Skip to main content
SpringerLink
Log in

QSAR study of Nav1.7 antagonists by multiple linear regression method based on genetic algorithm (GA–MLR)

Medicinal Chemistry Research volume 23, pages 2264–2276 (2014)Cite this article

Abstract

In this work, a quantitative structure–activity relationship study was developed to predict the NaV1.7 antagonist activities. A data set consisted of 36 compounds with known NaV1.7 antagonist activities was split into two subsets of training set and test set using hierarchical clustering technique. To select the most respective descriptors among the pool of descriptors, genetic algorithm was applied. The model based on selected descriptors through genetic algorithm (GA) was built by employing multiple linear regression (MLR) method. The squared correlation coefficient (\( R_{\text{train}}^{2} \)) of 0.813, squared cross-validated correlation coefficient for leave-one-out (\( Q_{\text{LOO}}^{2} \)) of 0.699 and root mean square error of 0.214 were calculated for the training set compounds by GA–MLR model. The proposed model performed good predictive ability when it was verified by internal and external validation tests. The results of predictive model can lead to design better compounds with high NaV1.7 antagonist activities.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3

References

  • Agrawal VK, Khadikar PV (2001) QSAR prediction of toxicity of nitrobenzenes. Bioorg Med Chem 9:3035–3040. doi:10.1016/S0968-0896(01)00211-5

    Article  CAS  PubMed  Google Scholar 

  • Beheshti A, Pourbasheer E, Nekoei M, Vahdani S, (2012) QSAR modeling of antimalarial activity of urea derivatives using genetic algorithm–multiple linear regressions. J Saudi Chem Soc. doi:10.1016/j.jscs.2012.07.019

    Google Scholar 

  • Bregman H, Nguyen HN, Feric E, Ligutti J, Liu D, McDermott JS, Wilenkin B, Zou A, Huang L, Li X, McDonough SI, DiMauro EF (2012) The discovery of aminopyrazines as novel, potent Nav1.7 antagonists: hit-to-lead identification and SAR. Bioorg Med Chem Lett 22:2033–2042. doi:10.1016/j.bmcl.2012.01.023

    Article  CAS  PubMed  Google Scholar 

  • Catterall WA, Dib-Hajj S, Meisler MH, Pietrobon D (2008) Inherited neuronal ion channelopathies: new windows on complex neurological diseases. J Neurosci 28:11768–11777. doi:10.1523/jneurosci.3901-08.2008

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Consonni V, Todeschini R, Pavan M (2002) Structure/response correlations and similarity/diversity analysis by GETAWAY descriptors. 1. Theory of the novel 3D molecular descriptors. J Chem Inf Comput Sci 42:682–692. doi:10.1021/ci015504a

    Article  CAS  PubMed  Google Scholar 

  • Dib-Hajj SD, Tyrrell L, Black JA, Waxman SG (1998) NaN, a novel voltage-gated Na channel, is expressed preferentially in peripheral sensory neurons and down-regulated after axotomy. Proc Nat Acad Sci 95:8963–8968

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Djouhri L, Newton R, Levinson SR, Berry CM, Carruthers B, Lawson SN (2003) Sensory and electrophysiological properties of guinea-pig sensory neurones expressing Nav 1.7 (PN1) Na+ channel α subunit protein. J Physiol 546:565–576. doi:10.1113/jphysiol.2002.026559

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Dubes R, Jain AK (1976) Clustering techniques: The user’s dilemma. Patt Recogn 8:247–260. doi:10.1016/0031-3203(76)90045-5

    Article  Google Scholar 

  • Eriksson L, Johansson E, Müller M, Wold S (2000) On the selection of the training set in environmental QSAR analysis when compounds are clustered. J Chemomet 14:599–616

    Article  CAS  Google Scholar 

  • Farrar JT, Portenoy RK, Berlin JA, Kinman JL, Strom BL (2000) Defining the clinically important difference in pain outcome measures. Pain 88:287–294

    Article  CAS  PubMed  Google Scholar 

  • Felts PA, Yokoyama S, Dib-Hajj S, Black JA, Waxman SG (1997) Sodium channel α-subunit mRNAs I, II, III, NaG, Na6 and hNE (PN1): different expression patterns in developing rat nervous system. Mol Br Res 45:71–82. doi:10.1016/S0169-328X(96)00241-0

    Article  CAS  Google Scholar 

  • Fischer TZ, Waxman SG (2010) Familial pain syndromes from mutations of the Nav1.7 sodium channel. Anna New York Acad Sci 1184:196–207

