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

Abstract

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.

Machine learning predicts the functional composition of the protein corona and the cellular recognition of nanoparticles

“…Extraction of the data regarding the protein coronas on NPs was performed according to the workflow described in the Methods and SI Appendix. To reduce publication bias and extract information from distinct experimental conditions, strict criteria were applied in the literature extraction and data mining (shown in the Methods) (15,28). Overall, 652 pieces of data related to the protein coronas on various NPs were mined and analyzed.…”

“…The limited amount of data and high heterogeneity were the major factors limiting the prediction accuracy of traditional statistical approaches and machine learning models (15,28). As observed in SI Appendix, Figs.…”

Predicting Cytotoxicity of Metal Oxide Nanoparticles Using Isalos Analytics Platform

“…In the current study, we used a subset of the data for modelling from the aforementioned single-parameter ATP and LDH assays carried out in BEAS-2B and RAW 264.7 cell lines. The type of assay (ATP or LDH) was included as an extra parameter, since significant correlation was identified in various instances [ 60 , 63 , 64 , 65 ] between the type of assay and cytotoxicity results. In total, 15 descriptors (independent variables) originating from Zhang et al (2012) were included in the analysis: 6 physicochemical (chemical formula, core size, specific surface area, total surface area, hydrodynamic size and ζ-potential), 6 molecular ( E V , E C , E g , χ cation , χ oxide and E ΔH ) and 3 assay-related (assay type, cell species and NP exposure dose) descriptors.…”

Practices and Trends of Machine Learning Application in Nanotoxicology

“…Trees are simple to understand and interpret and can be used even with small datasets. They are unaffected by data shortcomings that result in small changes of the outcome and are associated with high dimensionality, correlated variables, and missing values [66]. RF has been demonstrated to be ideal for rigorous meta-analysis of complex and heterogeneous data [64].…”