TY - JOUR
T1 - Evaluating the cytotoxicity of a large pool of metal oxide nanoparticles to Escherichia coli
T2 - Mechanistic understanding through In Vitro and In Silico studies
AU - Kar, Supratik
AU - Pathakoti, Kavitha
AU - Tchounwou, Paul B.
AU - Leszczynska, Danuta
AU - Leszczynski, Jerzy
N1 - Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2021/2
Y1 - 2021/2
N2 - 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 1st and 2nd 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.
AB - 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 1st and 2nd 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.
KW - Classification
KW - In silico
KW - In vitro
KW - Machine learning
KW - Metal oxide
KW - Nanoparticles
KW - Toxicity
UR - http://www.scopus.com/inward/record.url?scp=85092060602&partnerID=8YFLogxK
U2 - 10.1016/j.chemosphere.2020.128428
DO - 10.1016/j.chemosphere.2020.128428
M3 - Article
C2 - 33022504
AN - SCOPUS:85092060602
SN - 0045-6535
VL - 264
JO - Chemosphere
JF - Chemosphere
M1 - 128428
ER -