Machine learning methods for multi-walled carbon nanotubes (MWCNT) genotoxicity prediction.

Multi-walled carbon nanotubes (MWCNTs) are made of multiple single-walled carbon nanotubes (SWCNTs) which are nested inside one another forming concentric cylinders. These nanomaterials are widely used in industrial and biomedical applications, due to their unique physicochemical characteristics. However, previous studies have shown that exposure to MWCNTs may lead to toxicity and some of the physicochemical properties of MWCNTs can influence their toxicological profiles. In silico modelling can be applied as a faster and less costly alternative to experimental (in vivo and in vitro) testing for the hazard characterization of MWCNTs. This study aims at developing a fully validated predictive nanoinformatics model based on statistical and machine learning approaches for the accurate prediction of genotoxicity of different types of MWCNTs. Towards this goal, a number of different computational workflows were designed, combining unsupervised (Principal Component Analysis, PCA) and supervised classification techniques (Support Vectors Machine, "SVM", Random Forest, "RF", Logistic Regression, "LR" and Naïve Bayes, "NB") and Bayesian optimization. The Recursive Feature Elimination (RFE) method was applied for selecting the most important variables. An RF model using only three features was selected as the most efficient for predicting the genotoxicity of MWCNTs, exhibiting 80% accuracy on external validation and high classification probabilities. The most informative features selected by the model were "Length", "Zeta average" and "Purity".

QSAR modeling of the toxicity classification of superparamagnetic iron oxide nanoparticles (SPIONs) in stem-cell monitoring applications: an integrated study from data curation to model development.

The use of in silico approaches for the prediction of biomedical properties of nano-biomaterials (NBMs) can play a significant role in guiding and reducing wetlab experiments. Computational methods, such as data mining and machine learning techniques, can increase the efficiency and reduce the time and cost required for hazard and risk assesment and for designing new safer NBMs. A major obstacle in developing accurate and well-validated in silico models such as Nano Quantitative Structure-Activity Relationships (Nano-QSARs) is that although the volume of data published in the literature is increasing, the data are fragmented in many different publications and are not sufficiently curated for modelling purposes. Moreover, NBMs exhibit high complexity and heterogeneity in their structures, making data collection and curation and QSAR model development more challenging compared to traditional small molecules. The aim of this study was to construct and fully validate a Nano-QSAR model for the prediction of toxicological properties of superparamagnetic iron oxide nanoparticles (SPIONs), focusing on their application as Magnetic Resonance Imaging (MRI) contrast agents for non-invasive stem cell labelling and tracking. To achieve this goal, we first performed an extensive search through the literature for collecting and curating relevant data and we developed a dataset containing both physicochemical and toxicological properties of SPIONs. The data were analysed next, using Automated machine learning (Auto-ML) approaches for optimising the development and validation of nanotoxicity classification QSAR models of SPIONs. Further analysis of relative attribute importances revealed that physicochemical properties such as the size and the magnetic core are the dominant attributes correlated to the toxicity of SPIONs. Our results suggest that as more systematic information from NBM experimental tests becomes available, computational tools could play an important role in supporting the safety-by-design (SbD) concept in regenerative medicine and disease therapeutics.