Cytotoxicity of phytosynthesized silver nanoparticles: A meta-analysis by machine learning algorithms

Abstract

The rapid development and increasing use of silver nanoparticles (AgNPs) synthesized by “green” methods such as by plants for biomedical, textile and healthcare applications has raised questions regarding their potential impacts on human health. This study presents a meta-analysis of the cytotoxicity data of phytosynthesized AgNPs with heterogenous features from literature using two classification-based machine learning approaches, decision tree (DT) and random forest (RF). The inclusion of plant family as a biosynthesis-related feature clearly improved the accuracy and generalization performance of DT and RF models, revealing the potential impact of biosynthesizing parameters on the cytotoxic effects of phytosynthesized AgNPs. A measure of the mean decrease Gini in the RF modeling identified that exposure regime (including time and dose), plant family, and cell type were the most important predictors for the cell viability outcomes of green AgNPs. Further, the potential effects of major variables (cell assays, intrinsic nanoparticle properties, and reaction parameters used in biosynthesis) on AgNPs-mediated cytotoxicity and model performance were discussed to provide a basis for future work. Thus, this meta-analysis of published data by machine learning algorithms provides guidance and prediction to key variables affecting AgNP-mediated cytotoxicity, which may help direct future studies toward better experimental design as well as the virtual design or optimization of green AgNPs for specific applications.