    Article  CAS  Google Scholar 

  • Fischer TZ, Gilmore ES, Estacion M, Eastman E, Taylor S, Melanson M, Dib-Hajj SD, Waxman SG (2009) A novel Nav1.7 mutation producing carbamazepine-responsive erythromelalgia. Ann Neurol 65:733–741

    Article  PubMed Central  PubMed  Google Scholar 

  • George AL (2005) Inherited disorders of voltage-gated sodium channels. JCI 115:1990–1999

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Goldberg YP, MacFarlane J, MacDonald ML, Thompson J, Dube MP, Mattice M, Fraser R, Young C, Hossain S, Pape T, Payne B, Radomski C, Donaldson G, Ives E, Cox J, Younghusband HB, Green R, Duff A, Boltshauser E, Grinspan GA, Dimon JH, Sibley BG, Andria G, Toscano E, Kerdraon J, Bowsher D, Pimstone SN, Samuels ME, Sherrington R, Hayden MR (2007) Loss-of-function mutations in the Nav1.7 gene underlie congenital indifference to pain in multiple human populations. Clin Genet 71:311–319

    Article  CAS  PubMed  Google Scholar 

  • Habibi-Yangjeh A, Pourbasheer E, Danandeh-Jenagharad M (2008) Prediction of basicity constants of various pyridines in aqueous solution using a principal component-genetic algorithm-artificial neural network. Monatsh Chem 139:1423–1431

    Article  CAS  Google Scholar 

  • Habibi-Yangjeh A, Pourbasheer E, Danandeh-Jenagharad M (2009) Application of principal component-genetic algorithm-artificial neural network for prediction acidity constant of various nitrogen-containing compounds in water. Monatsh Chem 140:15–27

    Article  CAS  Google Scholar 

  • Hall LH, Kier LB (1995) Electrotopological state indices for atom types: a Novel combination of electronic, topological, and valence state information. J Chem Inf Comput Sci 35:1039–1045

    Article  CAS  Google Scholar 

  • Hansch C, Fujita T (1964) p-σ-π analysis. A method for the correlation of biological activity and chemical structure. J Am Chem Soc 86:1616–1626

    Article  CAS  Google Scholar 

  • Hocking RR (1976) A biometrics invited paper. The analysis and selection of variables in linear regression. Biometrics 32:1–49

    Article  Google Scholar 

  • Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Michigan

    Google Scholar 

  • Holm AN, Rich A, Miller SM, Strege P, Ou Y, Gibbons SJ, Sarr MG, Szurszewski JH, Rae JL, Farrugia G (2002) Sodium current in human jejunal circular smooth muscle cells. Gastroenterology 122:178–187

    Article  CAS  PubMed  Google Scholar 

  • HyperChem (2002) Molecular modeling system. Hypercube Inc., Gainesville

    Google Scholar 

  • Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31:264–323

    Article  Google Scholar 

  • Khajehsharifi H, Sadeghi M, Pourbasheer E (2009) Spectrophotometric simultaneous determination of creatine, creatinine, and uric acid in real samples by orthogonal signal correction–partial least squares regression. Monatsh Chem 140:685–691

    Article  CAS  Google Scholar 

  • Leardi R, Boggia R, Terrile M (1992) Genetic algorithms as a strategy for feature selection. J Chemomet 6:267–281

    Article  CAS  Google Scholar 

  • Li W, Tang Y, Zheng Y-L, Qiu Z-B (2006) Molecular modeling and 3D-QSAR studies of indolomorphinan derivatives as kappa opioid antagonists. Bioorg Med Chem 14:601–610

    Article  CAS  PubMed  Google Scholar 

  • Mathworks (2005) Genetic algorithm and direct search toolbox users guide. The Mathworks Inc., USA

    Google Scholar 

  • Nassar MA, Stirling LC, Forlani G, Baker MD, Matthews EA, Dickenson AH, Wood JN (2004) Nociceptor-specific gene deletion reveals a major role for Nav1.7 (PN1) in acute and inflammatory pain. Proc Nat Acad Sci USA 101:12706–12711

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Pourbasheer E, Riahi S, Ganjali MR, Norouzi P (2009) Application of genetic algorithm-support vector machine (GA-SVM) for prediction of BK-channels activity. Eur J Med Chem 44:5023–5028

    Article  CAS  PubMed  Google Scholar 

  • Pourbasheer E, Riahi S, Ganjali MR, Norouzi P (2010) QSAR study on melanocortin-4 receptors by support vector machine. Eur J Med Chem 45:1087–1093

