Research Article
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Year 2020, , 179 - 196, 15.02.2020
https://doi.org/10.18596/jotcsa.577259

Abstract

References

  • 1. Zhou J. NIH Public Access. 2006;29(12):1235–44.
  • 2. Rice DP, Miller LS. Health economics and cost implications of anxiety and other mental disorders in the United States. Br J Psychiatry. 1998;173(S34):4–9.
  • 3. Xue W, Yang F, Wang P, Zheng G, Chen Y, Yao X, et al. What Contributes to Serotonin–Norepinephrine Reuptake Inhibitors’ Dual-Targeting Mechanism? The Key Role of Transmembrane Domain 6 in Human Serotonin and Norepinephrine Transporters Revealed by Molecular Dynamics Simulation. ACS Chem Neurosci. 2018;9(5):1128–40.
  • 4. Blakely RD, Bauman AL. Biogenic amine transporters: regulation in flux. Curr Opin Neurobiol. 2000;10(3):328–36.
  • 5. Deorah S, Lynch CF, Sibenaller ZA, Ryken TC. Trends in brain cancer incidence and survival in the United States: Surveillance, Epidemiology, and End Results Program, 1973 to 2001. Neurosurg Focus. 2006;20(4):E1.
  • 6. Penmatsa A, Wang KH, Gouaux E. X-ray structure of dopamine transporter elucidates antidepressant mechanism. Nature [Internet]. 2013;503(7474):85–90. Available from: http://dx.doi.org/10.1038/nature12533
  • 7. Dessalew N. QSAR study on dual SET and NET reuptake inhibitors: an insight into the structural requirement for antidepressant activity. J Enzyme Inhib Med Chem. 2009;24(1):262–71.
  • 8. Om A-S, Ryu J-C, Kim J-H. Quantitative structure-activity relationships for radical scavenging activities of flavonoid compounds by GA-MLR technique. Mol Cell Toxicol. 2008;4(2):170–6.
  • 9. Davis GDJ, Vasanthi AHR. QSAR based docking studies of marine algal anticancer compounds as inhibitors of protein kinase B (PKBβ). Eur J Pharm Sci. 2015;76:110–8.
  • 10. Hehre WJ, Huang WW. Chemistry with computation: an introduction to SPARTAN. Wavefunction, Inc.; 1995.
  • 11. Bauernschmitt R, Ahlrichs R. Treatment of electronic excitations within the adiabatic approximation of time dependent density functional theory. Chem Phys Lett. 1996;256(4–5):454–64.
  • 12. Kennard RW, Stone LA. Computer aided design of experiments. Technometrics. 1969;11(1):137–48.
  • 13. Zeng H, Zheng R, Guo Y, Zhang S, Zou X, Wang N, et al. Cancer survival in C hina, 2003–2005: A population‐based study. Int J cancer. 2015;136(8):1921–30.
  • 14. Yap CW. PaDEL‐descriptor: An open source software to calculate molecular descriptors and fingerprints. J Comput Chem. 2011;32(7):1466–74.
  • 15. Roy K, Kar S, Ambure P. On a simple approach for determining applicability domain of QSAR models. Chemom Intell Lab Syst. 2015;145:22–9.
  • 16. Tropsha A, Gramatica P, Gombar VK. The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb Sci. 2003;22(1):69–77.
  • 17. Arthur DE, Uzairu A, Mamza P, Abechi SE, Shallangwa G. Qsar Modelling of Some Anticancer Pgi 50 Activity on Hl - 60 Cell Lines. 2016;3(1).
  • 18. Andersen J, Ringsted KB, Bang-Andersen B, Strømgaard K, Kristensen AS. Binding site residues control inhibitor selectivity in the human norepinephrine transporter but not in the human dopamine transporter. Sci Rep. 2015;5:15650.
  • 19. Topliss JG, Edwards RP. Chance factors in studies of quantitative structure-activity relationships. J Med Chem. 1979;22(10):1238–44.
  • 20. Damme S Van, Bultinck P. A new computer program for QSAR‐analysis: ARTE‐QSAR. J Comput Chem. 2007;28(11):1924–8.
  • 21. Tropsha A. Best practices for QSAR model development, validation, and exploitation. Mol Inform. 2010;29(6‐7):476–88.
  • 22. Viswanadhan VN, Ghose AK, Revankar GR, Robins RK. Atomic physicochemical parameters for three dimensional structure directed quantitative structure-activity relationships. 4. Additional parameters for hydrophobic and dispersive interactions and their application for an automated superposition of certain . J Chem Inf Comput Sci. 1989;29(3):163–72.
  • 23. Ghose AK, Crippen GM. Atomic physicochemical parameters for three‐ dimensional structure‐directed quantitative structure‐activity relationships I. Partition coefficients as a measure of hydrophobicity. J Comput Chem. 1986;7(4):565–77.
  • 24. Caballero J, Fernández M. Artificial neural networks from MATLAB® in medicinal chemistry. Bayesian-regularized genetic neural networks (BRGNN): Application to the prediction of the antagonistic activity against human platelet thrombin receptor (PAR-1). Curr Top Med Chem. 2008;8(18):1580–605.

