Publication Topic

Moyer H, Valdiviezo A, Sakolish C, Chiu WA, Vergara L, Stephan C, Rusyn I. A comparative analysis of chemical permeability between microphysiological tissue chip models. Texas A&M Superfund External Advisory Board Meeting, College Station, TX, November 2021.

East A, Dawson D, Glen G, Isaacs K, Dionisio K, Price PS, et al. 2021. The Residential Population Generator (RPGen): Parameterization of residential, demographic, and physiological data to model intraindividual exposure, dose, and risk. Toxics 9(11):303; doi: 10.3390/toxics9110303. PMID: 34822694.

Moran KR, Dunson D, Wheeler MW, Herring AH. 2021. Bayesian joint modeling of chemical structure and dose response curves. Ann Appl Stat 15(3):1405; doi: 10.214/21-aoas1461. PMID: 35765365.

Moyer H, Valdiviezo A, Sakolish C, Chiu WA, McDonald, T, Vergara, L, Stephan C, Rusyn I. Analysis of chemical permeability in a mimetas 3-lane microphysiological tissue chip model. Texas A&M University Toxicology Retreat, College Station, TX, August 2021.

East A, Isaacs K, Vallero D. Application of the Residential Population Generator (RPGen) in prediction of exposure outcomes for owners and renters from consumer products using the Combined Human Exposure Model (CHEM). Symposium presented at International Society of Exposure Science Annual Meeting, Virtual, September 2021; doi: 10.23645/epacomptox.16632319.

Yin W, Nie Z, Dingley K, Trzoss M, Krilov G, Marshall N et al. 2021. Characterization of potent paracaspase MALT1 inhibitors for hematological malignancies. Blood 138 (Sup 1):1187; doi: 10.1182/blood-2021-153159.

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.

Mansouri K, Karmaus A, Fitzpatrick J, Patlewicz G, Pradeep P, Alberga D et al. 2021. CATMoS: Collaborative acute toxicity modeling suite. Environ Health Perspect 129(4):47013; doi: 10.1289/EHP8495. PMID: 33929906.

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.

Kandarova H, Raabe H, Hilberer A, Choksi N, Allen D. Retrospective review on in vitro phototoxicity data generated in 3D skin models to support the development of new OECD test guideline. Poster presented at Society of Toxicology 60th Annual Meeting, Virtual, March 2021.

Dake MD, Fanelli F, Lottes AE, O’Leary EE, Reichert H, Jiang X, Fu W, Iida O, et al. 2021. Prediction model for freedom from TLR from a multi-study analysis of long-term results with the Zilver PTX drug-eluting peripheral stent. Cardiovasc Intervent Radiol 44(2):196-206; doi: 10.1007/s00270-020-02648-6. PMID: 33025243.

Marvel SW, House JS, Wheeler MW, Song K, Zhou Y-H, Wright FA, Chiu WA, Rusyn I, et al. 2021. The COVID-19 Pandemic Vulnerability Index (PVI) Dashboard: Monitoring county-level vulnerability using visualization, statistical modeling, and machine learning. Environ Health Perspect 129(1):017701; doi: 10.1289/EHP8690. PMID: 33400596.

Brozek JL, Canelo-Aybar C, Akl EA, Bowen JM, Bucher J, Chiu WA, Cronin M, Djulbegovic B,…, Patlewicz G, et al. 2021. GRADE Guidelines 30: The GRADE approach to assessing the certainty of modeled evidence—An overview in the context of health decision-making. J Clin Epidemiol 129(Jan):138-150; doi: 101016/j.jclinepi.2020.09.018. PMID: 32980429.

Eichenbaum G, Yang K, Gebremichael Y, Howell BA, Murray FJ, Jacobson-Kram D, Jaeschke H, Kuffner E, Gelotte CK, Lai JCK, Wikoff D, Atillasoy E. 2020. Application of the DILIsym® Quantitative Systems Toxicology drug-induced liver injury model to evaluate the carcinogenic hazard potential of acetaminophen. Regul Toxicol Pharmacol 118(Dec):104788; doi: 10.1016/j.yrtph.2020.104788. PMID: 33153971.

Nelms MD, Karmaus AL, Patlewicz G. 2020. An evaluation of the performance of selected (Q)SARs/expert systems for predicting acute oral toxicity. Comput Toxicol 16(Nov):100135; doi: 10.1016/j.comtox.2020.100135. PMID: 33163737.

East A, Price P, Dawson D, Glen G, Dionisio K, Isaacs K, Hubal EC, Vallero D. The Residential Population Generator (RPGen): Parameterization of residential, demographic, and physiological data to model intraindividual exposure, dose, and risk (presentation). Poster presented at Society of Environmental Toxicology and Chemistry North America 41st Annual Meeting, virtual conference, 2020. doi: 10.23645/epacomptox.13476864.

Zorn KM, Foil DH, Russo DP, Clark AM, Hillwalker W, Feifarek D, Jones F, Klaren W, Brinkman A, Ekins S. Generation of machine-learning models to anticipate endocrine disruption. Society of Toxicology 59th Annual Meeting, Virtual, 2020.

Zorn KM, Foil DH, Lane TR, Russo DP, Hillwalker W, Feifarek DJ, Jones F, Klaren WD, Brinkman AM, Ekins S. 2020. Machine learning models for estrogen receptor bioactivity and endocrine disruption prediction. Environ Sci Technol 54(19):12202–12213; doi: 10.1021/acs.est.0c03982. PMID: 32857505.

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.

Rogers EN, Mihalchik AL, Gad, SC. Comparison and analysis of discrepancies among commonly used Cramer decision tree methods in Toxtree software. Abstract 1517, Society of Toxicology 59th Annual Meeting, Virtual, 2020.