Toxicity profiling of engineered nanomaterials via multivariate dose-response surface modeling

Annals of Applied Statistics

Volumn 6, Issue 4, 2012, Pages 1707-1729

  Patel, Trina ^a   Telesca, Donatello ^a   George, Saji ^b   Nel, André E ^b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Additive models; Dose response models; Hierarchical models; Multivariate; Nanotoxicology

Indexed keywords

EID: 84878393628     PISSN: 19326157     EISSN: 19417330     Source Type: Journal    
DOI: 10.1214/12-AOAS563     Document Type: Article

References (38)

1. Baladandayuthapani, V., Mallick, B. K. and Carroll, R. J. (2005). Spatially adaptive Bayesian penalized regression splines (P-splines). J. Comput. Graph. Statist. 14 378-394. MR2160820
2. Besag, J. and Higdon, D. (1999). Bayesian analysis of agricultural field experiments. J. R. Stat. Soc. Ser. B Stat. Methodol. 61 691-746. MR1722238
3. Calabrese, E. and Baldwin, L. (2003). Toxicology rethinks its central belief. Nature 421 691-692.
4. Emmens, C. (1940). The dose-response relation for certain principles of the pituitary gland, and of the serum and urine of pregnancy. Journal of Endocrinology 2 194-225.
5. Finney, D. J. (1979). Bioassay and the practice of statistical inference. Internat. Statist. Rev. 47 1-12. MR0539067
6. Gelfand, A. E. and Smith, A. F. M. (1990). Sampling-based approaches to calculating marginal densities. J. Amer. Statist. Assoc. 85 398-409. MR1141740
7. Gelman, A. (2006). Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper). Bayesian Anal. 1 515-533 (electronic). MR2221284
8. Geman, S. and Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence 6 721-741.
9. George, S., Pokhrel, S., Xia, T., Gilbert, B., Ji, Z., Schowalter, M., Rosenauer, A., Damoiseaux, R., Bradley, K., Madler, L. andNel, A. (2009). Use of a rapid cytotoxicity screening approach to engineer a safer zinc oxide nanoparticle through iron doping. ACS Nano 4 15-29.
10. George, S., Xia, T., Rallo, R., Zhao, Y., Ji, Z., Lin, S., Wang, X., Zhang, H., France, B., Schoenfeld, D., Damoiseaux, R., Liu, R., Lin, S., Bradley, K., Cohen, Y. andNel, A. (2011). Use of a high-throughput screening approach coupled with in vivo zebrafish embryo screening to develop hazard ranking for engineered nanomaterials. ACS Nano 5 1805-1817.
11. Geys, H., Regan, M., Catalano, P. and Molenberghs, G. (2001). Two latent variable risk assessment approaches or mixed continuous and discrete outcomes from developmental toxicity data. J. Agric. Biol. Environ. Stat. 6 340-355.
12. Gneiting, T., Balabdaoui, F. and Raftery, A. E. (2007). Probabilistic forecasts, calibration and sharpness. J. R. Stat. Soc. Ser. B Stat. Methodol. 69 243-268. MR2325275
13. Green, P. J. (1995). Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 82 711-732. MR1380810
14. Hastie, T. and Tibshirani, R. (1986). Generalized additive models. Statist. Sci. 1 297-318. MR0858512
15. Hill, A. (1910). The possible effects of the aggregation of the molecules of haemoglobin on its dissociation curves. The Journal of Physiology 40 iv-vii.
16. Hoheisel, J. (2006). Microarray technology: Beyond transcript profiling and genotype analysis. Nature Review Genetics 7 200-210.
17. Kahru, A. and Dubourguier, H. (2009). From ecotoxicology to nanoecotoxicolgy. Toxicology 269 105-119.
18. Kong, M. and Eubank, R. L. (2006). Monotone smoothing with application to dose-response curve. Comm. Statist. Simulation Comput. 35 991-1004. MR2291377
