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In silico modelling of quantitative structure–activity relationship of multi-target anticancer compounds on k-562 cell line

  • David Ebuka ArthurEmail author
  • Adamu Uzairu
  • Paul Mamza
  • Stephen Eyije Abechi
  • Gideon Shallangwa
Original Article
  • 74 Downloads

Abstract

The pGI50 cytotoxicity values of 112 compounds on K-562 cancer cell line were modeled to illustrate the quantitative structure–activity relationship (QSAR) of the compounds. The dataset were divided into training and test set through Kennard-stone algorithm, while the pool of molecular descriptors calculated with paDEL descriptor metric program was subjected to the genetic functional algorithm (GFA) for selection of descriptor to be modeled. The best QSAR model developed was then subjected to a rigorous statistical test. The statistical significance of the model was verified by calculating the values of Q2LOO (0.845), Q2F1 (0.9397), Q2F2 (0.6862) and R2pred (0.6862) needed to evaluate the strength and robustness of the model. The result of the internal and external validation of the model indicates that the model is good and could be used to predict the GI50 of anticancer compounds on K-562 leukemia cell line. The model developed was used in designing new anticancer drugs with higher activity or more potent and less toxic in nature when compared to the lead compound. These compounds significant activities were found to correlate to with some of the molecular descriptors such as the number of hydrogen bond acceptors present in the surface of the molecule.

Keywords

K-562 cell line QSAR GFA-MLR Anticancer Williams plot 

Notes

Acknowledgements

We would like to acknowledge the National Cancer institute for providing the material data used for the QSAR study in the website (https://wiki.nci.nih.gov/display/NCIDTPdata/NCI-60+Growth+Inhibition+Data).

Supplementary material

13721_2018_168_MOESM1_ESM.docx (1.2 mb)
Supplementary material 1 (DOCX 1197 KB)

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Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  • David Ebuka Arthur
    • 1
    Email author
  • Adamu Uzairu
    • 1
  • Paul Mamza
    • 1
  • Stephen Eyije Abechi
    • 1
  • Gideon Shallangwa
    • 1
  1. 1.Department of ChemistryAhmadu Bello University (ABU) ZariaZariaNigeria

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