327
Views
7
CrossRef citations to date
0
Altmetric
Review

Quantitative structure–activity relationship models for compounds with anticonvulsant activity

&
Pages 653-665 | Received 09 Jan 2019, Accepted 26 Apr 2019, Published online: 10 May 2019

References

  • Pakpoor J, Goldacre M. The increasing burden of mortality from neurological diseases. Nat Rev Neurol. 2017;13:518–519.
  • GBD 2015 Neurological Disorders Collaborator Group. Global, regional, and national burden of neurological disorders during 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet. 2017;16:877–897.
  • Löscher W, Schmidt D. Modern antiepileptic drug development has failed to deliver: ways out of the current dilemma. Epilepsia. 2011;52:657–678.
  • Löscher W, Klitgaard H, Twyman RE, et al. New avenues for anti-epileptic drug discovery and development. Nat Rev Drug Discov. 2013;12:757–776.
  • Löscher W. The search for new screening models of pharmacoresistant epilepsy: is induction of acute seizures in epileptic rodents a suitable approach? Neurochem Res. 2017;42:1926–1938.
  • Margineanu DG. Systems biology, complexity, and the impact on antiepileptic drug discovery. Epilepsy Behav. 2014;38:131–142.
  • Talevi A. Computational approaches for innovative antiepileptic drug discovery. Expert Opin Drug Discov. 2016;11:1001–1016.
  • Verma J, Khedkar VM, Coutinho EC. 3D-QSAR in drug design–a review. Curr Top Med Chem. 2010;10:95–115.
  • Neves BJ, Braga RC, Melo-Fliho CC, et al. QSAR-based virtual screening: advances and applications in drug discovery. Front Pharmacol. 2018;9:1275.
  • Lewis RA. A general method for exploiting QSAR models in lead optimization. J Med Chem. 2005;48:1638–1648.
  • Todeschini R, Consonni V. Handbook of Molecular Descriptors. Weinheim: Wiley-VCH; 2008.
  • Ferreira RS, Simeonov A, Jadhav A, et al. Complementarity between a docking and a high-throughput screen in discovering new cruzain inhibitors. J Med Chem. 2010;53:4891−905.
  • Deng N, Forli S, Peng H, et al. Distinguishing binders from false positives by free energy calculations: fragment screening against the flap site of HIV protease. J Phys Chem B. 2015;119:976–988.
  • Ramirez D, Caballero J. Is it reliable to use common molecular docking methods for comparing the binding affinities of enantiomer pairs for their protein target? Int J Mol Sci. 2016;17:525.
  • Chen H, Engkvist O, Wang Y, et al. The rise of deep learning in drug discovery. Drug Discov Today. 2018;23:1241–1250.
  • Simões RS, Maltarollo VG, Oliveira PR, et al. Transfer and multi-task learning in QSAR modeling: advances and challenges. Front Pharmacol. 2018;9:74.
  • Lo YC, Rensi SE, Torng W, et al. Machine learning in chemoinformatics and drug discovery. Drug Discov Today. 2018;23:1538–1546.
  • Cruz-Monteagudo M, Schürer S, Tejera E, et al. Systemic QSAR and phenotypic virtual screening: chasing butterflies in drug Discovery. Drug Discov Today. 2017;22:994–1007.
  • Mintzer S, French JA, Perucca E, et al. Is a separate monotherapy indication warranted for antiepileptic drugs?. Lancet Neurol. 2015;14:1229–1240.
  • Beydoun A, Kutluay E. Conversion to monotherapy: clinical trials in patients with refractory partial seizures. Neurology. 2003;60:S13–25.
  • Talevi A. Multi-target pharmacology: possibilities and limitations of the “skeleton key approach” from a medicinal chemist perspective. Front Pharmacol. 2015;6:20564.
  • Croston GE. The utility of target-based discovery. Expert Opin Drug Discov. 2017;12:427–429.
  • Ramsay RR, Popovic-Nikolic MR, Nikolic K, et al. A perspective on multi-target drug discovery and design for complex diseases. Clin Transl Med. 2018;7:3.
  • Hopkins AL. Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol. 2008;11:682–690.
  • Hitaoka S, Chuman H. Revisiting the Hansch–fujita approach and development of a fundamental QSAR. J Pestic Sci. 2013;38:60–67.
  • Kubinyi H. QSAR and 3D QSAR in drug design Part 1: methodology. Drug Discov Today. 1997;2:457–467.
