title: Comparing the CORAL and Random Forest approaches for modelling the in vitro cytotoxicity of silica nanomaterials.
abstract: Nanotechnology is one of the most important technological developments of the 21st century. In silico methods to predict toxicity, such as quantitative structure-activity relationships (QSARs), promote the safe-by-design approach for the development of new materials, including nanomaterials. In this study, a set of cytotoxicity experimental data corresponding to 19 data points for silica nanomaterials were investigated, to compare the widely employed CORAL and Random Forest approaches in terms of their usefulness for developing so-called 'nano-QSAR' models. 'External' leave-one-out cross-validation (LOO) analysis was performed, to validate the two different approaches. An analysis of variable importance measures and signed feature contributions for both algorithms was undertaken, in order to interpret the models developed. CORAL showed a more pronounced difference between the average coefficient of determination (R²) for training and for LOO (0.83 and 0.65 for training and LOO, respectively), compared to Random Forest (0.87 and 0.78 without bootstrap sampling, 0.90 and 0.78 with bootstrap sampling), which may be due to overfitting. With regard to the physicochemical properties of the nanomaterials, the aspect ratio and zeta potential were found to be the two most important variables for Random Forest, and the average feature contributions calculated for the corresponding descriptors were consistent with the clear trends observed in the data set: less negative zeta potential values and lower aspect ratio values were associated with higher cytotoxicity. In contrast, CORAL failed to capture these trends.
authors.affiliation: School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK.
authors.forename: Antonio
authors.initials: A
authors.lastname: Cassano
authors.affiliation: School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK.
authors.forename: Richard L
authors.initials: RL
authors.lastname: Marchese Robinson
authors.affiliation: University of Leeds, School of Geography, Leeds, UK.
authors.forename: Anna
authors.initials: A
authors.lastname: Palczewska
authors.affiliation: Laboratory of Environmental Chemistry, University of Gdansk, Gdansk, Poland.
authors.forename: Tomasz
authors.initials: T
authors.lastname: Puzyn
authors.affiliation: Laboratory of Environmental Chemistry, University of Gdansk, Gdansk, Poland.
authors.forename: Agnieszka
authors.initials: A
authors.lastname: Gajewicz
authors.affiliation: Institute of Occupational Medicine, Edinburgh, Midlothian, UK.
authors.forename: Lang
authors.initials: L
authors.lastname: Tran
authors.affiliation: IRCSS-Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy.
authors.forename: Serena
authors.initials: S
authors.lastname: Manganelli
authors.affiliation: School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK.
authors.forename: Mark T D
authors.initials: MT
authors.lastname: Cronin
mesh_terms: D008962:Models, Theoretical; D049329:Nanostructures / Q000633:toxicity*; D021281:Quantitative Structure-Activity Relationship; D012822:Silicon Dioxide / Q000633:toxicity*; D018675:Toxicity Tests
chemical_list: D012822:Silicon Dioxide