QSAR MODELLING OF SOME ANTICANCER PGI50 ACTIVITY ON HL-60 CELL LINES

David Ebuka Arthur, Adamu Uzairu, Paul Mamza, Eyije Abechi, Gideon Shallangwa

Abstract


QSAR (2D and 3D) studies were performed on a series of CAMPTOTHECIN derivatives using Material Studio software (accelrys). QSAR study performed on 102 analogues of which 90 were used in the training set and the rest 22 considered for the test set.  QSAR study performed using Genetic function approximation (GFA). GFA method came out with good correlation coefficient 0.837 , cross-validated coefficient 0.792  and R2Test of 0.9408. A highly predictive and statistically significant model was generated. The QSAR models were found to accurately predict the anticancer activity of structurally diverse test set compounds and to yield reliable clues for further optimization of the of CAMPTOTHECIN derivatives in the data set.


Keywords


Anticancer agents, Genetic Function Approximation, QSAR, CAMPTOTHECIN

Full Text:

PDF

References


Thun, M.J., et al., Lung cancer death rates in lifelong nonsmokers. Journal of the National Cancer Institute, 2006. 98(10): p. 691-699.

Sohn, E.J., et al., EWS/FLI1 oncogene activates caspase 3 transcription and triggers apoptosis in vivo. Cancer research, 2010. 70(3): p. 1154-1163.

Ghanbari, Z., et al., Structure-Activity Relationship for Fe (III)-Salen-Like Complexes as Potent Anticancer Agents. The Scientific World Journal, 2014. 2014.

Csizmadia, I. and R. Enriz, The role of computational medicinal chemistry in the drug discovery process. 2000, ELSEVIER SCIENCE BV PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS.

Vaidya, A., et al., Quantitative structure-activity relationships: a novel approach of drug design and discovery. Journal of Pharmaceutical Sciences and Pharmacology, 2014. 1(3): p. 219-232.

Young, D., Computational chemistry: a practical guide for applying techniques to real world problems. 2004: John Wiley & Sons.

Salah, T., et al., In silico investigation by conceptual DFT and molecular docking of antitrypanosomal compounds for understanding cruzain inhibition. Journal of Theoretical and Computational Chemistry, 2016. 15(03): p. 1650021.

Benarous, N., et al., Synthesis, characterization, crystal structure and DFT study of two new polymorphs of a Schiff base (E)-2-((2,6-dichlorobenzylidene)amino)benzonitrile. Journal of Molecular Structure, 2016. 1105: p. 186-193.

Bauernschmitt, R. and R. Ahlrichs, Treatment of electronic excitations within the adiabatic approximation of time dependent density functional theory. Chemical Physics Letters, 1996. 256(4): p. 454-464.

Kennard, R.W. and L.A. Stone, Computer aided design of experiments. Technometrics, 1969. 11(1): p. 137-148.

Deb, K., et al., A fast and elitist multiobjective genetic algorithm: NSGA-II. Evolutionary Computation, IEEE Transactions on, 2002. 6(2): p. 182-197.

Leardi, R., R. Boggia, and M. Terrile, Genetic algorithms as a strategy for feature selection. J. Chemom, 1992. 6(5): p. 267-281.

Hehre, W.J. and W.W. Huang, Chemistry with Computation: An introduction to SPARTAN. 1995: Wavefunction, Inc.

Yap, C.W., PaDEL‐descriptor: An open source software to calculate molecular descriptors and fingerprints. J. Comput. Chem, 2011. 32(7): p. 1466-1474.

Panagos, P., et al., Soil erodibility in Europe: A high-resolution dataset based on LUCAS. Science of the total environment, 2014. 479: p. 189-200.

Roy, K., S. Kar, and P. Ambure, On a simple approach for determining applicability domain of QSAR models. Chemometr Intell Lab Syst, 2015. 145: p. 22-29.

Barretina, J., et al., The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature, 2012. 483(7391): p. 603-607.

Roy, K., S. Kar, and P. Ambure, On a simple approach for determining applicability domain of QSAR models. Chemometrics and Intelligent Laboratory Systems, 2015. 145: p. 22-29.

Chirico, N. and P. Gramatica, Real external predictivity of QSAR models: how to evaluate it? Comparison of different validation criteria and proposal of using the concordance correlation coefficient. Journal of chemical information and modeling, 2011. 51(9): p. 2320-2335.

Netzeva, T.I., et al., Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships. ATLA, 2005. 33: p. 155-173.

Pourbasheer, E., et al., Application of genetic algorithm-support vector machine (GA-SVM) for prediction of BK-channels activity. European journal of medicinal chemistry, 2009. 44(12): p. 5023-5028.

Duchowicz, P.R., et al., A new search algorithm for QSPR/QSAR theories: Normal boiling points of some organic molecules. Chemical Physics Letters, 2005. 412(4): p. 376-380.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2016 David Ebuka Arthur

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.