatocellular Carcinoma. Cancer Commun. 2020, 694. 10.1002/cac2.12103. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Kafka S. H.; Corbett R. Selective Adenosine A2A Receptor/Dopamine D2 Receptor Interactions in Animal Models of Schizophrenia. Eur. J. Pharmacol. 1996, 295, 147–154. 10.1016/0014-2999(95)00668-0. [PubMed] [CrossRef] [Google Scholar]Belforte J. E.; Magariños-Azcone C.; Armando I.; Buño W.; Pazo J. H. Pharmacological Involvement of the Calcium Channel Blocker Flunarizine in Dopamine Transmission at the Striatum. Parkinsonism Relat. Disord. 2001, 8, 33–40. 10.1016/s1353-8020(01)00006-2. [PubMed] [CrossRef] [Google Scholar]Karsan N.; Palethorpe D.; Rattanawong W.; Marin J. C.; Bhola R.; Goadsby P. J. Flunarizine in Migraine-Related Headache Prevention: Results from 200 Patients Treated in the UK. Eur. J. Neurol. 2018, 25, 811–817. 10.1111/ene.13621. [PubMed] [CrossRef] [Google Scholar]Amery W. K.; Caers L. I.; Aerts T. J. L. Flunarizine, a Calcium Entry Blocker in Migraine Prophylaxis. J. Headache Pain 1985, 25, 249–254. 10.1111/j.1526-4610.1985.hed2505249.x. [PubMed] [CrossRef] [Google Scholar]Piccini P.; Nuti A.; Paoletti A. M.; Napolitano A.; Melis G. B.; Bonuccelli U. Possible Involvement of Dopaminergic Mechanisms in the Antimigraine Action of Flunarizine. Cephalalgia 1990, 10, 3–8. 10.1046/j.1468-2982.1990.1001003.x. [PubMed] [CrossRef] [Google Scholar]Frisch M. J.et al. Gaussian 09, Revision B.01, Gaussian, Inc.2009. [Google Scholar]O’Boyle N. M.; Banck M.; James C. A.; Morley C.; Vandermeersch T.; Hutchison G. R. Open Babel: An Open Chemical Toolbox. J. Cheminf. 2011, 3, 33. 10.1186/1758-2946-3-33. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Jaiteh M.; Rodríguez-Espigares I.; Selent J.; Carlsson J. Performance of Virtual Screening against GPCR Homology Models: Impact of Template Selection and Treatment of Binding Site Plasticity. PLoS Comput. Biol. 2020, 16, e1007680–e1007680. 10.1371/journal.pcbi.1007680. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Morris G. M.; Lim-Wilby M. Molecular Docking. Methods Mol. Biol. 2008, 443, 365–382. 10.1007/978-1-59745-177-2_19. [PubMed] [CrossRef] [Google Scholar]Pettersen E. F.; Goddard T. D.; Huang C. C.; Couch G. S.; Greenblatt D. M.; Meng E. C.; Ferrin T. E. UCSF Chimera--a Visualization System for Exploratory Research and Analysis. J. Comput. Chem. 2004, 25, 1605–1612. 10.1002/jcc.20084. [PubMed] [CrossRef] [Google Scholar]Guner O.; Clement O.; Kurogi Y. Pharmacophore Modeling and Three Dimensional Database Searching for Drug Design Using Catalyst: Recent Advances. Curr. Med. Chem. 2004, 11, 2991–3005. 10.2174/0929867043364036. [PubMed] [CrossRef] [Google Scholar]Cheng F.; Li W.; Zhou Y.; Shen J.; Wu Z.; Liu G.; Lee P. W.; Tang Y. AdmetSAR: A Comprehensive Source and Free Tool for Assessment of Chemical ADMET Properties. J. Chem. Inf. Model. 2012, 52, 3099–3105. 10.1021/ci300367a. [PubMed] [CrossRef] [Google Scholar]Anbu V.; Karthick T.; Vijayalakshmi K. A. Combined Experimental and Computational Approach for the Vibrational Characteristics and Theoretical Evaluation of Binding Activities and ADME Descriptors of 2,6-Di-Tert-Butyl-p-Cresol. Polycyclic Aromat. Compd. 