Publication Topic

Wright PSR, Briggs KA, Thomas R, Smith GF, Maglennon G, Mikulskis P, Chapman M, Greene N, Bender A. The impact of pooling animal histopathology control data on the statistical detection of treatment-related findings. Abstract SOC02-05, 56^th Congress of the European Societies of Toxicology (EUROTOX 2021). Toxicol Lett 350(Sup):S63; doi: 10.1016/S0378-4274(21)00395-7, 2021.

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.

Liu S, Refaei M, Liu S, Decker A, Hinerman JM, Herr AB, Howell M, Musier-Forsyth K, Tsang P. 2020. Hairpin RNA-induced conformational change of a eukaryotic-specific lysyl-tRNA synthetase extension and role of adjacent anticodon-binding domain. J Biol Chem 295(34):12071–12085; doi: 10.1074/jbc.RA120.013852. PMID: 32611767.

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.

Patlewicz G. 2020. Navigating the minefield of computational toxicology and informatics: Looking back and charting a new horizon. Front Toxicol 2:2; doi: 10.3389/ftox.2020.00002. PMID: 35296116.

Zorn KM, Foil DH, Lane TR, Hillwalker W, Feifarek DJ, Jones F, Klaren WD, Brinkman AM, Ekins S. 2020. Comparing machine learning models for aromatase (P450 19A1). Environ Sci Technol 54(23):15546–15555; doi: 10.1021/acs.est.0c05771. PMID: 33207874.

Sturm N, Mayr A, Le Van T, Chupakhin V, Ceulemans H, Wegner J, Golib-Dzib J-F,…, Greene N, et al. 2020. Industry-scale application and evaluation of deep learning for drug target prediction. J Cheminform 12(1):26; doi: 10.1186/s13321-020-00428-5. PMID: 33430964.

Bell S, Mansouri K, Phillips J, Chang X, Abedini J, Karmaus AL, Lea I, Rai R, Allen DG, Casey W, Kleinstreuer NC. NCEATM computational tools and resources supporting alternative test method development and evaluation. Abstract #2976-P607, Society of Toxicology 59th Annual Meeting, Anaheim CA, March 2020.

Rogers EN, Mihalchik AL, Gad, SC. Comparison and analysis of discrepancies among commonly used Cramer decision tree methods in Toxtree software.  Abstract no. 1517. Society of Toxicology 59th Annual Meeting, Virtual, 2020.

Helman G, Patlewicz G, Shah I. 2019. Quantitative prediction of repeat dose toxicity values using GenRA. Regul Toxicol Pharmacol 109(Dec):104480; doi: 10.1016/j.yrtph.2019.104480. PMID: 31550520.

Sheridan E, Moose J, Liu Y, Martini C, Lea I, Fostel J. NTP high-level summary data collections in the Chemical Effects in Biological Systems (CEBS) database. Poster PS01-0606. Presented at 15th International Congress of Toxicology, Honolulu, HI, July 2019.

Bhawana B, Lea I, Liu Y, Martini C, Fostel J. Access to National Toxicology Program histopathology lesion collection in Chemical Effects in Biological Systems (CEBS) database. Poster P040. Presented at the 38th Annual Symposium of Society for Toxicologic Pathology, Raleigh, NC, June 2019.

Thomas RS, Bahadori T, Buckley T, Cowden J, Deisenroth C, Dionisio K, Frithsen J,…, Patlewicz G, et al. 2019. The next generation of computational toxicology at the US Environmental Protection Agency. Toxicol Sci 169(2):317-332; doi: 10.1093/toxsci/kfz058. PMID: 30835285.

Martini C, Liu Y, Motti D, Johnson J, McCormick K, Lea I, Fostel F. Integrating assay datasets into Chemical Effects in Biological Systems (CEBS). Abstract #1749-P125, Society of Toxicology 58th Annual Meeting, Baltimore, MD, March 2019.

Kleinstreuer NC, Karmaus A, Mansouri K, Allen D, Fitzpatrick J, Patlewicz G. 2018. Predictive models for acute oral systemic toxicity: A workshop to bridge the gap from research to regulation. Comput Toxicol 8(11):21-24; doi: 10.1016/j.comtox.2018.08.002. PMID: 30320239.

Nelms MD, Mellow CL, Enoch SJ, Judson RS, Patlewicz G, Richard AM, Madden JM, Cronin MTD, Edwards SW. 2018. A mechanistic framework for integrating chemical structure and high-throughput screening results to improve toxicity predictions. Comput Toxicol 8(Nov):1-12; doi: 10.1016/j.comtox.2018.08.003. PMID: 36779220.

Fourches D, Williams AJ, Patlewicz G, Shah I, Grulke C, Wambaugh J, et al. 2018. Computational tools for ADMET profiling. Chapter 8 in: Ekins E (ed), Computational Toxicology: Risk Assessment for Chemicals, pp. 211–244. doi: 10.1002/9781119282594.ch8.

Myatt GJ, Ahlberg E, Akahori Y, Allen D, Amberg A, Anger LT, Aptula A,…, Patlewicz G, et al. 2018. In silico toxicology protocols. Regul Toxicol Pharmacol 96(July):1-17; doi: 10.1016/j.yrtph.2018.04.014. PMID: 29678766.

Patlewicz G, Cronin MTD, Helman G, Lambert JC, Lizarraga LE, Shah I. 2018. Navigating through the minefield of read-across frameworks: A commentary perspective. Comput Toxicol 6(May):39-54; doi: 10.1016/j.comtox.2018.04.002.