IN SILICO MODELLING OF CYTOTOXIC BEHAVIOUR OF ANTI-LEUKEMIC COMPOUNDS ON HL-60 CELL LINE

Abstract

This research employs multiple linear regression technique in the modelling of some potent anti-leukemic compounds using paDEL molecular descriptor software calculator, to identify the best relationship between the chemical structure and toxicities of the anticancer datasets against some leukemic cell lines (HL-60). Statistical parameters such as Q2 and R2pred (test set) were computed to validate the strength of the model, while Williams plot was used to assess its applicability domain. The mean effects of the molecular descriptors in the models were calculated to illuminate the principal properties of the molecules responsible for their cytotoxicity.

References

1. Kwon, H.-C., et al., Establishment and characterization of an STI571-resistant human myelogenous leukemia cell line, SR-1. Cancer genetics and cytogenetics, 2004. 154(1): p. 52-56.
2. Monga, M. and E. Sausville, Developmental therapeutics program at the NCI: molecular target and drug discovery process. Leukemia, 2002. 16(4): p. 520-526.
3. Bhat, K.S., et al., Synthesis and antitumor activity studies of some new fused 1,2,4-triazole derivatives carrying 2,4-dichloro-5-fluorophenyl moiety. European Journal of Medicinal Chemistry, 2009. 44(12): p. 5066-5070.
4. Jemal, A., et al., Global cancer statistics. CA: a cancer journal for clinicians, 2011. 61(2): p. 69-90.
5. YAP, C.W. PaDEL-Descriptor: An Open Source Software to Calculate Molecular Descriptors and Fingerprints. Journal of computational chemistry 17 December 2010 [cited 32; 1466–1474]. Available from: http://onlinelibrary.wiley.com/doi/10.1002/jcc.21707/pdf.
6. Brignole, M., et al., 2013 ESC Guidelines on cardiac pacing and cardiac resynchronization therapy. Eur Heart J, 2013: p. eht150.
7. Waller, C.L. and M.P. Bradley, Development and validation of a novel variable selection technique with application to multidimensional quantitative structure-activity relationship studies. J Chem Inf Comput Sci, 1999. 39(2): p. 345-355.
8. Gomes, A., et al., Molecular epidemiology of methicillin-resistant Staphylococcus aureus in Colombian hospitals: dominance of a single unique multidrug-resistant clone. Microb Drug Resis, 2001. 7(1): p. 23-32.

9. Ahmad S, G.M. Design and training of a neural network for predicting the solvent accessibility of proteins. Journal of Computational Chemistry 2003 August; Available from: http://www.ncbi.nlm.nih.gov/pubmed/12827672.
10. Caballero, J. and M. Fernández, Artificial neural networks from MATLAB® in medicinal chemistry. Bayesian-regularized genetic neural networks (BRGNN): Application to the prediction of the antagonistic activity against human platelet thrombin receptor (PAR-1). Current topics in medicinal chemistry, 2008. 8(18): p. 1580-1605.
11. Galvao, R.K.H., et al., A method for calibration and validation subset partitioning. Talanta, 2005. 67(4): p. 736-740.
12. Kennard, R.W. and L.A. Stone, Computer aided design of experiments. Technometrics, 1969. 11(1): p. 137-148.
13. Golbraikh, A. and A. Tropsha, Beware of q 2! J Mol Graph Model, 2002. 20(4): p. 269-276.
14. Roy, K., S. Kar, and R.N. Das, Chapter 1 - Background of QSAR and Historical Developments, in Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment, K.R.K.N. Das, Editor. 2015, Academic Press: Boston. p. 1-46.
15. Eriksson, L., et al., Methods for reliability and uncertainty assessment and for applicability evaluations of classification-and regression-based QSARs. Environmental health perspectives, 2003. 111(10): p. 1361.
16. Ojha, P.K., et al., Further exploring r m 2 metrics for validation of QSPR models. Chemometrics and Intelligent Laboratory Systems, 2011. 107(1): p. 194-205.
17. Golbraikh, A., et al., Rational selection of training and test sets for the development of validated QSAR models. Journal of computer-aided molecular design, 2003. 17(2-4): p. 241-253.
18. Gramatica, P., E. Giani, and E. Papa, Statistical external validation and consensus modeling: A QSPR case study for K oc prediction. J Mol Graph Model, 2007. 25(6): p. 755-766.
19. Schultz, T.W., et al., Structure-toxicity relationships for the effects to Tetrahymena pyriformis of aliphatic, carbonyl-containing, α, β-unsaturated chemicals. Chemical research in toxicology, 2005. 18(2): p. 330-341.
20. OECD. Guidance Document on the Validation of (Quantitative) Structure–Activity Relationships [(Q)SAR] Models, Organisation for Economic Co-Operation and Development. 2007.
21. Riahi, S., et al., Investigation of different linear and nonlinear chemometric methods for modeling of retention index of essential oil components: Concerns to support vector machine. Journal of hazardous materials, 2009. 166(2): p. 853-859.
22. Zhu, X. and N.L. Kruhlak, Construction and analysis of a human hepatotoxicity database suitable for QSAR modeling using post-market safety data. Toxicology, 2014. 321: p. 62-72.
23. Todeschini, R. and V. Consonni, Molecular Descriptors for Chemoinformatics, Volume 41 (2 Volume Set). Vol. 41. 2009: John Wiley & Sons.