Meta-analysis of cellular toxicity for graphene via data-mining the literature and machine learning

Abstract

Since graphene is currently incorporated into various consumer products and used in a variety of applications, determining the relationships between the physicochemical properties of graphene and its toxicity is critical for conducting environmental and health risk analyses. Data from the literature suggest that exposure to graphene may result in cytotoxicity. However, existing graphene toxicity data are complex and heterogeneous, making it difficult to conduct risk assessments. Here, we conducted a meta-analysis of published data on the cytotoxicity of graphene based on 792 publications, including 986 cell viability data points, 762 half maximal inhibitory concentration (IC₅₀) data points, and 100 lactate dehydrogenase (LDH) release data points. Models to predict graphene cytotoxicity were then developed based on cell viability, IC₅₀, and LDH release as toxicity endpoints using random forests learning algorithms. The most influential attributes influencing graphene cytotoxicity were revealed to be exposure dose and detection method for cell viability, diameter and surface modification for IC₅₀, and detection method and organ source for LDH release. The meta-analysis produced three sets of key attributes for the three abovementioned toxicity endpoints that can be used in future studies of graphene toxicity. The findings indicate that rigorous data mining protocols can be combined with suitable machine learning tools to develop models with good predictive power and accuracy. The results also provide guidance for the design of safe graphene materials.

Keywords: Cytotoxicity; Graphene; Machine learning; Meta-analysis; Random forests.