Jeremy Gernand, Elizabeth Casman 9 May 2011 ICEIN 2011 – Duke University

1. Identifying Information-Rich Attributes for Nanomaterial Risk AssessmentAn Analysis of Carbon Nanotube Inhalation Toxicity Experiments Jeremy Gernand, Elizabeth Casman 9 May 2011 ICEIN 2011 – Duke University

2. Will carbon nanotube exposure induce asbestos-like effects? • Asbestos exposure causes lung disease and cancer due to frustrated phagocytosis (fibers cannot be removed by macrophages) • Aggregated CNTs could cause similar effects • CNTs documented to cause granulomas but not mesotheliomas in lungs [Varga and Szendi (2010) In Vivo] • Lung inflammation and fibrotic responses but no carcinogenicity due to CNT exposure [Muller et al. (2009) Tox. Sci.] Kane & Hunt (2008) Nature Nanotech. TEM image of CNT aggregate recovered from inhalation chamber [Pauluhn J., Tox. Sci. (2010)]

3. Total dose of CNTs only partially explains the observed toxic effects • While toxic responses to CNTs are related to dose, other attributes must explain the wide variability in experimental results • Identifying the physical or experimental attributes responsible for these variations can inform risk models

4. Existing CNT experiments are not consistent in characterization measures Uncertainty exists regarding the precise form of the nanomaterial at time of exposure due to inconsistent characterization between studies Published Carbon Nanotube Toxicity Experiments

5. Objective and Approach • Develop a quantitative measure of information value for the array of measurable nanomaterial properties and experimental parameters • Regression trees (RTs)* successively divide a population of observations by the use of available input variables to achieve the greatest entropy (information) gain • RTs makeno assumptions of independence between input variables • Minimize problems caused by missing data • Identification of nonlinear relationships is possible • Generate a series of simple models and prevented from overfitting through crossvalidation and pruning *Breiman, L., Friedman, J., Stone, C., Olshen, R.A., Classification and Regression Trees . (1984)

6. Data include CNT characterization, exposure attributes, quantitative toxicological endpoints

7. 7 quantitative endpoints included sufficient quality and quantity of data

8. Regression tree model for BAL total cell count shows dependence on geometry Small Particle Fraction < 67.5 % (11-92 nm) x̅ = 17 ± 25 KEY Input Variable <Split Value x̅ = mean value of output at node ± standard deviation (# of obs. @ node) (176) true false R2 = 0.94 Aggregate Dia. (MMAD) < 1920 nm x̅ = 16 ± 12 Length, 5th % < 85 nm x̅ = 17 ± 29 (54) (122) BAL ~ bronchoalveolarlavage Aggregate Dia. (MMAD) < 1790 nm x̅ = 8 ± 3 Recovery Period < 14.5 days x̅ = 30 ± 11 N = 18 x̅ = 79 ± 34 N = 104 x̅ = 7 ± 6 (36) (18) N = 18 x̅ = 6 ± 1 N = 18 x̅ = 10 ± 2 N = 6 x̅ = 43 ± 8 N = 12 x̅ = 24 ± 5

9. Simple regression trees compare favorably to more complex linear models

10. Reduction in error provided by each branch in the tree indicates attribute value • Comparing the total error of the two child nodes to the error of the parent node indicates the degree of information gain provided by each branch variable Small Particle Fraction Agg. Dia. (MMAD) Length, 5th% Agg. Dia. (MMAD) Recovery Period

11. A series of RTs in a random forest (RF)* reveals sensitivity of attribute importance BAL Total Cell Count Variables included in best individual tree *Breiman, L., “Random Forests”. Machine Learning. 45:5-32. (2001)

12. %wt Oxidized Carbon < 1.3 % x̅ = 310 ± 270 Tree for BAL total protein uniquely displays dependence on CNT impurities KEY Dose Co < 0.77 pg/kg x̅ = 280 ± 180 N = 5 x̅ = 1480 ± 190 Input Variable <Split Value x̅ = mean value of output at node ± standard deviation (190) (# of obs. @ node) true false (185) R2 = 0.92 Dose Co < 0.22 pg/kg x̅ = 170 ± 70 Dose Fe < 3.1 pg/kg x̅ = 440 ± 190 (73) (112) Particle Count Conc. < 198 #/cm3 x̅ = 150 ± 30 Recovery Per. < 12 days x̅ = 250 ± 90 Recovery Per. < 2 days x̅ = 360 ± 130 N = 30 x̅ = 560 ± 180 (84) (28) (43) N = 18 x̅ = 120 ± 20 N = 66 x̅ = 150 ± 30 N = 11 x̅ = 340 ± 70 N = 17 x̅ = 190 ± 30 N = 6 x̅ = 530 ± 70 N = 37 x̅ = 330 ± 120

13. BAL total protein sensitivity testing confirms influence of impurity doses Variables included in best tree

14. Major findings address current questions in CNT toxicity experiments • CNT geometry including aggregate size distribution explains some of the variance in toxicity results • More small particles and larger aggregates both were associated with increased toxicity for some measures • Exposure mode (aspiration, inhalation, instillation) is not as important as dose and CNT geometry for explaining these toxicity outcomes • Only BAL total protein was dependent primarily on metallic impurities, for other toxicity measures, impurities played a minor role, if any • Surface area did not explain as much variation in toxicity outcomes as expected

15. Surface area measurement may not be biologically relevant quantity for CNTs • Measured surface area does not decrease as expected with increased aggregation from J. Pauluhn (2010)

16. Recommendations • Include distributions of particle and aggregate sizes to improve variable importance and characterization standards • Improve surface area characterization for CNT aggregates to better reflect likely biological interactions • Continuing variable importance analysis to include additional nanomaterials, exposure routes, and target subjects

17. Acknowledgements This material is based upon work supported by the National Science Foundation (NSF) and the Environmental Protection Agency (EPA) under NSF Cooperative Agreement EF-0830093, Center for the Environmental Implications of NanoTechnology (CEINT).