Author(s):
Arpan Adhikary, Ronak Nair, Lakshya Moukthika, Ruchi Verma
Email(s):
ruchiverma.pharma@gmail.com
DOI:
10.52711/0974-360X.2023.00761 
Address:
Arpan Adhikary, Ronak Nair, Lakshya Moukthika, Ruchi Verma*
Department of Pharmaceutical Chemistry, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal-576104, Karnataka, India.
*Corresponding Author
Published In:
Volume - 16,
Issue - 10,
Year - 2023
ABSTRACT:
Quantitative Structure Activity Relationship (QSAR) studies are tools mostly used in many research areas, including drug discovery process. The tropomyosin receptor kinase (TRK) family are emerging as an important target for cancer therapeutics. The atom based 3D QSAR model and 2D QSAR model were designed and suitable models were generated useful for predicting the tetrahydropyrrolo[3,4-c]pyrazol derivatives prior to their synthesis, developed for predicting the anti-cancer activity against TRKs . The given study indicates the credibility of derived QSAR model by the determination of suitable statistical parameters as we have observed high relationship between experimental and predicted activity values showing ligand molecule larotrectinib with various possibilities of structural modifications to develop potential molecules with significant TRKs inhibitory activity and also predict the activity of any unknown derivative. The data reported by the above QSAR models provides necessary directions for the designing of new TRKs inhibitors against cancer.
Cite this article:
Arpan Adhikary, Ronak Nair, Lakshya Moukthika, Ruchi Verma. 2D QSAR and Atom based 3D QSAR study of Tropomyosin Receptor Kinases inhibition by pyrazol derivatives. Research Journal of Pharmacy and Technology 2023; 16(10):4681-0. doi: 10.52711/0974-360X.2023.00761
Cite(Electronic):
Arpan Adhikary, Ronak Nair, Lakshya Moukthika, Ruchi Verma. 2D QSAR and Atom based 3D QSAR study of Tropomyosin Receptor Kinases inhibition by pyrazol derivatives. Research Journal of Pharmacy and Technology 2023; 16(10):4681-0. doi: 10.52711/0974-360X.2023.00761 Available on: https://www.rjptonline.org/AbstractView.aspx?PID=2023-16-10-31
REFERENCES
1. Zhao M, Wang L, Zheng L, Zhang M, Qiu C, Zhang Y, Donshu D, Bing. 2D-QSAR and 3D-QSAR Analyses for EGFR Inhibitors. Biomed Res Int. 2017:4649191 https://doi.org/10.1155/2017/4649191.
2. Verma R, Bairy I, Tiwari M, Bhat GV, Shenoy GG. In silico studies, synthesis and anticancer activity of novel diphenyl ether-based pyridine derivatives. Mol Divers. 2019;23(3):541–554: https://doi.org/10.1007/s11030-018-9889-1
3. N. Noolvi M, M. Patel H, Bhardwaj V. A Comparative QSAR Analysis of Quinazoline Analogues as Tyrosine Kinase (erbB-2) Inhibitors. Med Chem. 2011;7(3):200–212. https://doi.org/10.2174/157340611795564213
4. Goudzal A, Hadaji E, Bouachrine M, El Hamdani H, Ouammou A. QSAR and docking molecular models to predict anti-cancer activity on a series of azacalix [2] arene [2] pyrimidine derivatives as anticancer agents. Mater Today Proc. 2020;51(2).https://doi.org/10.1016/j.matpr.2020.08.002
5. Ul-Haq Z, Effendi JS, Ashraf S, Bkhaitan MM. Atom and receptor based 3D QSAR models for generating new conformations from pyrazolopyrimidine as IL-2 inducible tyrosine kinase inhibitors. J Mol Graph Model. 2017;74:379–395. https://doi.org/10.1016/j.jmgm.2017.03.016