19. Li, C.-S. and Hunt, D. (2004). Regression splines for threshold selection with application to a random-effects logistic dose-response model. Comput. Statist. Data Anal. 46 1-9. MR2056821
20. Maynard, A., Aitken, R., Butz, T., Colvin, V., Donaldson, K., Oberdörster, G., Philbert, M., Ryan, J., Seaton, A., Stone, V., Tinkle, S., Tran, L., Walker, N. and Warheit, D. (2006). Safe handling of nanotechnology. Nature Biotechnology 444 267-268.
21. Meng, H., Liong, M., Xia, T., Li, Z., Ji, Z. Zink, J. and Nel, A. E. (2010). Engineered design of mesoporous silica nanoparticles to deliver doxorubicin and p-glycoprotein sirna to overcome drug resistance in a cancer cell line. ACS Nano 4 4539-4550.
22. Nel, A., Xia, T., Mädler, L. and Li, N. (2006). Toxic potential of materials at the nanolevel. Science 311 622-627.
23. Nel, A., Mädler, L., Velegol, D., Xia, T., Hoek, E., Somasundaran, P., Klaessig, F., Castranova, V. and Thompson, M. (2009). Understanding biophysicochemical interactions at the nano-bio interface. Nature Materials 8 543-557.
24. Patel, T., Telesca, D., George, S. and Nel, A. (2012). Supplement to "Toxicity profiling of engineered nanomaterials via multivariate dose-response surface modeling. " DOI:10.1214/12-AOAS563SUPP.
25. Plummerm, M., Best, N., Cowles, K. and Vines, K. (2006). CODA: Convergence diagnosis and output analysis for MCMC. R News 6 7-11.
26. Ramsay, J. (1988). Monotone regression splines in action. Statist. Sci. 3 425-461.
27. Regan, M.M. and Catalano, P. J. (1999). Bivariate dose-response modeling and risk estimation in developmental toxicology. J. Agric. Biol. Environ. Stat. 4 217-237. MR1812242
28. Ritz, C. (2010). Toward a unified approach to dose-response modeling in ecotoxicology. Environ. Toxicol. Chem. 29 220-229.
29. Roberts, G. O. and Rosenthal, J. S. (2001). Optimal scaling for various Metropolis-Hastings algorithms. Statist. Sci. 16 351-367. MR1888450
30. Scott, J. G. and Berger, J. O. (2006). An exploration of aspects of Bayesian multiple testing. J. Statist. Plann. Inference 136 2144-2162. MR2235051
31. Severini, T. A. and Staniswalis, J. G. (1994). Quasi-likelihood estimation in semiparametric models. J. Amer. Statist. Assoc. 89 501-511. MR1294076
32. Stanley, S., Westly, E., Pittet, M., Subramanian, A., Schreiber, S. and Weissleder, R. (2008). Pertubational profiling of nanomaterial biologic activity. Proc. Natl. Acad. Sci. USA 105 7387-7392.
33. Stern, S. and McNeil, S. (2008). Nanotechnology safely concerns revisited. Toxicological Sciences 101 4-21.
34. Tierney, L. (1994). Markov chains for exploring posterior distributions. Ann. Statist. 22 1701-1762. MR1329166
35. West, M. (1984). Outlier models and prior distributions in Bayesian linear regression. J. Roy. Statist. Soc. Ser. B 46 431-439. MR0790630
36. White, R. E. (2000). High-throughput screening in drug metabolism and pharmacokinetic support of drug discovery. Annu. Rev. Pharmacol. Toxicol. 40 133-157.
37. Xia, T., Kovochich, M., Brant, J., Hotze, M., Sempf, J., Oberley, T., Sioutas, C., Yeh, J., Wiesner, M. and AE, N. (2006). Comparison of the abilities of ambient and manufactured nanoparticles to induce cellular toxicity according to an oxidative stress paradigm. Nano Letters 6 1794-1807.
38. Yu, Z.-F. and Catalano, P. J. (2005). Quantitative risk assessment for multivariate continuous outcomes with application to neurotoxicology: The bivariate case. Biometrics 61 757-766. MR2196164