SYNTHESIS, REGRESSION ANALYSIS, DOCKING STUDIES OF ISONIAZID-BASED COMPOUNDS AS ANTI-TUBERCULOSIS THERAPEUTIC AGENTS
DOI:
https://doi.org/10.71336/jabs.1092Keywords:
Structure-activity, biological activity, tuberculosis, descriptorsAbstract
In the drug-design process, a structure-activity relationship is a method for the estimation of the biological activity of the newly designed unknown molecules by using regression analysis. This method aims to develop a mathematical relationship between the structural features of molecules (descriptors) and the property of interest, i.e. biological activity based on reference molecules. Based on this relationship, biological activity can be predicted for newly designed molecules. Initially, the molecules with known biological activities were considered as a Training set for regression analysis model-building purposes. The properties module from Datawarrior is used to calculate descriptors related to the molecule's structural features. Employing the structure-activity relationship method a significant relationship is developed between these descriptors with observed biological activity in the form of a mathematical equation. This mathematical equation calculates the newly designed molecules' biological activity. In the present study, the new substituted Isoniazid molecules are designed and optimized and their descriptors were calculated using Datawarrior modules. Then by using the Regression analysis model, their biological activities are calculated. The molecules with good biological activity are subjected to inhibition studies for the protein 1QPQ by the molecular docking method to confirm the inhibition activity of these molecules against Mycobacterium tuberculosis. Thus, based on regression analysis study and docking studies of substituted isoniazid derivatives, we can conclude that the Isoniazid derivatives reported here can be a good therapeutic agent against tuberculosis.
References
Hall, L.H. (2004): A Structure-Information Approach to the Prediction of Biological Activities and Properties. CHEMISTRY & BIODIVERSITY vol.1, p.183.
Judson, P. N. (1992): QSAR and Expert System in Prediction of". Pestic.Sci. pp. 155-160. DOI: https://doi.org/10.1002/ps.2780360211
Tropsha, A. (2010): Best Practices for QSAR Model Development, Validation, and Exploitation, Mol. Inf., pp. 476-488. DOI: https://doi.org/10.1002/minf.201000061
Kier, L. H. L.B. (1997): Quantitative Information Analysis: The New Center of Gravity in Medicinal Chemistry. Medicinal Chemistry Research vol. 7, pp. 335-339.
Kier, L. H. H. L. B. (1999): Molecular Structure Description: The Electrotopological State, Academic Press.
Hall, L. B. K. L. H. (1999): Topological Indices and Related Descriptors in QSAR and QSPR, UK.
George J. A. B., Adamson, W. (1976): Evaluation of an empirical structure-activity relationship for property prediction in a structurally diverse group of local anesthetics. Journal of the Chemical Society Perkin Transactions 1, no. 2, pp. 168-172. DOI: https://doi.org/10.1039/P19760000168
Kumar, D., Khare, G., Kidwai, S., Tyagi, A. K., Singh, R., Rawat, D. S. (2015): Novel isoniazid-amidoether derivatives: Synthesis, characterization and antimycobacterial activity evaluation. MedChemComm 6(1), 131-137. DOI: https://doi.org/10.1039/C4MD00288A
Shaw, D. J., Adamczyk, K., Frederix, P. W., Simpson, N., Robb, K., Greetham, G. M., Towrie, T., Parker, A. W., Hoskisson, P. A., Hunt, N. T. (2015): Multidimensional infrared spectroscopy reveals the vibrational and solvation dynamics of isoniazid. The Journal of Chemical Physics 142(21), 212401. DOI: https://doi.org/10.1063/1.4914097
Parumasivam, T., Kumar, H. S. N., Ibrahim, P., Sadikun, A., Mohamad, S. (2013): Anti-tuberculosis activity of lipophilic isoniazid derivatives and their interactions with first-line anti-tuberculosis drugs. Journal of Pharmacy Research 7(4), 313-317. DOI: https://doi.org/10.1016/j.jopr.2013.04.039
Hall, L. H. (2004): A Structure-Information Approach to the Prediction of Biological Activities and Properties. Chemistry & Biodiversity 1(1), 183-201. DOI: https://doi.org/10.1002/cbdv.200490010
Draper, P. (2000): Lipid biochemistry takes a stand against tuberculosis. Nature Medicine 6(9), 977-978. DOI: https://doi.org/10.1038/79670
Huuskonen, J. (2000): Estimation of Aqueous Solubility for a Diverse Set of Organic Compounds Based on Molecular Topology. Journal of Chemical Information and Computer Sciences 40(3), 773-777. DOI: https://doi.org/10.1021/ci9901338
Lilienkampf, A., Mao, J., Wan, B., Wang, Y., Franzblau, S. G., Kozikowski, A. P. (2009): Structure-Activity Relationships for a Series of Quinoline-Based Compounds Active against. Journal of Medicinal Chemistry 52(7), 2109-2118. DOI: https://doi.org/10.1021/jm900003c
SPSS version 24 for Windows. SPSS software packages, SPSS Inc., 444 North Michigan Avenue, Suite 3000, Chicago, Illinois, 60611, USA.
Eswaran, S., Adhikari, A. V., Pal, N. K., Chowdhury, I. H. (2010): Design and synthesis of some new quinoline-3-carbohydrazone derivatives as potential antimycobacterial agents. Bioorganic & Medicinal Chemistry Letters 20(3), 1040-1044. DOI: https://doi.org/10.1016/j.bmcl.2009.12.045
Patle. M. R., Bhagat G. K. Ganatra S. H., (2012), Inhibition Studies of Pyridine Based Compounds on Quinolinic Acid. Asian Journal of Research in Chemistry 5(9), 1159-1165.
Hsu, K. C., Chen, Y. F., Lin, S. R., & Yang, J. M. (2011): iGEMDOCK: a graphical environment of enhancing GEMDOCK using pharmacological interactions and post-screening analysis. BMC Bioinformatics 12(Suppl 1): 1-11. DOI: https://doi.org/10.1186/1471-2105-12-S1-S33
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Journal of Applied Biological Sciences
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
