Meta-Analysis of Nanoparticle Cytotoxicity via Data-Mining the Literature

Developing
predictive modeling frameworks of potential cytotoxicity
of engineered nanoparticles is critical for environmental and health
risk analysis. The complexity and the heterogeneity of available data
on potential risks of nanoparticles, in addition to interdependency
of relevant influential attributes, makes it challenging to develop
a generalization of nanoparticle toxicity behavior. Lack of systematic
approaches to investigate these risks further adds uncertainties and
variability to the body of literature and limits generalizability
of existing studies. Here, we developed a rigorous approach for assembling
published evidence on cytotoxicity of several organic and inorganic
nanoparticles and unraveled hidden relationships that were not targeted
in the original publications. We used a machine learning approach
that employs decision trees together with feature selection algorithms
(e.g., Gain ratio) to analyze a set of published
nanoparticle cytotoxicity sample data (2896 samples). The specific
studies were selected because they specified nanoparticle-, cell-,
and screening method-related attributes. The resultant decision-tree
classifiers are sufficiently simple, accurate, and with high prediction
power and should be widely applicable to a spectrum of nanoparticle
cytotoxicity settings. Among several influential attributes, we show
that the cytotoxicity of nanoparticles is primarily predicted from
the nanoparticle material chemistry, followed by nanoparticle concentration
and size, cell type, and cytotoxicity screening indicator. Overall,
our study indicates that following rigorous and transparent methodological
experimental approaches, in parallel to continuous addition to this
data set developed using our approach, will offer higher predictive
power and accuracy and uncover hidden relationships. Results obtained
in this study help focus future studies to develop nanoparticles that
are safe by design.