Dose-Response Modeling for 2,3,7,8-Tetrachlorodibenzo-p-Dioxin

2005 ◽  
pp. 247-298
Author(s):  
Michael J. DeVito ◽  
Amy Kim ◽  
Nigel J. Walker ◽  
Fred Parham ◽  
Christopher Portier
Keyword(s):  
Dose Response ◽  
Tetrachlorodibenzo P Dioxin ◽  
Response Modeling

scholarly journals Estimates of Cancer Potency of 2,3,7,8-Tetrachlorodibenzo(p)dioxin Using Linear and Nonlinear Dose-Response Modeling and Toxicokinetics

2009 ◽  
Vol 112 (2) ◽  
pp. 490-506 ◽  
Author(s):  
Ted Simon ◽  
Lesa L. Aylward ◽  
Christopher R. Kirman ◽  
J. Craig Rowlands ◽  
Robert A. Budinsky
Keyword(s):  
Dose Response ◽  
Linear And Nonlinear ◽  
Tetrachlorodibenzo P Dioxin ◽  
Response Modeling

The developmental toxicity of inhaled methanol in the CD-1 mouse, with quantitative dose—response modeling for estimation of benchmark doses

Teratology ◽  
1993 ◽  
Vol 47 (3) ◽  
pp. 175-188 ◽  
Author(s):  
John M. Rogers ◽  
M. Leonard Mole ◽  
Neil Chernoff ◽  
Brenda D. Barbee ◽  
Christine I. Turner ◽  
...  
Keyword(s):  
Dose Response ◽  
Developmental Toxicity ◽  
Benchmark Doses ◽  
Response Modeling

scholarly journals Dose-Response Relationship Between Serum 2,3,7,8-Tetrachlorodibenzo-p-Dioxin and Diabetes Mellitus: A Meta-Analysis

2015 ◽  
Vol 181 (6) ◽  
pp. 374-384 ◽  
Author(s):  
Michael Goodman ◽  
K. M. Venkat Narayan ◽  
Dana Flanders ◽  
Ellen T. Chang ◽  
Hans-Olov Adami ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Dose Response ◽  
Meta Analysis ◽  
Response Relationship ◽  
Dose Response Relationship ◽  
Tetrachlorodibenzo P Dioxin

TK/TD dose–response modeling of toxicity

2007 ◽  
Vol 18 (5) ◽  
pp. 515-525
Author(s):  
Munni Begum ◽  
Pranab K. Sen
Keyword(s):  
Dose Response ◽  
Response Modeling

scholarly journals T1000: A reduced toxicogenomics gene set for improved decision making

2019 ◽  
Author(s):  
Othman Soufan ◽  
Jessica Ewald ◽  
Charles Viau ◽  
Doug Crump ◽  
Markus Hecker ◽  
...  
Keyword(s):  
Dose Response ◽  
Gene Clusters ◽  
Gene Set ◽  
Chemical Hazards ◽  
Gene Sets ◽  
Toxicological Research ◽  
Multiple Species ◽  
Response Modeling

There is growing interest within regulatory agencies and toxicological research communities to develop, test, and apply new approaches, such as toxicogenomics, to more efficiently evaluate chemical hazards. Given the complexity of analyzing thousands of genes simultaneously, there is a need to identify reduced gene sets.Though several gene sets have been defined for toxicological applications, few of these were purposefully derived using toxicogenomics data. Here, we developed and applied a systematic approach to identify 1000 genes (called Toxicogenomics-1000 or T1000) highly responsive to chemical exposures. First, a co-expression network of 11,210genes was built by leveraging microarray data from the Open TG-GATEs program. This network was then re-weighted based on prior knowledge of their biological (KEGG, MSigDB) and toxicological (CTD) relevance. Finally, weighted correlation network analysis was applied to identify 258 gene clusters. T1000 was defined by selecting genes from each cluster that were most associated with outcome measures. For model evaluation, we compared the performance of T1000 to that of other gene sets (L1000, S1500, Genes selected by Limma, and random set) using two external datasets. Additionally, a smaller (T384) and a larger version (T1500) of T1000 were used for dose-response modeling to test the effect of gene set size. Our findings demonstrated that the T1000 gene set is predictive of apical outcomes across a range of conditions (e.g.,in vitroand in vivo, dose-response, multiple species, tissues, and chemicals), and generally performs as well, or better than other gene sets available.

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