  • Vijayan RSK, Bera I, Prabu M, et al. Combinatorial library enumeration and lead hopping using comparative interaction fingerprint analysis and classical 2D QSAR methods for seeking novel GABAA α3 modulators. J Chem Inf Model. 2009;49:2498–2511.
  • Langer T, Byrant SD. 3D quantitative structure-property relationships. In: Wermuth C, editor. The Practice of Medicinal Chemistry. London: Academic Press; 2008. p. 587–604.
  • Sippl W. 3D QSAR: applications, recent advances, and limitations. In: Puzyn T, Leszczynski J, Cronin MT, editors. Recent Advances in QSAR Studies. Method and Applications. Dordrecht: Springer; 2010. p. 103–125.
  • Fujita T, Winkler DA. Understanding the Roles of the “Two QSARs”. J Chem Inf Model. 2016;56:269–274.
  • Richard AM, Benigni R. AI and SAR approaches for predicting chemical carcinogenicity: survey and status report. SAR QSAR Environ Res. 2002;13:1–19.
  • Myshkin E, Brennan R, Khasanova T, et al. Prediction of organ toxicity endpoints by QSAR modeling based on precise chemical histopathology annotations. Chem Biol Drug Des. 2012;80:406–416.
  • Senese CL, Duca J, Pan D, et al. 4D-fingerprints, universal QSAR and QSPR descriptors. J Chem Inf Comput Sci. 2004;44:1526–1539.
  • Gálvez J, Gálvez-Llompart M, García-Domenech R. Introduction to molecular topology: basic concepts and application to drug design. Curr Comput Aided Drug Des. 2012;8:196–223.
  • Dudek AZ, Arodz T, Gálvez J. Computational methods in developing quantitative structure-activity relationships (QSAR): a review. Comb Chem High Throughput Screen. 2006;9:213–228.
  • De Julián-Ortiz JV, Gálvez J, Muñoz-Collado C, et al. Virtual combinatorial syntheses and computational screening of new potential anti-herpes compounds. J Med Chem. 1999;42:3308–3314.
  • Ríos-Santamarina I, García-Domenech R, Gálvez J, et al. New bronchodilators selected by molecular topology. Bioorg Med Chem Lett. 1998;8:477–482.
  • De Gregorio Alapont C, García-Domenech R, Gálvez J, et al. Molecular topology: A useful tool for the search of new antibacterials. Bioorg Med Chem Lett. 2000;10:2033–2036.
  • Gálvez J, García-Domenech R, Gómez-Lechón MJ, et al. Use of molecular topology in the selection of new cytostatic drugs. J Mol Struct THEOCHEM. 2000;504:241–248.
  • García-Domenech R, Rios-Santamarina I, Catalá A, et al. Application of molecular topology to the prediction of antifungal activity for a set of dication substituted carbazoles, furans and benzimidazoles. J Mol Struct THEOCHEM. 2003;624:97–107.
  • Calabuig C, Antón-Fos GM, Gálvez J, et al. New hypoglycaemic agents selected by molecular topology. J Pharm. 2004;278:111–118.
  • Marrero-Ponce Y, Iyarreta-Veitía M, Montero-Torres A, et al. Ligand-based virtual screening and in silico design of new antimalarial compounds using nonstochastic and stochastic total and atom-type quadratic maps. J Chem Inf Model. 2005;45:1082–1100.
  • Mahmoudi N, Garcia-Domenech R, Gálvez J, et al. New active drugs against liver stages of Plasmodium predicted by molecular topology. Antimicrob Agents Chemother. 2008;52:1215–1220.
  • Gálvez-Llompart M, Gálvez J, García-Domenech R, et al. Modeling drug-induced anorexia by molecular topology. J Chem Inf Model. 2012;52:1337–1344.
  • Gálvez-Llompart M, Recio MC, García-Domenech R. Topological virtual screening: A way to find new compounds active in ulcerative colitis by inhibiting NF-κB. Mol Divers. 2011;15:917–926.
  • Gálvez-Llompart M, Recio MC, García-Domenech R, et al. Molecular topology: A strategy to identify novel compounds against ulcerative colitis. Mol Divers. 2017;21:219–234.
  • Marrero-Ponce Y, Castillo-Garit JA, Olazabal E, et al. TOMOCOMD-CARDD, a novel approach for computer-aided “rational” drug design: I. Theoretical and experimental assessment of a promising method for computational screening and in silico design of new anthelmintic compounds. J Comput Aided Mol Des. 2004;18:615–634.
