Limitations on the reliability of in vitro predictive toxicity models to predict pulmonary toxicity in rodents

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Given the rapidly proliferating varieties of nanomaterials and ongoing concerns that these novel materials may pose emerging occupational and environmental risks, combined with the possibility that each variety might pose a different unique risk due to the unique combination of material properties, researchers and regulators have been searching for methods to identify hazards and prioritize materials for further testing. While several screening tests and toxic risk models have been proposed, most have relied on cellular-level in vitro data. This foundation enables answers to be developed quickly for any material, but it is yet unclear how this information may translate to more realistic exposure scenarios in people or other more complex animals. A quantitative evaluation of these models or at least the inputs variables to these models in the context of rodent or human health outcomes is necessary before their classifications may be believed for the purposes of risk prioritization. This paper presents the results of a machine learning enabled metaanalysis of animal studies attempting to use significant descriptors from in vitro nanomaterial risk models to predict the relative toxicity of nanomaterials following pulmonary exposures in rodents. A series of highly non-linear random forest models (each made up of an ensemble of 1,000 regression tree models) were created to assess the maximum possible information value of the in vitro risk models and related methods of describing nanomaterial variants and their toxicity in rat and mouse experiments. The variety of chemical descriptors or quantitative chemical property measurements such as bond strength, surface charge, and dissolution potential, while important in describing observed differences with in vitro experiments, proved to provide little indication of the relative magnitude of inflammation in rodents (explained variance amounted to less than 32%). Important factors in predicting rodent pulmonary inflammation such as primary particle size and chemical type demonstrate that there are critical differences between these two toxicity assays that cannot be captured by a series of in vitro tests alone. Predictive models relying primarily on these descriptors alone explained more than 62% of the variance of the short term in vivo toxicity results. This means that existing proposed nanomaterial toxicity screening methods are inadequate as they currently stand, and either the community must be content with the slower and more expensive animal testing to evaluate nanomaterial risks, or further conceptual development of improved alternative in vitro screening methodologies is necessary before manufacturers and regulators can rely on them to promote safer use of nanotechnology.

Original languageEnglish (US)
Title of host publicationEmerging Technologies; Materials
Subtitle of host publicationGenetics to Structures; Safety Engineering and Risk Analysis
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791850688
DOIs
StatePublished - Jan 1 2016
EventASME 2016 International Mechanical Engineering Congress and Exposition, IMECE 2016 - Phoenix, United States
Duration: Nov 11 2016Nov 17 2016

Publication series

NameASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)
Volume14

Other

OtherASME 2016 International Mechanical Engineering Congress and Exposition, IMECE 2016
CountryUnited States
CityPhoenix
Period11/11/1611/17/16

Fingerprint

Toxicity
Nanostructured materials
Screening
Animals
Rodentia
Testing
Surface charge
Nanotechnology
Chemical properties
Learning systems
Rats
Assays
Materials properties
Hazards
Dissolution
Experiments
Particle size
Health

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering

Cite this

Gernand, J. M. (2016). Limitations on the reliability of in vitro predictive toxicity models to predict pulmonary toxicity in rodents. In Emerging Technologies; Materials: Genetics to Structures; Safety Engineering and Risk Analysis (ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE); Vol. 14). American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/IMECE201667151
Gernand, Jeremy Michael. / Limitations on the reliability of in vitro predictive toxicity models to predict pulmonary toxicity in rodents. Emerging Technologies; Materials: Genetics to Structures; Safety Engineering and Risk Analysis. American Society of Mechanical Engineers (ASME), 2016. (ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)).
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Gernand, JM 2016, Limitations on the reliability of in vitro predictive toxicity models to predict pulmonary toxicity in rodents. in Emerging Technologies; Materials: Genetics to Structures; Safety Engineering and Risk Analysis. ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE), vol. 14, American Society of Mechanical Engineers (ASME), ASME 2016 International Mechanical Engineering Congress and Exposition, IMECE 2016, Phoenix, United States, 11/11/16. https://doi.org/10.1115/IMECE201667151

Limitations on the reliability of in vitro predictive toxicity models to predict pulmonary toxicity in rodents. / Gernand, Jeremy Michael.

Emerging Technologies; Materials: Genetics to Structures; Safety Engineering and Risk Analysis. American Society of Mechanical Engineers (ASME), 2016. (ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE); Vol. 14).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Gernand JM. Limitations on the reliability of in vitro predictive toxicity models to predict pulmonary toxicity in rodents. In Emerging Technologies; Materials: Genetics to Structures; Safety Engineering and Risk Analysis. American Society of Mechanical Engineers (ASME). 2016. (ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)). https://doi.org/10.1115/IMECE201667151