Quantitative Structure-Activity Relationship (QSAR) Studies and Molecular docking Simulation of Norepinephrine Transporter (NET) Inhibitors as Anti-psychotic Therapeutic Agents

Year 2020, , 179 - 196, 15.02.2020
https://doi.org/10.18596/jotcsa.577259

Abstract


The Norepinephrine transporter (NET) is a Na+/Cl- coupled
neurotransmitter transporter responsible for reuptake of released
norepinephrine (NE) into neural terminals in the brain, an important
therapeutic agent used in the treatment of psychiatric disorders. A
quantitative structural activity relationship (QSAR) investigation
was carried out on 50 Molecules of NET Inhibitors to investigate
their inhibitory potencies against norepinephrine transporter as
novel agents for anti-psychotic disorders. The molecules were
optimized by employing Density functional theory (DFT) with basis set
of B3LYP/6-31G*. The genetic function Algorithm (GFA) approach was
used to generate a highly predictive and statistically significant
model with good correlation coefficient R2 Train = 0.952, Cross
validated coefficient Q2cv = 0.870 and adjusted squared correlation
coefficient R2adj = 0.898. The predictability and accuracy of the
developed model was evaluated through external validation using test
set molecules, Y-randomization and applicability domain techniques.
The results of Molecular docking simulation by using two
neurotransmitter transporters PDB ID 2A65 (resolution = 1.65 Å ) and
PDB ID 4M48 (resolution = 2.955 Å) showed that two of the ligands
(compound numbers 12 and 44) having higher binding affinity were
observed to inhibit the targets by forming hydrogen bonds and
hydrophobic interactions with amino acids of the two receptors
respectively. The results of this study are envisaged to provide very
important new insights into the molecular basis and structural
requirements that would help in designing more potent and more
specific therapeutic anti-psychotic agents.

