Evaluating the cytotoxicity of a large pool of metal oxide nanoparticles to Escherichia coli: Mechanistic understanding through In Vitro and In Silico studies

Abstract

The toxic effect of eight metal oxide nanoparticles (MONPs) on Escherichia coli was experimentally evaluated following standard bioassay protocols. The obtained cytotoxicity ranking of these studied MONPs is Er₂O₃, Gd₂O₃, CeO₂, Co₂O₃, Mn₂O₃, Co₃O₄, Fe₃O₄/WO₃ (in descending order). The computed EC₅₀ values from experimental data suggested that Er₂O₃ and Gd₂O₃ were the most acutely toxic MONPs to E. coli. To identify the mechanism of toxicity of these 8 MONPs along with 17 other MONPs from our previous study, we employed seven classifications and machine learning (ML) algorithms including linear discriminant analysis (LDA), naïve bayes (NB), multinomial logistic regression (MLogitR), sequential minimal optimization (SMO), AdaBoost, J48, and random forest (RF). We also employed 1^st and 2^nd generation periodic table descriptors developed by us (without any sophisticated computing facilities) along with experimentally analyzed Zeta-potential, to model the cytotoxicity of these MONPs. Based on qualitative validation metrics, the LDA model appeared to be the best among the 7 tested models. The core environment of metal defined by the ratio of the number of core electrons to the number of valence electrons and the electronegativity count of oxygen showed a positive impact on toxicity. The identified properties were important for understanding the mechanisms of nanotoxicity and for predicting the potential environmental risk associated with MONPs exposure. The developed models can be utilized for environmental risk assessment of any untested MONP to E. coli, thereby providing a scientific basis for the design and preparation of safe nanomaterials.

Keywords: Classification; In silico; In vitro; Machine learning; Metal oxide; Nanoparticles; Toxicity.

Fig. 1.

TEM images of all 25 MONPs.

Fig. 2.

Comparison of classical validation metrics outcome among seven classification-based models.

Fig. 3.

(Top) PDD for the training set and the test set; (Middle) Contribution plot for indices to the discriminant functions for higher and lower toxic groups (left) and Euclidean distance-based AD plot (right); and (Bottom) ROC curve of training and test sets where red dots and blue line define fitted ROC curve and grey line signifies random guess. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)