ABSTRACT:
A Quantitative Structure-Property relationship (QSPR) model was developed for prediction of Activity of Phenol’s and its congeners against L1210 Leukaemia cells. Murine cell lines such as P388 leukemia, L1210 leukemia, and B16 melanoma, dominated the early years of cancer cell testing both in culture and in mice. In this study we have attempted to develop a multiple linear regression (MLR) model with high accuracy and precision. For this first we prepare several models and then validate them by statistical parameters like Q Factor, PE, PSE, SPRESS etc. and proposed a model which has better prediction power to prediction of Activity against L1210 Leukaemia cells.
Cite this article:
Sameer Dixit, Arun K. Sikarwar. Statistical Approach to Modelling of Activity of Phenol’s and its Derivatives against L1210 Leukaemia cells. Asian J. Research Chem. 2020; 13(3):237-240. doi: 10.5958/0974-4150.2020.00046.2
Cite(Electronic):
Sameer Dixit, Arun K. Sikarwar. Statistical Approach to Modelling of Activity of Phenol’s and its Derivatives against L1210 Leukaemia cells. Asian J. Research Chem. 2020; 13(3):237-240. doi: 10.5958/0974-4150.2020.00046.2 Available on: https://ajrconline.org/AbstractView.aspx?PID=2020-13-3-16
REFERENCES:
1. Selassie and colleagues, J Med Chem, 48, 7234, (2005)
2. Nandi S. l., Vracko M., Bagchi M. C., Anticancer activity of selected phenolic compounds: QSAR studies using ridge regression and neural networks, Chem Biol Drug Des, 70(5), 424-36, (2007)
3. Haghi, A.K. Methodologies and Applications for Chemoinformatics and Chemical Engineering, IGI Global, 2, (2013)
4. Richon A. B., An Introduction to QSAR Methodology, Network Science Corporation, 5, (2018)
5. Pogliani L., Structure Property Relationships of Amino Acids and Some Dipeptides, Amino Acids, 6,141-153, (1994)
6. Rishikesh V. Antre, Rajesh J. Oswal, et al., QSAR Studies of Substituted Pyrazolone Derivatives as AntiInflammatory Agents, Med chem, 2(6): 126-130 (2012)
7. J. H. van Drie, Curr. Pharm.Des. 2003, 9, 1649.
8. J. H. van Drie, in: Computational Medicinal Chemistry for Drug Discovery, P. Bultinck et al., Eds., Marcel Dekker, 2004.
9. H. Meyer, Arch. Exp. Pathol. Pharmakol. 1999, 42, 109.
10. A. Golbraikh, A. Tropsha, J. Mol.Graph Mod. 2002, 20, 269.
11. A. Tropsha, P. Gramatica, V.J. Gombar, QSAR Comb. Sci., 2003, 22, 69.
12. P. Gramatica, Principles of QSAR models validation: internal and external, QSAR Comb.Sci.2007, 26(5), 694-701.