THEORETICAL MODELLING FOR INVESTIGATING SOME ACTIVE COMPOUNDS AS POTENT INHIBITORS AGAINST LUNG CANCER: A MULTI-LINEAR REGRESSION APPROACH

Authors

  • Shola Elijah Adeniji Ahmadu Bello University, Zaria, Nigeria
  • Momohjimoh Idris Ovaku
  • Tukur Saidu
  • Ahanonu Saviour Ugochukwu
  • Gideon Shallangwa
  • Adamu Uzairu

DOI:

https://doi.org/10.18540/jcecvl5iss1pp0125-0136

Keywords:

Ciprofloxacin, Descriptor, Genetic Function Approximation, Lung Cancer, QSAR.

Abstract

A Quantitative Structure Activity Relationship (QSAR) study has been attempted on ciprofloxacin derivatives as potent anti-lung cancer. QSAR models were derived with the aid of multi-linear regression (MLR) approach using topological, molecular shape, electronic and structural descriptors. The predictive ability of the QSAR models generated  were validated and the best model selected has squared correlation coefficient (R2) of 0.954801, adjusted squared correlation coefficient (Radj) of 0.939265, Leave one out (LOO) cross validation coefficient () value of 0.907523. The external validation set used for confirming the predictive power of the model has its R2pred of 0.8387. The QSAR models point out that AATSC2m, VR3_Dzp and BIC2 are the important descriptors effectively describing the bioactivity of these compounds.

Downloads

Download data is not yet available.

Author Biographies

Shola Elijah Adeniji, Ahmadu Bello University, Zaria, Nigeria

Shola Elijah Adeniji (corresponding author)

shola4343@gmail.com

Ahamadu Bello University, Zaria

Lecturer (reader)

Tukur Saidu

Tukur Saidu

saidutukur@gmail.com

Ahamadu Bello University, Zaria

Student

Ahanonu Saviour Ugochukwu

Ahanonu Saviour Ugochukwu

favour_saviour@yahoo.com

Ahamadu Bello University, Zaria

Student

Gideon Shallangwa

Gideon Shallangwa

shallangwa@gmail.com

Ahamadu Bello University, Zaria

Lecturer ( reader)

Adamu Uzairu

Adamu Uzairu 

adamuuzairu@yahoo.com

Ahamadu Bello University, Zaria

Professor

References

Afantitis, A., Melagraki, G., Sarimveis, H., Koutentis, P. A., Markopoulos, J., & Igglessi-Markopoulou, O. (2006). A novel QSAR model for predicting induction of apoptosis by 4-aryl-4H-chromenes. Bioorganic & Medicinal Chemistry, 14(19), 6686–6694.

Azéma, J., Guidetti, B., Dewelle, J., Le Calve, B., Mijatovic, T., Korolyov, A., … Kiss, R. (2009). 7-((4-Substituted) piperazin-1-yl) derivatives of ciprofloxacin: synthesis and in vitro biological evaluation as potential antitumor agents. Bioorganic & Medicinal Chemistry, 17(15), 5396–5407.

Becke, A. D. (1993). Becke’s three parameter hybrid method using the LYP correlation functional. J. Chem. Phys, 98, 5648–5652.

Chakraborti, A. K., Gopalakrishnan, B., Sobhia, M. E., & Malde, A. (2003). 3D-QSAR studies of indole derivatives as phosphodiesterase IV inhibitors. European Journal of Medicinal Chemistry, 38(11), 975–982.

Cramer, R. D., Patterson, D. E., & Bunce, J. D. (1988). Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. Journal of the American Chemical Society, 110(18), 5959–5967.

Friedman, J. H. (1991). Multivariate adaptive regression splines. The Annals of Statistics, 1–67.

Gold, K. A., Wistuba, I. I., & Kim, E. S. (2012). New strategies in squamous cell carcinoma of the lung: identification of tumor drivers to personalize therapy. Clinical Cancer Research, 18(11), 3002–3007.

Ibezim, E. C., Duchowicz, P. R., Ibezim, N. E., Mullen, L. M. A., Onyishi, I. V, Brown, S. A., & Castro, E. A. (2009). Computer-aided linear modeling employing QSAR for drug discovery. Scientific Research and Essays, 4(13), 1559–1564.

Jalali-Heravi, M., & Kyani, A. (2004). Use of computer-assisted methods for the modeling of the retention time of a variety of volatile organic compounds: a PCA-MLR-ANN approach. Journal of Chemical Information and Computer Sciences, 44(4), 1328–1335.

