Exploring the cytotoxic and antioxidant properties of lanthanide-doped ZnO nanoparticles: a study with machine learning interpretation.

Background: Lanthanide-based nanomaterials offer a promising alternative for cancer therapy because of their selectivity and effectiveness, which can be modified and predicted by leveraging the improved accuracy and enhanced decision-making of machine learning (ML) modeling.

Methods: In this study, erbium (Er) and ytterbium (Yb) were used to dope zinc oxide (ZnO) nanoparticles (NPs). Various characterization techniques and biological assays were employed to investigate the physicochemical and optical properties of the (Er, Yb)-doped ZnO NPs, revealing the influence of the lanthanide elements.

Results: The (Er, Yb)-doped ZnO NPs exhibited laminar-type morphologies, negative surface charges, and optical bandgaps that vary with the presence of Er and Yb. The incorporation of lanthanide ions reduced the cytotoxicity activity of ZnO against HEPG-2, CACO-2, and U87 cell lines. Conversely, doping with Er and Yb enhanced the antioxidant activity of the ZnO against DPPH, ABTS, and HO radicals. The extra tree (ET) and random forest (RF) models predicted the relevance of the characterization results vis-à-vis the cytotoxic properties of the synthesized NPs.

Conclusion: This study demonstrates, for the first time, the synthesis of ZnO NPs doped with Er and Yb via a solution polymerization route. According to characterization results, it was unveiled that the effect of optical bandgap variations influenced the cytotoxic performance of the developed lanthanide-doped ZnO NPs, being the undoped ZnO NPs the most cytotoxic ones. The presence alone or in combination of Er and Yb enhanced their scavenging capacity. ML models such as ET and RF efficiently demonstrated that the concentration and cell line type are key parameters that influence the cytotoxicity of (Er, Yb)-doped ZnO NPs achieving high accuracy rates of 98.96% and 98.67%, respectively. This study expands the knowledge of lanthanides as dopants of nanomaterials for biological and medical applications and supports their potential in cancer therapy by integrating robust ML approaches.