Abstract
We attempted to formulate quantitative structure–activity relationship modeling of 2,5-bis(1-Aziridinyl) 1,4-benzoquinone (BABQ) compounds according to calculated molecular descriptors. Various molecular descriptors such as physicochemical, constitutional, geometrical, electrostatic, and topological indices of such compounds have been calculated and QSAR models have been developed considering in vitro and in vivo biological activities. To establish a relationship between activity and structural descriptors of BABQ compounds, it is essential to develop a regression or an input–output model. Because the number of molecular descriptors greatly exceeds the number of observations, conventional regression methodologies are not useful in such studies. Hence, we developed QSAR models based on a large set of theoretical molecular descriptors using ridge regression methodology, which overcomes this limitation and also because the independent variables are highly intercorrelated. Finally, we applied the model for prediction of a promising new BABQ compound expected to be highly active, and it is seen that our model is in good agreement with the hypothesis in terms of in vitro and in vivo activities.
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References
Bagchi MC, Maiti BC (2003) On application of atom pairs on drug design. J Mol Struct THEOCHEM 623:31–37
Bagchi MC, Maiti BC, Bose S (2004) QSAR of antituberculosis drugs of INH type using graphical invariants. J Mol Struct THEOCHEM 679:179–186
Bagchi MC, Maiti BC, Mills D, Basak SC (2004) Usefulness of graphical invariants in quantitative structure–activity correlations of tuberculostatic drugs of the isonicotinic acid hydrazide type. J Mol Model 10:102–111
Basak SC (1987). Use of molecular complexity indices in predictive pharmacology and toxicology: A QSAR approach. Med Sci Res 15:605–609
Basak SC, Grunwald GD, Niemi GJ (1997) In: Balaban AT (eds) From chemical topology to three-dimensional geometry. Plenum Press, New York, pp 73–116
Basak SC, Mills D, Hawkins DM, El-Masri H (2003) Prediction of human blood:air partition coefficient: A comparison of structure-based and property-based methods. Risk Analysis 23:1173–1184
Basak SC, Mills D, Mumtaz MM, Balasubramanian K (2003) Use of topological indices in predicting aryl hydrocarbon receptor binding potency of dibenzofurans: a hierarchical QSAR approach. Ind J Chem 42A:1385–1391
Domagk G, Petersen S, Gauss W (1954) Experimental chemotherapy of tumors. Z Krebsforsch 59:617–622
Driebergen RJ, Moret EE, Janssen LHM, Blauw JS, Beijnen JH, Holthuis JJM, Postma Kelder SJ, Verboom W, Reinhoudt DN, Lelieveld P (1993) Electrochemistry of potentially bioreductive alkylating quinones. Part 4. Qualitative and quantitative structure–activity relationships of aziridinylquinones. Recl Trav Chim Pays-Bas (J R Neth Chem Soc) 112:174–185
Estrada E (1999) In: Devillers J, Balaban AT (eds) Topological indices and related Descriptors in QSAR and QSPR. Gordon and Breach, Amsterdam, pp 403–453
Frank IE, Friedman JH (1993) A statistical view of some chemometrics regression tools. Technometrics 35:109–135
Ghosh Payel, Thanadath Megha, Bagchi Manish C (2006) On an aspect of calculated molecular descriptors in QSAR studies of quinolone antibacterials. Mol Divers (in press)
Hendry JA, Romer RF, Rose FL, Walpole AL (1951) Cytotoxic agents. III. Derivatives of ethylenimine. Br J Pharmacol Chemother 6:357–410
Hoerl AE, Kennard RW (1970) Ridge regression biased estimation for nonorthogonal problems. Technometrics 8:27–51
Hoskuldsson A (1988) PLS regression methods. J Chemometr 2:211–228
Hoskuldsson A (1995). A combined theory for PCA and PLS. J Chemometr 9:91–123
Katritzky AR, Petrukhin R,Tatham D, Basak S, Benfenati E, Karelson M, Maran U (2001) Interpretation of quantitative structure–property–activity relationships. J Chem Inf Comput Sci 41:679–685
Massy WF (1965) Principal components regression in exploratory statistical research. J Am Statist Assoc 60:234–246
Meier R, Allgöwer M (1945) Characterization in tissue culture of substances affecting cell division. Experientia 1:57–61
Miller AJ (1990) Subset selections in regression. Chapman and Hall, New York
Moret EE (1993) Computational Medicinal Chemistry of Aziridinylquinones, Ph.D. Thesis Utrecht University, October. http://www.cmc.pharm.uu.nl/moret/phd/thesis/thesis.html
Nakao H, Arakawa M, Nakamura T, Fukushima M (1972) Antileukemic Agents. II. New 2,5-bis (1- aziridinyl)-p-benzoquinone derivatives. Chem Pharm Bull 20:1968–1974
NCSS—Statistical and Power Analysis Software; Hintze, J (2004) NCSS and PASS. Number Cruncher Statitical Systems, Kaysville, UT. http://www.ncss.com/
Randic M (2001) Novel shape descriptors for molecular graphs. J Chem Inf Comput Sci 41:607–613
Rao CR (1973) Linear statistical inference and its applications, 2nd ed. John Wiley & Sons, New York
Rencher AC, Pun FC (1980) Inflation of R2 in best subset regression. Technometrics 22:49–53
Wold H (1975). Soft modelling by latent variables: The non-linear iterative partial least squares approach. In: Gani J (ed) Perspectives in probability and statistics, papers in honor of MS Bartlett. Academic Press, London
Acknowledgment
Sisir Nandi thanks the Council of Scientific and Industrial Research, New Delhi 110001, India for the grant of a Junior Research Fellowship to him.
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Nandi, S., Bagchi, M.C. QSAR Analysis of BABQ compounds via calculated molecular descriptors. Med Chem Res 15, 393–406 (2007). https://doi.org/10.1007/s00044-006-0010-4
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DOI: https://doi.org/10.1007/s00044-006-0010-4