Publication Topic

Covington TR, Borghoff SJ, Bacigalupi L, O’Neal S, Cook B, Nelms M, et al. Prediction of chemical suitability for screening in a high-throughput assay platform using an in silico mass balance model. American Society of Cellular and Computational Toxicology Annual Meeting, Research Triangle Park, NC, October 2024.

Perry CS, Verwiel AH, Covington TR, Proctor DM. 2024. PBPK modeling demonstrates that exposure time adjustment is unnecessary for setting an acute manganese inhalation exposure guideline. Regul Toxicol Pharmacol 153(Nov):105698; doi: 10.1016/j.yrtph.2024.105698. PMID: 39251127.

Racz L, Mittal L, Perry CS, Blanchette A, Proctor D. Assessing sustainable applications of electric arc furnace steel slag as construction aggregate: Applications of probabilistic risk assessment and physiologically-based pharmacokinetic modeling. Poster presented at Society of Environmental Toxicology and Chemistry North America 44^th Annual Meeting, Louisville, KY, November 2023.

Perry CS, Blanchette AD, Vivanco SN, Verwiel AH, Proctor DM. 2023. Use of physiologically based pharmacokinetic modeling to support development of an acute (24-hour) health-based inhalation guideline for manganese. Regul Toxicol Pharmacol 145:105518; doi: 10.1016/j.yrtph.2023.105518. PMID: 3786341.

Thompson CM, Kirman C, Harris MA. 2023. Derivation of oral cancer slope factors for hexavalent chromium informed by pharmacokinetic models and in vivo genotoxicity data. Regul Toxicol Pharmacol 145(Dec):105521; doi: 10.1016/j.yrtph.2023.105521. PMID: 37863416.

Ring C, Sipes NS, Hsieh JH, Carberry C, Koval LE, Klaren WD, Harris MA, Auerbach SS, Rager JE. 2021. Predictive modeling of biological responses in the rat liver using in vitro Tox21 bioactivity: Benefits from high throughput toxicokinetics. Comput Toxicol 18(May):100166; doi: 10.1016/j.comtox.2021.100166. PMID: 34013136.

Dirven H, Vist GE, Bandhakavi S, Mehta J, Fitch SE, Pound P, Ram R, Kincaid B, Leenaars CHC, Chen M, Wright RA, Tsaioun K. 2021. Performance of preclinical models in predicting drug-induced liver injury in humans: A systematic review. Sci Reports 11(1)6403; doi: https://doi.org/10.1038/s41598-021-85708-2. PMID: 33737635.

Zorn KM, Foil DH, Lane TR, Hillwalker W, Feifarek DJ, Jones F, Klaren WD, Brinkman AM, Ekins S. 2020. Comparison of machine learning models for the androgen receptor. Environ Sci Technol 54(21):13690–13700; doi: 10.1021/acs.est.0c03984. PMID: 33085465.

Thompson CM, Suh M, Hixon G, Bichteler A. 2015. Comparison of smoothing spline regression and conventional modeling approaches for quantitative risk sssessments of human dioxin exposure. Presented at Society of Toxicology 54th Annual Meeting, San Diego, March 2015.

Hixon JG, Thompson C, Bichteler A, Abraham L. Smoothing regression splines as the basis for dose-response modeling. Presented at Society of Toxicology 53rd Annual Meeting, Phoenix, AZ, March 2014.