    Article  CAS  PubMed  Google Scholar 

  • Pourbasheer E, Beheshti A, Khajehsharifi H, Ganjali MR, Norouzi P (2012) QSAR study on hERG inhibitory effect of kappa opioid receptor antagonists by linear and non-linear methods. Med Chem Res. doi:10.1007/s00044-012-0412-4

    Google Scholar 

  • Pourbasheer E, Aalizadeh R, Ganjali M, Norouzi P (2013) QSAR study of IKKβ inhibitors by the genetic algorithm: multiple linear regressions. Med Chem Res. doi:10.1007/s00044-013-0611-7

    Google Scholar 

  • Riahi S, Ganjali M, Pourbasheer E, Norouzi P (2008) QSRR Study of GC retention indices of essential-oil compounds by multiple linear regression with a genetic algorithm. Chroma 67:917–922

    Article  CAS  Google Scholar 

  • Rush AM, Dib-Hajj SD, Liu S, Cummins TR, Black JA, Waxman SG (2006) A single sodium channel mutation produces hyper- or hypoexcitability in different types of neurons. Proc Nat Acad Sci 103:8245–8250

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Saleh S, Yeung SYM, Prestwich S, Pucovský V, Greenwood I (2005) Electrophysiological and molecular identification of voltage-gated sodium channels in murine vascular myocytes. J Physiol 568:155–169

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Shen Q, Lü Q-Z, Jiang J-H, Shen G-L, Yu R-Q (2003) Quantitative structure–activity relationships (QSAR): studies of inhibitors of tyrosine kinase. Eur J Pharm Sci 20:63–71

    Article  CAS  PubMed  Google Scholar 

  • Timmerman H (1995) New developments and applications: QSAR and drug design. In: Fujita T (ed) Pharmacochemistry Library. Elsevier, Amsterdam, pp 413–450

    Google Scholar 

  • Todeschini R, Consonni V (2000) Handbook of molecular descriptors. Wiley, Weinheim

    Book  Google Scholar 

  • Todeschini R, Bettiol C, Giurin G, Gramatica P, Miana P, Argese E (1996) Modeling and prediction by using WHIM descriptors in QSAR studies: submitochondrial particles (SMP) as toxicity biosensors of chlorophenols. Chemosphere 33:71–79

    Article  CAS  Google Scholar 

  • Todeschini R, Consonni V, Mauri A, Pavan M (2005) DRAGON software for the calculation of molecular descriptors, 53 edn. Talete srl, Milan

  • Toledo-Aral JJ, Moss BL, He Z-J, Koszowski AG, Whisenand T, Levinson SR, Wolf JJ, Silos-Santiago I, Halegoua S, Mandel G (1997) Identification of PN1, a predominant voltage-dependent sodium channel expressed principally in peripheral neurons. Proc Nat Acad Sci 94:1527–1532

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Tropsha A, Gramatica P, Gombar VK (2003) The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb Sci 22:69–77

    Article  CAS  Google Scholar 

  • Vapnik V (1998) Statistical learning theory. Wiley, New York

    Google Scholar 

  • Waxman SG (2007) Nav1.7, its mutations, and the syndromes that they cause. Neurology 69:505–507

    Article  PubMed  Google Scholar 

  • Zhou YX, Xu L, Wu YP, Liu BL (1999) A QSAR study of the antiallergic activities of substituted benzamides and their structures. Chemometr Intell Lab 45:95–100

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Young Researchers and Elite Club, Ardabil Branch, Islamic Azad University, Ardabil, Iran

    Eslam Pourbasheer & Reza Aalizadeh

  2. Center of Excellence in Electrochemistry, Faculty of Chemistry, University of Tehran, P.O. Box 14155-6455, Tehran, Iran

    Mohammad Reza Ganjali & Parviz Norouzi

  3. Biosensor Research Center, Endocrinology and Metabolism Research Center, Tehran University of Medical Sciences, Tehran, Iran

    Mohammad Reza Ganjali & Parviz Norouzi

  4. Department of Chemistry, University of Athens, Athens, 15784, Greece

    Javad Shadmanesh & Constantinos Methenitis

Authors
  1. Eslam Pourbasheer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Reza Aalizadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammad Reza Ganjali
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Parviz Norouzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Javad Shadmanesh
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Constantinos Methenitis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eslam Pourbasheer.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Pourbasheer, E., Aalizadeh, R., Ganjali, M.R. et al. QSAR study of Nav1.7 antagonists by multiple linear regression method based on genetic algorithm (GA–MLR). Med Chem Res 23, 2264–2276 (2014). https://doi.org/10.1007/s00044-013-0821-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00044-013-0821-z

Keywords

search

Navigation