6. Pourbasheer E, Amanlou M. 3D-QSAR analysis of anti-cancer agents by CoMFA and CoMSIA. Med Chem Res. 2014;23(2):800–809. https://doi.org/10.1007/s00044-013-0676-3
7. Kasmi R, Hadaji E, Chedadi O, El Aissouq A, Bouachrine M, Ouammou A. 2D-QSAR and docking study of a series of coumarin derivatives as inhibitors of CDK (anticancer activity) with an application of the molecular docking method. Heliyon. 2020;6(8):e04514. https://doi.org/10.1016/j.heliyon.2020.e04514
8. Singh KD, Karthikeyan M, Kirubakaran P, Nagamani S. Pharmacophore filtering and 3D-QSAR in the discovery of new JAK2 inhibitors. J Mol Graph Model [Internet]. 2011;30:186–197. http://dx.doi.org/10.1016/j.jmgm.2011.07.004
9. Wu T, Zhang C, Lv R, Qin Q, Liu N, Yin W, et al. Design, synthesis, biological evaluation and pharmacophore model analysis of novel tetrahydropyrrolo[3,4-c]pyrazol derivatives as potential TRKs inhibitors. Eur J Med Chem. 2021;223:113627. https://doi.org/10.1016/j.ejmech.2021.113627
10. Ma J, Zhang H, Zhang X, Lei M. 3D-QSAR studies of D3R antagonists and 5-HT1AR agonists. J Mol Graph Model [Internet]. 2019;86:132–141. https://doi.org/10.1016/j.jmgm.2018.10.013
11. Wang M, Li W, Wang Y, Song Y, Wang J, Cheng M. In silico insight into voltage-gated sodium channel 1.7 inhibition for anti-pain drug discovery. J Mol Graph Model. 2018;84:18–28. https://doi.org/10.1016/j.jmgm.2018.05.006
12. Sadeghian-Rizi S, Sakhteman A, Hassanzadeh F. A quantitative structure-activity relationship (QSAR) study of some diaryl urea derivatives of B-RAF inhibitors. Res Pharm Sci. 2016;11(6):445–453. https://doi.org/10.4103/1735-5362.194869
13. Dermeche K, Tchouar N, Belaidi S, Salah T. Qualitative Structure-activity relationships and 2D-QSAR modeling of TNF-α inhibition by thalidomide derivatives. J Bionanoscience. 2015;9(5):395–400. http://doi.org/10.1166/jbns.2015.1320
14. Kristam R, Parmar V, Viswanadhan VN. 3D-QSAR analysis of TRPV1 inhibitors reveals a pharmacophore applicable to diverse scaffolds and clinical candidates. J Mol Graph Model. 2013;45:157–172. http://dx.doi.org/10.1016/j.jmgm.2013.08.014
15. Zhao Y, Gu W, Li Y. Molecular design of 1,3,5,7-TetraCN derivatives with reduced bioconcentration using 3D-QSAR modeling, full factorial design, and molecular docking. J Mol Graph Model. 2018;84:197–214. http://doi: 10.1016/j.jmgm.2018.07.006
16. Jiménez Villalobos TP, Gaitán Ibarra R, Montalvo Acosta JJ. 2D, 3D-QSAR and molecular docking of 4(1H)-quinolones analogues with antimalarial activities. J Mol Graph Model [Internet]. 2013;46:105–124. http://dx.doi.org/10.1016/j.jmgm.2013.10.002
17. Poojita K, Fathima F, Ray R, Kumar L, Verma R. 3D Atom based QSAR model of DprE1 inhibitors as Anti-tubercular Agents. Res J Pharm Technol. 2021;14(11):5903–10. https://doi.org/10.52711/0974-360X.2021.01026
18. Amata E, Marrazzo A, Dichiara M, Modica MN, Salerno L, Prezzavento O, et al. Comprehensive data on a 2D-QSAR model for Heme Oxygenase isoform 1 inhibitors. Data Br. 2017;15:281–299. https://doi.org10.1016/j.dib.2017.09.036
19. Poojita K, Fathima F, Ray R, Kumar L, Verma R. Atom based 3D QSAR and Fingerprint based 2D QSAR of Novel Molecules as MmpL3 receptor inhibitors for Mycobacterium tuberculosis. Res J Pharm Technol. 2021;14(12):6321–6329. https://doi.org/10.52711/0974-360X.2021.01093