  • Vega MC, Montero-Torres A, Marrero-Ponce Y, et al. New ligand-based approach for the discovery of antitrypanosomal compounds. Bioorg Med Chem Lett. 2006;16:1898–1904.
  • Marrero-Ponce Y, Meneses-Marcel A, Rivera-Borroto OM, et al. Bond-based linear indices in QSAR: computational discovery of novel anti-trichomonal compounds. J Comput Aided Mol Des. 2008;22:523–540.
  • Bruno-Blanch L, Gálvez J, García-Domenech R. Topological virtual screening: A way to find new anticonvulsant drugs from chemical diversity. Bioorg Med Chem Lett. 2003;13:2749–2754.
  • Talevi A, Bellera CL, Castro EA, et al. A successful virtual screening application: prediction of anticonvulsant activity in MES test of widely used pharmaceutical and food preservatives methylparaben and propylparaben. J Comput Aided Mol Des. 2007;21:527–538.
  • Morales JF, Alberca LN, Chuguransky S, et al. Molecular topology and other promiscuity determinants as predictors of therapeutic class - A theoretical framework to guide drug repositioning?. Curr Top Med Chem. 2018;18:1110–1122.
  • Hu Y, Bajorath J. Polypharmacology directed compound data mining: identification of promiscuous chemotypes with different activity profiles and comparison to approved drugs. J Chem Inf Model. 2010;50:2112–2118.
  • Talevi, A. Network Pharmacology and Epilepsy. In: Talevi A, Rocha L, editors. Antiepileptic Drug Discovery. New York: Springer; 2016. p. 351–364
  • Yang Y, Chen H, Nilsson I, et al. Investigation of the relationship between topology and selectivity for druglike molecules. J Med Chem. 2010;53:7709–7714.
  • Bianchi MT, Pathmanathan J, Cash SS. From ion channels to complex networks: magic bullet versus magic shotgun approaches to anticonvulsant pharmacotherapy. Med Hypotheses. 2009;72:297–305.
  • Speck-Planche A, Cordeiro MN. Multitasking models for quantitative structure-biological effect relationships: current status and future perspectives to speed up drug discovery. Expert Opin Drug Discov. 2015;10:245–256.
  • Luan F, Cordeiro MN, Alonso N, et al. TOPS-MODE model of multiplexing neuroprotective effects of drugs and experimental-theoretic study of new 1,3-rasagiline derivatives potentially useful in neurodegenerative diseases. Bioorg Med Chem. 2013;21:1870–1879.
  • Speck-Planche A, Kleandrova VV, Ruso JM, et al. First multitarget chemo-bioinformatic model to enable the discovery of antibacterial peptides against multiple gram-positive pathogens. J Chem Inf Model. 2016;56:588–598.
  • Steinlein OK. Genetic and epilepsy. Dialogues Clin Neurosci. 2008;10:29–38.
  • Koeleman BPC. What do genetic studies tell us about the heritable basis of common epilepsy? Polygenic or complex epilepsy?. Neurosci Lett. 2018;667:10–16.
  • Tropsha A, Golbraikh A. Predictive QSAR modeling workflow, model applicability domains, and virtual screening. Curr Pharm Des. 2007;13:3494–3504.
  • Esneault E, Peyon G, Castagné V. Efficacy of anticonvulsant substances in the 6Hz seizure test: comparison of two rodent species. Epilepsy Res. 2017;134:9–15.
  • Morimoto K, Fahnestock M, Racine RJ. Kindling and status epilepticus models of epilepsy: rewiring the brain. Prog Neurobiol. 2004;73:1–60.
  • Brown ML, Zha CC, Van Dyke CC, et al. Comparative molecular field analysis of hydantoin binding to the neuronal voltage-dependent sodium channel. J Med Chem. 1999;42:1537–1545.
  • Lenkowski PW, Batts TW, Smith MD, et al. A pharmacophore derived phenytoin analogue with increased affinity for slow inactivated sodium channels exhibits a desired anticonvulsant profile. Neuropharmacology. 2007;52:1044–1054.
  • Wang Y, Jones PJ, Batts TW, et al. Ligand-based design and synthesis of novel sodium channel blockers from a combined phenytoin-lidocaine pharmacophore. Bioorg Med Chem. 2009;17:7064–7072.
