Virtual Screening and Statistical Analysis in the Design of New Caffeine Analogues Molecules with Potential Epithelial Anticancer Activity

Author(s): Josivan da Silva Costa*, Karina da Silva Lopes Costa, Josiane Viana Cruz, Ryan da Silva Ramos, Luciane Barros Silva, Davi Do Socorro Barros Brasil, Carlos Henrique Tomich de Paula da Silva, Cleydson Breno Rodrigues dos Santos, Williams Jorge da Cruz Macedo.

Journal Name: Current Pharmaceutical Design

Volume 24 , Issue 5 , 2018

Abstract:

About 132 thousand cases of melanoma (more severe type of skin cancer) were registered in 2014 according to the World Health Organization. This type of cancer significantly affects the quality of life of individuals. Caffeine has shown potential inhibitory effect against epithelial cancer. In this study, it was proposed to obtain new caffeine-based molecules with potential epithelial anticancer activity. For this, a training set of 21 molecules was used for pharmacophore perception procedures. Multiple linear regression analyses were used to propose mono-, bi-, tri-, and tetra-parametric models applied in the prediction of the activity. The generated pharmacophore was used to select 350 molecules available at the ZINCpharmer server, followed by reduction to 24 molecules, after selection using the Tanimoto index, yielding 10 molecules after final selection by predicted activity values > 1.5229. These ten molecules had better pharmacokinetic properties than the other ones used as reference and within the clinically significant limits. Only two molecules show minor hits of toxicity and were submitted to molecular docking procedures, showing BFE (binding free energy) values lower than the reference values. Statistical analyses indicated strong negative correlations between BFE and pharmacophoric properties (high influence on BFE lowering) and practically null correlation between BFE and BBB. The two most promising molecules can be indicated as candidates for further in vitro and in vivo analyzes.

Keywords: Epithelial cancer, caffeine, Chk1, Molecular modeling, multiple linear regression, Pharmacokinetic and toxicological properties.

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Article Details

VOLUME: 24
ISSUE: 5
Year: 2018
Page: [576 - 594]
Pages: 19
DOI: 10.2174/1381612823666170711112510

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