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Predictive modeling of nanomaterial exposure effects in biological systems.

TitlePredictive modeling of nanomaterial exposure effects in biological systems.
Publication TypeJournal Article
Year of Publication2013
AuthorsLiu X, Tang K, Harper S, Harper B, Steevens JA, Xu R
JournalInt J Nanomedicine
Volume8 Suppl 1
Date Published2013

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
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.

Alternate JournalInt J Nanomedicine
PubMed ID24098077
PubMed Central IDPMC3790277
Grant ListES016896-01 / ES / NIEHS NIH HHS / United States
ES017552-01A2 / ES / NIEHS NIH HHS / United States
P30 ES03850 / ES / NIEHS NIH HHS / United States
Project Reference: 
Zebrafish EZ Metric Assay
Nanomaterial-Biological Interactions Knowledgebase

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