Meta-analysis of cellular toxicity for cadmium-containing quantum dots

Understanding the relationships between the physicochemical properties of engineered nanomaterials and their toxicity is critical for environmental and health risk analysis. However, this task is confounded by material diversity, heterogeneity of published data and limited sampling within individual studies. Here, we present an approach for analysing and extracting pertinent knowledge from published studies focusing on the cellular toxicity of cadmium-containing semiconductor quantum dots. From 307 publications, we obtain 1,741 cell viability-related data samples, each with 24 qualitative and quantitative attributes describing the material properties and experimental conditions. Using random forest regression models to analyse the data, we show that toxicity is closely correlated with quantum dot surface properties (including shell, ligand and surface modifications), diameter, assay type and exposure time. Our approach of integrating quantitative and categorical data provides a roadmap for interrogating the wide-ranging toxicity data in the literature and suggests that meta-analysis can help develop methods for predicting the toxicity of engineered nanomaterials. Literature data mining and knowledge extraction (known as meta-analysis) can be used to derive the relationships between toxicological outcome and the physicochemical properties of cadmium-containing quantum dots.