  • Brown ML, Eidam HA, Paige M, et al. Comparative molecular field analysis and synthetic validation of a hydroxyamide-propofol binding and functional block of neuronal voltage-dependent sodium channels. Bioorg Med Chem. 2009;17:7056–7063.
  • Zha C, Brown GB, Brouillette WJ. A highly predictive 3D-QSAR model for binding to the voltage-gated sodium channel: design of potent new ligands. Bioorg Med Chem. 2014;22:95–104.
  • Gavernet L, Dominguez Cabrera MJ, Bruno-Blanch LE, et al. 3D-QSAR design of novel antiepileptic sulfamides. Bioorg Med Chem. 2007;15:1556–1567.
  • Gavernet L, Elvira JE, Samaja GA, et al. Synthesis and anticonvulsant activity of amino acid-derived sulfamides. J Med Chem. 2009;52:1592–1601.
  • Shen M, LeTiran A, Xiao Y, et al. Quantitative structure-activity relationship analysis of functionalized amino acid anticonvulsant agents using k nearest neighbor and simulated annealing PLS methods. J Med Chem. 2002;45:2811–2823.
  • Shen M, Béguin C, Golbraikh A, et al. Application of predictive QSAR models to database mining: identification and experimental validation of novel anticonvulsant compounds. J Med Chem. 2004;47:2356–2364.
  • Golbraikh A, Shen M, Xiao Z, et al. Rational selection of training and test sets for the development of validated QSAR models. J Comput Aided Mol Des. 2003;17:241–253.
  • Zhang S, Golbraikh A, Oloff S, et al. A novel automated lazy learning QSAR (ALL-QSAR) approach: method development, applications, and virtual screening of chemical databases using validated ALL-QSAR models. J Chem Inf Model. 2006;46:1984–1995.
  • Talevi A, Cravero MS, Castro EA, et al. Discovery of anticonvulsant activity of abietic acid through application of linear discriminant analysis. Bioorg Med Chem Lett. 2007;17:1684–1690.
  • Talevi A, Enrique AV, Bruno-Blanch LE. Anticonvulsant activity of artificial sweeteners: A structural link between sweet-taste receptor T1R3 and brain glutamate receptors. Bioorg Med Chem Lett. 2012;22:4072–4074.
  • Villalba ML. Palestro P1, Ceruso M et al. Sulfamide derivatives with selective carbonic anhydrase VII inhibitory action. Bioorg Med Chem. 2916;24:894–901.
  • Lara-Valderrábano L, Rocha L, Galván EJ. Propylparaben reduces the excitability of hippocampal neurons by blocking sodium channels. Neurotoxicology. 2016;57:183–193.
  • Lara-Valderrábano L, Galván EJ, Rocha L. Propylparaben suppresses epileptiform activity in hippocampal CA1 pyramidal cells in vitro. Epilepsy Res. 2017;136:126–129.
  • Santana-Gómez CE, Orozco-Suárez SA2, Talevi A, et al. Propylparaben applied after pilocarpine-induced status epilepticus modifies hippocampal excitability and glutamate release in rats. Neurotoxicology. 2017;59:110–120.
  • Santana-Gómez CE, Valle-Dorado MG, Domínguez-Valentín AE, et al. Neuroprotective effects of levetiracetam, both alone and combined with propylparaben, in the long-term consequences induced by lithium-pilocarpine status epilepticus. Neurochem. 2018;120:224–232.
  • Pisera-Fuster A, Otero S, Talevi A, et al. Anticonvulsant effect of sodium cyclamate and propylparaben on pentylenetetrazol-induced seizures in zebrafish. Synapse. 2017;71:e21961.
  • Gavernet L, Talevi A, Castro EA, et al. A combined virtual screening 2D and 3D QSAR methodology for the selection of new anticonvulsant candidates from a natural product library. QSAR Comb Sci. 2008;27:1120–1129.
  • Di Ianni ME, Enrique AV, Palestro PH, et al. Several new diverse anticonvulsant agents discovered in a virtual screening campaign aimed at novel antiepileptic drugs to treat refractory epilepsy. J Chem Inf Model. 2012;52:3323–3330.
  • Romero-Durán FJ, Alonso N, Yañez M, et al. Brain-inspired cheminformatics of drug-target brain interactome, synthesis, and assay of TVP1022 derivatives. Neuropharmacology. 2016;103:270–278.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.