Predictive modeling of nanomaterial exposure effects in biological systems

Xiong Liu,1 Kaizhi Tang,1 Stacey Harper,2 Bryan Harper,2 Jeffery A Steevens,3 Roger Xu1 1Intelligent Automation, Inc., Rockville, MD, USA; 2Department of Environmental and Molecular Toxicology, School of Chemical, Biological, and Environmental Engineering, Oregon State University, Corvallis, OR, USA...

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Autores principales:	Liu X, Tang K, Harper S, Harper B, Steevens JA, Xu R
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2013
Materias:	Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/e6a8770576b74a25a56bd7d947e58878
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spelling	oai:doaj.org-article:e6a8770576b74a25a56bd7d947e588782021-12-02T00:11:49ZPredictive modeling of nanomaterial exposure effects in biological systems1176-91141178-2013https://doaj.org/article/e6a8770576b74a25a56bd7d947e588782013-09-01T00:00:00Zhttp://www.dovepress.com/predictive-modeling-of-nanomaterial-exposure-effects-in-biological-sys-a14360https://doaj.org/toc/1176-9114https://doaj.org/toc/1178-2013Xiong Liu,1 Kaizhi Tang,1 Stacey Harper,2 Bryan Harper,2 Jeffery A Steevens,3 Roger Xu1 1Intelligent Automation, Inc., Rockville, MD, USA; 2Department of Environmental and Molecular Toxicology, School of Chemical, Biological, and Environmental Engineering, Oregon State University, Corvallis, OR, USA; 3ERDC Environmental Laboratory, Vicksburg, MS, USA Background: Predictive modeling of the biological effects of nanomaterials is critical for industry and policymakers to assess the potential hazards resulting from the application of engineered nanomaterials. Methods: We generated an experimental dataset on the toxic effects experienced by embryonic zebrafish due to exposure to nanomaterials. Several nanomaterials were studied, such as metal nanoparticles, dendrimer, metal oxide, and polymeric materials. The embryonic zebrafish metric (EZ Metric) was used as a screening-level measurement representative of adverse effects. Using the dataset, we developed a data mining approach to model the toxic endpoints and the overall biological impact of nanomaterials. Data mining techniques, such as numerical prediction, can assist analysts in developing risk assessment models for nanomaterials. Results: We found several important attributes that contribute to the 24 hours post-fertilization (hpf) mortality, such as dosage concentration, shell composition, and surface charge. These findings concur with previous studies on nanomaterial toxicity using embryonic zebrafish. We conducted case studies on modeling the overall effect/impact of nanomaterials and the specific toxic endpoints such as mortality, delayed development, and morphological malformations. The results show that we can achieve high prediction accuracy for certain biological effects, such as 24 hpf mortality, 120 hpf mortality, and 120 hpf heart malformation. The results also show that the weighting scheme for individual biological effects has a significant influence on modeling the overall impact of nanomaterials. Sample prediction models can be found at http://neiminer.i-a-i.com/nei_models. Conclusion: The EZ Metric-based data mining approach has been shown to have predictive power. The results provide valuable insights into the modeling and understanding of nanomaterial exposure effects. Keywords: nanomaterial exposure effects, biological response, toxicity, embryonic zebrafish, data mining, numerical predictionLiu XTang KHarper SHarper BSteevens JAXu RDove Medical PressarticleMedicine (General)R5-920ENInternational Journal of Nanomedicine, Vol 2013, Iss Supplement 1 Nanoinformatics, Pp 31-43 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine (General) R5-920
spellingShingle	Medicine (General) R5-920 Liu X Tang K Harper S Harper B Steevens JA Xu R Predictive modeling of nanomaterial exposure effects in biological systems
description	Xiong Liu,1 Kaizhi Tang,1 Stacey Harper,2 Bryan Harper,2 Jeffery A Steevens,3 Roger Xu1 1Intelligent Automation, Inc., Rockville, MD, USA; 2Department of Environmental and Molecular Toxicology, School of Chemical, Biological, and Environmental Engineering, Oregon State University, Corvallis, OR, USA; 3ERDC Environmental Laboratory, Vicksburg, MS, USA Background: Predictive modeling of the biological effects of nanomaterials is critical for industry and policymakers to assess the potential hazards resulting from the application of engineered nanomaterials. Methods: We generated an experimental dataset on the toxic effects experienced by embryonic zebrafish due to exposure to nanomaterials. Several nanomaterials were studied, such as metal nanoparticles, dendrimer, metal oxide, and polymeric materials. The embryonic zebrafish metric (EZ Metric) was used as a screening-level measurement representative of adverse effects. Using the dataset, we developed a data mining approach to model the toxic endpoints and the overall biological impact of nanomaterials. Data mining techniques, such as numerical prediction, can assist analysts in developing risk assessment models for nanomaterials. Results: We found several important attributes that contribute to the 24 hours post-fertilization (hpf) mortality, such as dosage concentration, shell composition, and surface charge. These findings concur with previous studies on nanomaterial toxicity using embryonic zebrafish. We conducted case studies on modeling the overall effect/impact of nanomaterials and the specific toxic endpoints such as mortality, delayed development, and morphological malformations. The results show that we can achieve high prediction accuracy for certain biological effects, such as 24 hpf mortality, 120 hpf mortality, and 120 hpf heart malformation. The results also show that the weighting scheme for individual biological effects has a significant influence on modeling the overall impact of nanomaterials. Sample prediction models can be found at http://neiminer.i-a-i.com/nei_models. Conclusion: The EZ Metric-based data mining approach has been shown to have predictive power. The results provide valuable insights into the modeling and understanding of nanomaterial exposure effects. Keywords: nanomaterial exposure effects, biological response, toxicity, embryonic zebrafish, data mining, numerical prediction
format	article
author	Liu X Tang K Harper S Harper B Steevens JA Xu R
author_facet	Liu X Tang K Harper S Harper B Steevens JA Xu R
author_sort	Liu X
title	Predictive modeling of nanomaterial exposure effects in biological systems
title_short	Predictive modeling of nanomaterial exposure effects in biological systems
title_full	Predictive modeling of nanomaterial exposure effects in biological systems
title_fullStr	Predictive modeling of nanomaterial exposure effects in biological systems
title_full_unstemmed	Predictive modeling of nanomaterial exposure effects in biological systems
title_sort	predictive modeling of nanomaterial exposure effects in biological systems
publisher	Dove Medical Press
publishDate	2013
url	https://doaj.org/article/e6a8770576b74a25a56bd7d947e58878
work_keys_str_mv	AT liux predictivemodelingofnanomaterialexposureeffectsinbiologicalsystems AT tangk predictivemodelingofnanomaterialexposureeffectsinbiologicalsystems AT harpers predictivemodelingofnanomaterialexposureeffectsinbiologicalsystems AT harperb predictivemodelingofnanomaterialexposureeffectsinbiologicalsystems AT steevensja predictivemodelingofnanomaterialexposureeffectsinbiologicalsystems AT xur predictivemodelingofnanomaterialexposureeffectsinbiologicalsystems
_version_	1718403898023084032

Predictive modeling of nanomaterial exposure effects in biological systems

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