References

  • 1. Zhou J. NIH Public Access. 2006;29(12):1235–44.
  • 2. Rice DP, Miller LS. Health economics and cost implications of anxiety and other mental disorders in the United States. Br J Psychiatry. 1998;173(S34):4–9.
  • 3. Xue W, Yang F, Wang P, Zheng G, Chen Y, Yao X, et al. What Contributes to Serotonin–Norepinephrine Reuptake Inhibitors’ Dual-Targeting Mechanism? The Key Role of Transmembrane Domain 6 in Human Serotonin and Norepinephrine Transporters Revealed by Molecular Dynamics Simulation. ACS Chem Neurosci. 2018;9(5):1128–40.
  • 4. Blakely RD, Bauman AL. Biogenic amine transporters: regulation in flux. Curr Opin Neurobiol. 2000;10(3):328–36.
  • 5. Deorah S, Lynch CF, Sibenaller ZA, Ryken TC. Trends in brain cancer incidence and survival in the United States: Surveillance, Epidemiology, and End Results Program, 1973 to 2001. Neurosurg Focus. 2006;20(4):E1.
  • 6. Penmatsa A, Wang KH, Gouaux E. X-ray structure of dopamine transporter elucidates antidepressant mechanism. Nature [Internet]. 2013;503(7474):85–90. Available from: http://dx.doi.org/10.1038/nature12533
  • 7. Dessalew N. QSAR study on dual SET and NET reuptake inhibitors: an insight into the structural requirement for antidepressant activity. J Enzyme Inhib Med Chem. 2009;24(1):262–71.
  • 8. Om A-S, Ryu J-C, Kim J-H. Quantitative structure-activity relationships for radical scavenging activities of flavonoid compounds by GA-MLR technique. Mol Cell Toxicol. 2008;4(2):170–6.
  • 9. Davis GDJ, Vasanthi AHR. QSAR based docking studies of marine algal anticancer compounds as inhibitors of protein kinase B (PKBβ). Eur J Pharm Sci. 2015;76:110–8.
  • 10. Hehre WJ, Huang WW. Chemistry with computation: an introduction to SPARTAN. Wavefunction, Inc.; 1995.
  • 11. Bauernschmitt R, Ahlrichs R. Treatment of electronic excitations within the adiabatic approximation of time dependent density functional theory. Chem Phys Lett. 1996;256(4–5):454–64.
  • 12. Kennard RW, Stone LA. Computer aided design of experiments. Technometrics. 1969;11(1):137–48.
  • 13. Zeng H, Zheng R, Guo Y, Zhang S, Zou X, Wang N, et al. Cancer survival in C hina, 2003–2005: A population‐based study. Int J cancer. 2015;136(8):1921–30.
  • 14. Yap CW. PaDEL‐descriptor: An open source software to calculate molecular descriptors and fingerprints. J Comput Chem. 2011;32(7):1466–74.
  • 15. Roy K, Kar S, Ambure P. On a simple approach for determining applicability domain of QSAR models. Chemom Intell Lab Syst. 2015;145:22–9.
  • 16. Tropsha A, Gramatica P, Gombar VK. The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb Sci. 2003;22(1):69–77.
  • 17. Arthur DE, Uzairu A, Mamza P, Abechi SE, Shallangwa G. Qsar Modelling of Some Anticancer Pgi 50 Activity on Hl - 60 Cell Lines. 2016;3(1).
  • 18. Andersen J, Ringsted KB, Bang-Andersen B, Strømgaard K, Kristensen AS. Binding site residues control inhibitor selectivity in the human norepinephrine transporter but not in the human dopamine transporter. Sci Rep. 2015;5:15650.
  • 19. Topliss JG, Edwards RP. Chance factors in studies of quantitative structure-activity relationships. J Med Chem. 1979;22(10):1238–44.
  • 20. Damme S Van, Bultinck P. A new computer program for QSAR‐analysis: ARTE‐QSAR. J Comput Chem. 2007;28(11):1924–8.
  • 21. Tropsha A. Best practices for QSAR model development, validation, and exploitation. Mol Inform. 2010;29(6‐7):476–88.
  • 22. Viswanadhan VN, Ghose AK, Revankar GR, Robins RK. Atomic physicochemical parameters for three dimensional structure directed quantitative structure-activity relationships. 4. Additional parameters for hydrophobic and dispersive interactions and their application for an automated superposition of certain . J Chem Inf Comput Sci. 1989;29(3):163–72.
  • 23. Ghose AK, Crippen GM. Atomic physicochemical parameters for three‐ dimensional structure‐directed quantitative structure‐activity relationships I. Partition coefficients as a measure of hydrophobicity. J Comput Chem. 1986;7(4):565–77.
  • 24. Caballero J, Fernández M. Artificial neural networks from MATLAB® in medicinal chemistry. Bayesian-regularized genetic neural networks (BRGNN): Application to the prediction of the antagonistic activity against human platelet thrombin receptor (PAR-1). Curr Top Med Chem. 2008;8(18):1580–605.
There are 24 citations in total.

Details

Primary Language English
Subjects Physical Chemistry
Journal Section Articles
Authors

Sabitu Babatunde Olasupo 0000-0003-1946-7404

Adamu Uzaıru This is me 0000-0002-6973-6361

Gideon Shallangwa 0000-0001-8564-868X

SANI Uba This is me 0000-0002-7750-8174

Publication Date February 15, 2020
Submission Date June 13, 2019
Acceptance Date November 26, 2019
Published in Issue Year 2020

Cite

Vancouver Olasupo SB, Uzaıru A, Shallangwa G, Uba S. Quantitative Structure-Activity Relationship (QSAR) Studies and Molecular docking Simulation of Norepinephrine Transporter (NET) Inhibitors as Anti-psychotic Therapeutic Agents. JOTCSA. 2020;7(1):179-96.