Author(s): Shivani Rawat, Sarvesh Paliwal, Yogita Ale


DOI: 10.52711/0974-360X.2024.00054   

Address: Shivani Rawat1*, Sarvesh Paliwal2, Yogita Ale3
1School of Pharmaceutical Sciences, Sardar Bhagwan Singh University, Balawala, Dehradun 248001, Uttarakhand, India.
2Department of Pharmacy, Banasthali Vidyapith (Banasthali University), Tonk, 304001 Rajasthan, India.
3Uttaranchal Institute of Pharmaceutical Sciences, Uttaranchal University, 248007, Uttarakhand, India.
*Corresponding Author

Published In:   Volume - 17,      Issue - 1,     Year - 2024

11-ß hydroxysteroid dehydrogenase type 1 is a key enzyme accountable for the interconversion of physiologically inert cortisone to active cortisol thus presents an effective target for the development of pharmacologically active anti diabetic agents focused on manage blood glucose levels, improve insulin sensitivity. The 11ß-HSD1 facilitates intracellular cortisol construction that have a disease-causing role in type 2 diabetes and the co-morbidities that it causes. Drugs in habiting the enzyme 11 ß-HSD1 offers a potential therapy to lessen the type 2 diabetes. Oxazinanone ring has shown activities as antitumor, antihypertensive, antibacterial, anti-inflammatory, antioxidant and many more. Oxazinanone ring have emerged as potent inhibitors of 11ß-HSD1 enzyme. QSAR of Oxazinanone derivatives is performed with a goal of elucidating the key characteristics that cause their anti-diabetic action. QSAR is the most widespread method to ligand-based drug design. It is supposed that structures of the molecules are directly proportional with biological activities, and thus, the biological activities can be altered with any structural changes. The process involves computational or mathematical models to find important correlations between a series of structures and functions. Step wise partial least square, multiple linear regressions, and feed forward neural network were used in a QSAR investigation on enzyme (IC50 nM). The developed models were cross confirmed by the ‘‘leave one out’’ method. The model reveals the significance of steric parameter Verloop B1 (Substitution 1) and Total lipole molecular descriptor.Total lipole bear a resemblance to lipophilicity which is a ratio of the capability of molecules to transfer between oily partition and aqueous partition. These descriptors will have an impact on the design and expansion of novel anti-diabetic 11-hydroxysteroid dehydrogenase type 1 inhibitors.

Cite this article:
Shivani Rawat, Sarvesh Paliwal, Yogita Ale. QSAR of Oxazinanone Derivatives As 11-Β Hydroxysteroid Dehydrogenase Type 1 Inhibitor A Potent Anti Diabetic Agent. Research Journal of Pharmacy and Technology. 2024; 17(1):347-7. doi: 10.52711/0974-360X.2024.00054

Shivani Rawat, Sarvesh Paliwal, Yogita Ale. QSAR of Oxazinanone Derivatives As 11-Β Hydroxysteroid Dehydrogenase Type 1 Inhibitor A Potent Anti Diabetic Agent. Research Journal of Pharmacy and Technology. 2024; 17(1):347-7. doi: 10.52711/0974-360X.2024.00054   Available on:

