A Predictive Bayesian Dose-Response Assessment for Evaluating the Toxicity of Carbon Nanotubes Relative to Crocidolite Using a Proposed Emergent Model

ABSTRACT

Carbon Nanotubes (CNTs) are a product of the nanotechnology revolution and show great promise in industrial applications. However, their relative toxicity is still not well understood and has drawn comparison to asbestos fibers due to their size and shape. In this study, a predictive Bayesian dose-response assessment was conducted with extremely limited initial dose-response data to compare the toxicity of long-fiber CNTs with that of crocidolite, an asbestos fiber associated with human mesothelioma. In the assessment, a new, theoretically derived emergent dose-response model was used and compared with the single-hit and multistage models. The multistage and emergent DRFs were selected for toxicity assessment based on two criteria: visual fit to several datasets, and a goodness-of-fit test using an available data-rich study with crocidolite. The predictive assessment supports previous concerns that long-fiber CNTs have toxicity comparable to crocidolite in intratracheal and intraperitoneal applications. Collection of further dose-response data on these materials is strongly recommended.

Key Words:

• carbon nanotubes
• crocidolite
• toxicity
• Bayesian
• Markov Chain Monte Carlo

ACKNOWLEDGMENTS

Dr. David Chin is thanked for his insight into parameter selection and the MLE. Drs. Anna Shvedova and Val Kagan are thanked for their advice and expertise in inhalation analysis of CNT toxicity. Dr. Robert Bolton is thanked for his insight into the experiment performed by Davis et al. (1991). Dr. Agnes Kane is thanked for her insight into the experiment performed by Marsella et al. (1997).