Temporal Concordance Between Apical and Transcriptional Points of Departure for Chemical Risk Assessment
Male
0301 basic medicine
Dose-Response Relationship, Drug
Carcinogenicity Tests
Endpoint Determination
Neoplasms, Experimental
Risk Assessment
Rats, Inbred F344
Rats
3. Good health
Rats, Sprague-Dawley
03 medical and health sciences
Organ Specificity
Carcinogens
Animals
Female
Transcriptome
Signal Transduction
DOI:
10.1093/toxsci/kft094
Publication Date:
2013-04-18T00:31:54Z
AUTHORS (13)
ABSTRACT
The number of legacy chemicals without toxicity reference values combined with the rate of new chemical development is overwhelming the capacity of the traditional risk assessment paradigm. More efficient approaches are needed to quantitatively estimate chemical risks. In this study, rats were dosed orally with multiple doses of six chemicals for 5 days and 2, 4, and 13 weeks. Target organs were analyzed for traditional histological and organ weight changes and transcriptional changes using microarrays. Histological and organ weight changes in this study and the tumor incidences in the original cancer bioassays were analyzed using benchmark dose (BMD) methods to identify noncancer and cancer points of departure. The dose-response changes in gene expression were also analyzed using BMD methods and the responses grouped based on signaling pathways. A comparison of transcriptional BMD values for the most sensitive pathway with BMD values for the noncancer and cancer apical endpoints showed a high degree of correlation at all time points. When the analysis included data from an earlier study with eight additional chemicals, transcriptional BMD values for the most sensitive pathway were significantly correlated with noncancer (r = 0.827, p = 0.0031) and cancer-related (r = 0.940, p = 0.0002) BMD values at 13 weeks. The average ratio of apical-to-transcriptional BMD values was less than two, suggesting that for the current chemicals, transcriptional perturbation did not occur at significantly lower doses than apical responses. Based on our results, we propose a practical framework for application of transcriptomic data to chemical risk assessment.
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