Kennard, R. W., & Stone, L. A. (1969). Computer aided design of experiments. Technometrics, 11(1), 137–148.

Khaled, K. F. (2011). Modeling corrosion inhibition of iron in acid medium by genetic function approximation method: A QSAR model. Corrosion Science, 53(11), 3457–3465.

Lamelas, I. P., Arca, J. A., & Pérez, J. L. F. (2012). Directed therapies in lung cancer: new hope? Archivos de Bronconeumología (English Edition), 48(10), 367–371.

Lee, C., Yang, W., & Parr, R. G. (1988). Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density. Physical Review B, 37(2), 785.

Li, Z., Wan, H., Shi, Y., & Ouyang, P. (2004). Personal experience with four kinds of chemical structure drawing software: review on ChemDraw, ChemWindow, ISIS/Draw, and ChemSketch. Journal of Chemical Information and Computer Sciences, 44(5), 1886–1890.

Longo, D. L., Fauci, A. S., Kasper, D. L., Hauser, S. L., Jameson, J. L., & Loscalzo, J. (2012). Harrison’s Principles of Internal Medicine 18E Vol 2 EB. McGraw Hill Professional.

MacConaill, L. E. (2012). Advancing personalized cancer medicine in lung cancer. Archives of Pathology & Laboratory Medicine, 136(10), 1210–1216.

Melagraki, G., Afantitis, A., Makridima, K., Sarimveis, H., & Igglessi-Markopoulou, O. (2006). Prediction of toxicity using a novel RBF neural network training methodology. Journal of Molecular Modeling, 12(3), 297–305.

Mendes, F., Antunes, C., Abrantes, A., Goncalves, A., Nobre-Gois, I., Sarmento, A., … Rosa, M. (2015). Lung cancer: the immune system and radiation. British Journal of Biomedical Science, 72(2), 78–84.

Raparia, K., Villa, C., DeCamp, M. M., Patel, J. D., & Mehta, M. P. (2013). Molecular Profiling in Non–Small Cell Lung Cancer: A Step Toward Personalized Medicine. Archives of Pathology & Laboratory Medicine, 137(4), 481–491.

Shahid, M., Choi, T. G., Nguyen, M. N., Matondo, A., Jo, Y. H., Yoo, J. Y., … Akter, S. (2016). An 8-gene signature for prediction of prognosis and chemoresponse in non-small cell lung cancer. Oncotarget, 7(52), 86561.

Shimizu, K., Okita, R., & Nakata, M. (2013). Clinical significance of the tumor microenvironment in non-small cell lung cancer. Annals of Translational Medicine, 1(2).

Singh, P. (2013). Quantitative Structure-Activity Relationship Study of Substituted-[1, 2, 4] Oxadiazoles as S1P1 Agonists. Journal of Current Chemical and Pharmaceutical Sciences, 3(1).

Teixeira, S. F., Guimarães, I. dos S., Madeira, K. P., Daltoé, R. D., Silva, I. V., & Rangel, L. B. A. (2013). Metformin synergistically enhances antiproliferative effects of cisplatin and etoposide in NCI-H460 human lung cancer cells. Jornal Brasileiro de Pneumologia, 39(6), 644–649.

Travis, W. D. (2011). Pathology of lung cancer. Clinics in Chest Medicine, 32(4), 669–692.

Tropsha, A., Gramatica, P., & Gombar, V. K. (2003). The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. Molecular Informatics, 22(1), 69–77.

Veerasamy, R., Rajak, H., Jain, A., Sivadasan, S., Varghese, C. P., & Agrawal, R. K. (2011). Validation of QSAR models-strategies and importance. International Journal of Drug Design & Discovery, 3, 511–519.

Wu, W., Walczak, B., Massart, D. L., Heuerding, S., Erni, F., Last, I. R., & Prebble, K. A. (1996). Artificial neural networks in classification of NIR spectral data: design of the training set. Chemometrics and Intelligent Laboratory Systems, 33(1), 35–46.

Downloads

Published

2019-03-08

How to Cite

Adeniji, S. E., Ovaku, M. I., Saidu, T., Ugochukwu, A. S., Shallangwa, G., & Uzairu, A. (2019). THEORETICAL MODELLING FOR INVESTIGATING SOME ACTIVE COMPOUNDS AS POTENT INHIBITORS AGAINST LUNG CANCER: A MULTI-LINEAR REGRESSION APPROACH. The Journal of Engineering and Exact Sciences, 5(1), 0125–0136. https://doi.org/10.18540/jcecvl5iss1pp0125-0136

Issue

Section

Physical Chemistry

Most read articles by the same author(s)

1 2 > >>