2020, 1–17. 10.1080/10406638.2020.1768567. [CrossRef] [Google Scholar]Newby D.; Freitas A. A.; Ghafourian T. Decision Trees to Characterise the Roles of Permeability and Solubility on the Prediction of Oral Absorption. Eur. J. Med. Chem. 2015, 90, 751–765. 10.1016/j.ejmech.2014.12.006. [PubMed] [CrossRef] [Google Scholar]Ursu O.; Rayan A.; Goldblum A.; Oprea T. I. Understanding Drug-Likeness. WIREs Comput. Mol. Sci. 2011, 1, 760–781. 10.1002/wcms.52. [CrossRef] [Google Scholar]Rankovic Z. CNS Drug Design: Balancing Physicochemical Properties for Optimal Brain Exposure. J. Med. Chem. 2015, 58, 2584–2608. 10.1021/jm501535r. [PubMed] [CrossRef] [Google Scholar]Ghose A. K.; Herbertz T.; Hudkins R. L.; Dorsey B. D.; Mallamo J. P. Knowledge-Based, Central Nervous System (CNS) Lead Selection and Lead Optimization for CNS Drug Discovery. ACS Chem. Neurosci. 2012, 3, 50–68. 10.1021/cn200100h. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Zafar F.; Gupta A.; Thangavel K.; Khatana K.; Sani A. A.; Ghosal A.; Tandon P.; Nishat N. Physicochemical and Pharmacokinetic Analysis of Anacardic Acid Derivatives. ACS Omega 2020, 5, 6021–6030. 10.1021/acsomega.9b04398. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Singh N.; Kumar N.; Rathee G.; Sood D.; Singh A.; Tomar V.; Dass S. K.; Chandra R. Privileged Scaffold Chalcone: Synthesis, Characterization and Its Mechanistic Interaction Studies with BSA Employing Spectroscopic and Chemoinformatics Approaches. ACS Omega 2020, 5, 2267–2279. 10.1021/acsomega.9b03479. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Al-Blewi F.; Rezki N.; Naqvi A.; Qutb Uddin H.; Al-Sodies S.; Messali M.; Aouad M. R.; Bardaweel S. A Profile of the In Vitro Anti-Tumor Activity and In Silico ADME Predictions of Novel Benzothiazole Amide-Functionalized Imidazolium Ionic Liquids. Int. J. Mol. Sci. 2019, 20, 2865. 10.3390/ijms20122865. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Ahmad S. S.; Sinha M.; Ahmad K.; Khalid M.; Choi I. Study of Caspase 8 Inhibition for the Management of Alzheimer’s Disease: A Molecular Docking and Dynamics Simulation. Molecules 2020, 25, 2071. 10.3390/molecules25092071. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Loureiro D. R. P.; Soares J. X.; Costa J. C.; Magalhães Á. F.; Azevedo C. M. G.; Pinto M. M. M.; Afonso C. M. M. Structures, Activities and Drug-Likeness of Anti-Infective Xanthone Derivatives Isolated from the Marine Environment: A Review. Molecules 2019, 24, 243. 10.3390/molecules24020243. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Kehinde I.; Ramharack P.; Nlooto M.; Gordon M. The Pharmacokinetic Properties of HIV-1 Protease Inhibitors: A Computational Perspective on Herbal Phytochemicals. Heliyon 2019, 5, e02565–e02565. 10.1016/j.heliyon.2019.e02565. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Garg P.; Verma J.; Roy N. In Silico Modeling for Blood—Brain Barrier Permeability Predictions. Drug Absorpt. Stud. 2008, 510–556. 10.1007/978-0-387-74901-3_22. [CrossRef] [Google Scholar]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. 