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<p>The development of innovative anticancer agents is essential for addressing cancer-related complications and reducing mortality rates associated with the disease. This study employed a machine learning approach to assess the anticancer efficacy of various triphenylphosphonium salt derivatives against the rhabdomyosarcoma cell line (RD). The research used resources from the publicly accessible Online Chemical Database and Modelling Environment (OCHEM). The dataset for QSAR modeling included 751 compounds exhibiting cytotoxic activity against RD cells. The predictive performance of the QSAR models was comprehensively validated using both cross-validation techniques and external test sets. Five compounds identified by the QSAR models as having high anticancer activity were then evaluated in vitro on RD cell lines. Based on cytotoxicity indices of the various types of non-oncogenic cell lines used in the presented in vitro study - L20B (modified mouse fibroblasts), BHK-21 (baby hamster kidney), and CEF (chicken embryonic fibroblasts) - to determine the potential selective action of the studied phosphonium salts as anticancer agents, the obtained results indicate that salt <b>2</b> can be considered the most promising object with a highly selective mechanism of anticancer action. The remaining salts, which demonstrated lower, more specific cytostatic activity and, accordingly, a less pronounced selective cytotoxicity index, can serve as useful and promising scaffolds for further research into the analysis and development of new effective anticancer agents.</p><p></p>

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QSAR modeling and in vitro studies of the selective action of triphenylphosphonium salts as anticancer agents against the rhabdomyosarcoma cell line

  • 期刊论文

  • 发表日期：

    2026年7月11日

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  • Diana Hodyna,
  • Vasyl Kovalishyn,
  • Olena Trokhimenko,
  • Serhii Soloviov,
  • Oksana Bahrieieva,
  • Maryna Kachaeva,
  • Larysa Metelytsia

摘要

The development of innovative anticancer agents is essential for addressing cancer-related complications and reducing mortality rates associated with the disease. This study employed a machine learning approach to assess the anticancer efficacy of various triphenylphosphonium salt derivatives against the rhabdomyosarcoma cell line (RD). The research used resources from the publicly accessible Online Chemical Database and Modelling Environment (OCHEM). The dataset for QSAR modeling included 751 compounds exhibiting cytotoxic activity against RD cells. The predictive performance of the QSAR models was comprehensively validated using both cross-validation techniques and external test sets. Five compounds identified by the QSAR models as having high anticancer activity were then evaluated in vitro on RD cell lines. Based on cytotoxicity indices of the various types of non-oncogenic cell lines used in the presented in vitro study - L20B (modified mouse fibroblasts), BHK-21 (baby hamster kidney), and CEF (chicken embryonic fibroblasts) - to determine the potential selective action of the studied phosphonium salts as anticancer agents, the obtained results indicate that salt 2 can be considered the most promising object with a highly selective mechanism of anticancer action. The remaining salts, which demonstrated lower, more specific cytostatic activity and, accordingly, a less pronounced selective cytotoxicity index, can serve as useful and promising scaffolds for further research into the analysis and development of new effective anticancer agents.