1.    Kharroubi AT, Darwish HM. Diabetes mellitus : The epidemic of the century. 2015; 6(6): 850–67. doi: 10.4239/wjd.v6.i6.850
2.    Karishma S, Lakshmi K, Tony DE, Babu AN, Nadendla RR. et al.  Pharmacological Evaluation of Leaf Extract of Terminalia bellerica with Moringa oleifera for its Synergistic Action on Anti-diabetic Activity and Anti-inflammatory Activity in Rats. Res J Pharm Technol. 2019; 12(3): 1181-4. DOI: 10.5958/0974-360X.2019.00195.1
3.    Sam SG, Kumar A, Babu VD, Swamy NV. et al.  Evaluation of Anti-diabetic and Anti-inflammatory activities of ethanolic extract of whole plant of Inula racemosa. Research Journal of Pharmacology and Pharmacodynamics. 2015; 7(3): 129-34.
4.    Saeedi P, Petersohn, Salpea P, Malanda B, Karuranga S, Unwin N, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045 : Results from the International Diabetes Federation Diabetes Atlas, 9 th edition. Diabetes Res Clin Pract. 2019; 157: 107843.
5.    Antony J, Debroy S, Manisha C, Thomas P, Jeyarani V, Choephel T, et al.  In-vitro cell line Models and Assay methods to study the Anti-diabetic Activity. Research Journal of Pharmacy and Technology. 2019; 12(5): 2200-6. DOI: 10.5958/0974-360X.2019.00367.6
6.    World Health Organization. Classification of diabetes mellitus. 2019
7.    Hu Y, Chen Y. Overview of Type 2 Diabetes Drugs on the Market. 2020; 1–14.
8.    Ge R, Huang Y, Liang G, Li X. 11 -Hydroxysteroid Dehydrogenase Type 1 Inhibitors as Promising  Therapeutic Drugs for Diabetes : Status and Development. 2010; 412–22.
9.    Anagnostis P, Katsiki N, Adamidou F, Athyros VG, Karagiannis A, Kita M, et al. 11beta-Hydroxysteroid dehydrogenase type 1 inhibitors: Novel agents for the treatment of metabolic syndrome and obesity-related disorders? Metabolism. 2013; 62(1): 21–33. Available from:
10.    Kotelevtsev Y, Holmes M.C., Burchell A, Houston P.M., Schmoll D, Jamieson P, et al. 11β-Hydroxysteroid dehydrogenase type 1 knockout mice show attenuated glucocorticoid-inducible responses and resist hyperglycemia on obesity or stress. Proc Natl Acad Sci USA. 1997; 94(26): 14924–9.
11.    Patel HM, Noolvi MN, Sharma P. CHEMISTRY Quantitative structure – activity relationship ( QSAR ) studies as strategic approach in Drug Discovery. 2014; 4991–5007.
12.    Neves BJ, Braga RC, Melo-Filho C.C., et al. QSAR-based virtual screening: Advances and applications in drug discovery. Front Pharmacol. 2018; 9: 1–7. doi: 10.3389/fphar.2018.01275
13.    Kaur N, Singh K. 3D-QSAR and Molecular Docking Studies of N-(2-Aminophenyl)-Benzamide Derivatives as Inhibitors of HDAC^ sub 2^. Research Journal of Pharmacy and Technology. 2014; 7(7): 760.
14.    Rosy JC, Balamurali S, Mary JA, Shenbagarathai R, Sundar K. Generation of 2D-QSAR model for angiogenin inhibitors: A ligand-based approach for cancer drug design. Trends  Bioinforma. 2016; 9(1–3): 1–13.
15.    Vaishnav Y, Thakur A, Kaur CD, Verma S, Mishra A, Jain SK, Ghode P, et al.  QSAR Analysis of some N, N-diphenyl urea derivatives as CCR5 Receptor Antagonist. MeSO. 2018; 15(7.8239): 14. DOI: 10.5958/0974-360X.2018.00697.2
16.    Burns JA, Whitesides GM. Feed-forward neural networks in chemistry: mathematical systems for classification and pattern recognition. Chemical Reviews. 1993; 93(8): 2583-601.
17.    Poojita K, Fathima F, Ray R, Kumar L, Verma R et al.  Atom based 3D QSAR and Fingerprint based 2D QSAR of Novel Molecules as MmpL3 receptor inhibitors for Mycobacterium tuberculosis. Research Journal of Pharmacy and Technology. 2021; 14(12): 6321-9. DOI:10.52711/0974-360X.2021.01093
18.    Xu Z, Tice CM, Zhao W, Cacatian S, Ye YJ, Singh SB, et al. Structure-based design and synthesis of 1,3-oxazinan-2-one inhibitors of 11β-hydroxysteroid dehydrogenase type 1. J Med Chem. 2011; 54(17): 6050–62.
19.    Dubey S, Bhardwaj S, Parbhakaran P, Singh E. et al.  In silico Prediction of Pyrazoline Derivatives as Antimalarial agents. Asian Journal of Pharmaceutical Research. 2022; 12(2): 119-24.
20.    Dessalew N. Sci Pharm QSAR Study on Novel CCR5 Receptor Antagonists : An Insight into the Structural Requirement for the HIV Co Receptor Antagonist Activity. 2008;
21.    Singh S, Das S, Pandey A, Paliwal S, Singh R. Quantitative structure activity relationship studies of topoisomerase i inhibitors as potent antibreast cancer agents. J Chem. 2013; 2013.
22.    Peter SC., Dhanjal JK, Malik V, Radhakrishnan N, Jayakanthan M, Sundar D, et al. Quantitative structure-activity relationship (QSAR): Modeling approaches to biological applications [Internet]. Vols. 1–3, Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics. 2018. 661–676 p. Available from:
23.    Dessalew N. Sci Pharm QSAR Study on Novel CCR5 Receptor Antagonists : An Insight into the Structural Requirement for the HIV Co Receptor Antagonist Activity. 2008;
24.    Baviskar BA, Deore SL, Jadhav AI. 2D and 3D QSAR Studies of Saponin Analogues as Antifungal Agents against Candida albicans. J Young Pharm. 2020; 12(1): 48–54.
25.    Madhawai K, Rishipathak D, Chhajed S, Kshirsagar S. et al.  Predicting the Anti-Inflammatory Activity of Novel 5-Phenylsulfamoyl-2-(2-Nitroxy)(Acetoxy) Benzoic acid derivatives using 2D and 3D-QSAR (kNN-MFA) Analysis. Asian J. Res. Pharm. Sci. 2017; 7(4): 227-34. DOI:  10.5958/2231-5659.2017.00036.4
26.    Sainy N, Dubey N, Sharma R, Dubey N, Sainy J. et al.  3D QSAR Analysis of Flavones as Antidiabetic agents. Research Journal of Pharmacy and Technology. 2022; 15(4): 1689-95.
27.    Prajapati PM, Shah YR, Sen DJ. Artificial Neural Network: A New Approach for QSAR Study. Research Journal of Science and Technology. 2011; 3(1): 17-24.
28.    Malik JK, Soni H, Singhai AK. QSAR-Application in Drug Design. International Journal of Pharmaceutical Research and Allied Sciences. 2013; 2(1).
29.    Harper KC, Bess EN, Sigman MS, of asymmetric catalytic reactions. Nat Chem. 2012; 4(5): 366–74. Available from:
30.    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.

Recomonded Articles:

Research Journal of Pharmacy and Technology (RJPT) is an international, peer-reviewed, multidisciplinary journal.... Read more >>>

RNI: CHHENG00387/33/1/2008-TC                     
DOI: 10.5958/0974-360X 

56th percentile
Powered by  Scopus

SCImago Journal & Country Rank

Recent Articles


Not Available