Fullerene derivatives as lung cancer cell inhibitors: Investigation of potential descriptors using qsar approaches

Hung Jin Huang, Olga A. Kraevaya, Ilya I. Voronov, Pavel A. Troshin, Shan Hui Hsu

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

Background: Nanotechnology-based strategies in the treatment of cancer have potential advantages because of the favorable delivery of nanoparticles into tumors through porous vasculature. Materials and Methods: In the current study, we synthesized a series of water-soluble fullerene derivatives and observed their anti-tumor effects on human lung carcinoma A549 cell lines. The quantitative structure–activity relationship (QSAR) modeling was employed to investigate the relationship between anticancer effects and descriptors relevant to peculia-rities of molecular structures of fullerene derivatives. Results: In the QSAR regression model, the evaluation results revealed that the determina-tion coefficient r2 and leave-one-out cross-validation q2 for the recommended QSAR model were 0.9966 and 0.9246, respectively, indicating the reliability of the results. The molecular modeling showed that the lack of chlorine atom and a lower number of aliphatic single bonds in saturated hydrocarbon chains may be positively correlated with the lung cancer cytotoxi-city of fullerene derivatives. Synthesized water-soluble fullerene derivatives have potential functional groups to inhibit the proliferation of lung cancer cells. Conclusion: The guidelines obtained from the QSAR model might strongly facilitate the rational design of potential fullerene-based drug candidates for lung cancer therapy in the future.

Original languageEnglish
Pages (from-to)2485-2499
Number of pages15
JournalInternational Journal of Nanomedicine
Volume15
DOIs
Publication statusPublished - 2020

Keywords

  • Cytotoxicity
  • Machine learning
  • Non-small cell lung cancer
  • QSAR
  • Water-soluble fullerene derivatives

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