10.1016/0010-4655(95)00042-E. [CrossRef] [Google Scholar]Hess B.; Kutzner C.; van der Spoel D.; Lindahl E. GROMACS 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation. J. Chem. Theory Comput. 2008, 4, 435–447. 10.1021/ct700301q. [PubMed] [CrossRef] [Google Scholar]van der Spoel D.; Lindahl E.; Hess B.; Groenhof G.; Mark A. E.; Berendsen H. J. C. GROMACS: Fast, Flexible, and Free. J. Comput. Chem. 2005, 26, 1701–1718. 10.1002/jcc.20291. [PubMed] [CrossRef] [Google Scholar]Michino M.; Boateng C. A.; Donthamsetti P.; Yano H.; Bakare O. M.; Bonifazi A.; Ellenberger M. P.; Keck T. M.; Kumar V.; Zhu C.; Verma R.; Deschamps J. R.; Javitch J. A.; Newman A. H.; Shi L. Toward Understanding the Structural Basis of Partial Agonism at the Dopamine D3 Receptor. J. Med. Chem. 2017, 60, 580–593. 10.1021/acs.jmedchem.6b01148. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Wassenaar T. A.; Mark A. E. The Effect of Box Shape on the Dynamic Properties of Proteins Simulated under Periodic Boundary Conditions. J. Comput. Chem. 2006, 27, 316–325. 10.1002/jcc.20341. [PubMed] [CrossRef] [Google Scholar]Ali J.; Camilleri P.; Brown M. B.; Hutt A. J.; Kirton S. B. In Silico Prediction of Aqueous Solubility Using Simple QSPR Models: The Importance of Phenol and Phenol-like Moieties. J. Chem. Inf. Model. 2012, 52, 2950–2957. 10.1021/ci300447c. [PubMed] [CrossRef] [Google Scholar]Okuda S.; Yamada T.; Hamajima M.; Itoh M.; Katayama T.; Bork P.; Goto S.; Kanehisa M. KEGG Atlas Mapping for Global Analysis of Metabolic Pathways. Nucleic Acids Res. 2008, 36, W423–W426. 10.1093/nar/gkn282. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Kanehisa M.; Goto S.; Sato Y.; Furumichi M.; Tanabe M. KEGG for Integration and Interpretation of Large-Scale Molecular Data Sets. Nucleic Acids Res. 2012, 40, D109–D114. 10.1093/nar/gkr988. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Kanehisa M.; Goto S.; Furumichi M.; Tanabe M.; Hirakawa M. KEGG for Representation and Analysis of Molecular Networks Involving Diseases and Drugs. Nucleic Acids Res. 2010, 38, D355–D360. 10.1093/nar/gkp896. [PMC free article] [PubMed] [CrossRef] [Google Scholar]http://purl.org/coar/resource_type/c_2df8fbb1ORIGINALIn_Silico_Repositioning_of_Dopamine_Modulators_wit.pdfIn_Silico_Repositioning_of_Dopamine_Modulators_wit.pdfapplication/pdf8241127https://repositorio.uniatlantico.edu.co/bitstream/20.500.12834/1158/1/In_Silico_Repositioning_of_Dopamine_Modulators_wit.pdf69eea69653b7566cc82f5236c24c2cfaMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8914https://repositorio.uniatlantico.edu.co/bitstream/20.500.12834/1158/2/license_rdf24013099e9e6abb1575dc6ce0855efd5MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81306https://repositorio.uniatlantico.edu.co/bitstream/20.500.12834/1158/3/license.txt67e239713705720ef0b79c50b2ececcaMD5320.500.12834/1158oai:repositorio.uniatlantico.edu.co:20.500.12834/11582022-12-20 14:13:32.261DSpace de la Universidad de Atlánticosysadmin@mail.uniatlantico.edu.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

Publicaciones similares