Nanosafety-data-reusability-34-datasets

Machine learning predictions of concentration-specific aggregate hazard scores of inorganic nanomaterials in embryonic zebrafish (2021)

• Authors: C. Gousiadou and R. L. Marchese Robinson and M. Kotzabasaki and P. Doganis and T. A. Wilkins and X. Jia and H. Sarimveis and S. L. Harper
• Journal: Nanotoxicology
• Article Type: journal-article
• Dataset Type: Environmetal Datasets
• Date: 2021-02-15
• License: https://creativecommons.org/licenses/by/4.0/
• URL: https://www.tandfonline.com/doi/abs/10.1080/17435390.2021.1872113
• DOI: 10.1080/17435390.2021.1872113

Original Study Abstract

The possibility of employing computational approaches like nano-QSAR or nano-read-across to predict nanomaterial hazard is attractive from both a financial, and most importantly, where in vivo tests are required, ethical perspective. In the present work, we have employed advanced Machine Learning techniques, including stacked model ensembles, to create nano-QSAR tools for modeling the toxicity of metallic and metal oxide nanomaterials, both coated and uncoated and with a variety of different core compositions, tested at different dosage concentrations on embryonic zebrafish. Using both computed and experimental descriptors, we have identified a set of properties most relevant for the assessment of nanomaterial toxicity and successfully correlated these properties with the associated biological responses observed in zebrafish. Our findings suggest that for the group of metal and metal oxide nanomaterials, the core chemical composition, concentration and properties dependent upon nanomaterial surface and medium composition (such as zeta potential and agglomerate size) are significant factors influencing toxicity, albeit the ranking of different variables is sensitive to the exact analysis method and data modeled. Our generalized nano-QSAR ensemble models provide a promising framework for anticipating the toxicity potential of new nanomaterials and may contribute to the transition out of the animal testing paradigm. However, future experimental studies are required to generate comparable, similarly high quality data, using consistent protocols, for well characterized nanomaterials, as per the dataset modeled herein. This would enable the predictive power of our promising ensemble modeling approaches to be robustly assessed on large, diverse and truly external datasets.

Data Sample

NBI ID	Core	Surface.Charge…positive..negative..neutral.	Purity	Core.Shape	MW_FG	XlogP_FG	MW_Core	pseudomol	MC	Zeta.potential	Stable.Average.Agglomerate.Size.in.Media	Surface.Area..Core…Shell…Ligands…mm2..	Concentration..ppm.	Primary.Particle.Size..Avg…nm.	Core..Heavy.Atom.Count	Core..HBAcc	Pauling.metal.atom.electronegativity	FG..GAP..EV.	FG..McGowanVolume	FG..SMR	Weighted.EZ.METRIC	Additive.EZ.METRIC	FIELD24
87	ZnO	unknown	unknown	unknown	NA	NA	81,4	0,024570025	40,7	NA	NA	NA	2	NA	2	1	1,65	NA	NA	NA	0,17	0,17
87	ZnO	unknown	unknown	unknown	NA	NA	81,4	0,122850123	40,7	NA	NA	NA	10	NA	2	1	1,65	NA	NA	NA	0,12	0,12
87	ZnO	unknown	unknown	unknown	NA	NA	81,4	0,614250614	40,7	NA	NA	NA	50	NA	2	1	1,65	NA	NA	NA	0,25	0,29
87	ZnO	unknown	unknown	unknown	NA	NA	81,4	3,071253071	40,7	NA	NA	NA	250	NA	2	1	1,65	NA	NA	NA	0,29	0,29
128	Fe2O3	negative	unknown	unknown	NA	NA	159,69	0,01252427	4,669298	NA	NA	NA	2	110	5	3	1,83	NA	NA	NA	0,2	1,5

Data Summary

Variable	Count (unique values)
Core	4

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