Evaluation of Biologically Based Dose–Response Modeling for Developmental Toxicity: A Workshop Report

2000 ◽  
Vol 31 (2) ◽  
pp. 190-199 ◽  
Author(s):  
Christopher Lau ◽  
Melvin E. Andersen ◽  
Douglas J. Crawford-Brown ◽  
Robert J. Kavlock ◽  
Carole A. Kimmel ◽  
...  
Keyword(s):  
Dose Response ◽  
Developmental Toxicity ◽  
Workshop Report ◽  
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 Modeling with Summary Data from Developmental Toxicity Studies

Risk Analysis ◽  
2016 ◽  
Vol 37 (5) ◽  
pp. 905-917 ◽  
Author(s):  
John F. Fox ◽  
Karen A. Hogan ◽  
Allen Davis
Keyword(s):  
Dose Response ◽  
Developmental Toxicity ◽  
Toxicity Studies ◽  
Response Modeling ◽  
Summary Data

scholarly journals Predicting the in vivo developmental toxicity of benzo[a]pyrene (BaP) in rats by an in vitro–in silico approach

2021 ◽  
Author(s):  
Danlei Wang ◽  
Maartje H. Rietdijk ◽  
Lenny Kamelia ◽  
Peter J. Boogaard ◽  
Ivonne M. C. M. Rietjens
Keyword(s):  
Dose Response ◽  
In Silico ◽  
Response Curve ◽  
Developmental Toxicity ◽  
Toxicity Testing ◽  
Maximal Effect ◽  
Blood Concentrations ◽  
Reverse Dosimetry

AbstractDevelopmental toxicity testing is an animal-intensive endpoints in toxicity testing and calls for animal-free alternatives. Previous studies showed the applicability of an in vitro–in silico approach for predicting developmental toxicity of a range of compounds, based on data from the mouse embryonic stem cell test (EST) combined with physiologically based kinetic (PBK) modelling facilitated reverse dosimetry. In the current study, the use of this approach for predicting developmental toxicity of polycyclic aromatic hydrocarbons (PAHs) was evaluated, using benzo[a]pyrene (BaP) as a model compound. A rat PBK model of BaP was developed to simulate the kinetics of its main metabolite 3-hydroxybenzo[a]pyrene (3-OHBaP), shown previously to be responsible for the developmental toxicity of BaP. Comparison to in vivo kinetic data showed that the model adequately predicted BaP and 3-OHBaP blood concentrations in the rat. Using this PBK model and reverse dosimetry, a concentration–response curve for 3-OHBaP obtained in the EST was translated into an in vivo dose–response curve for developmental toxicity of BaP in rats upon single or repeated dose exposure. The predicted half maximal effect doses (ED50) amounted to 67 and 45 mg/kg bw being comparable to the ED50 derived from the in vivo dose–response data reported for BaP in the literature, of 29 mg/kg bw. The present study provides a proof of principle of applying this in vitro–in silico approach for evaluating developmental toxicity of BaP and may provide a promising strategy for predicting the developmental toxicity of related PAHs, without the need for extensive animal testing.

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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