title: Temporal concordance between apical and transcriptional points of departure for chemical risk assessment.
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.
authors.affiliation: Hamner Institutes for Health Sciences, Research Triangle Park, NC, USA.
authors.forename: Russell S
authors.initials: RS
authors.lastname: Thomas
authors.forename: Scott C
authors.initials: SC
authors.lastname: Wesselkamper
authors.forename: Nina Ching Y
authors.initials: NC
authors.lastname: Wang
authors.forename: Q Jay
authors.initials: QJ
authors.lastname: Zhao
authors.forename: Dan D
authors.initials: DD
authors.lastname: Petersen
authors.forename: Jason C
authors.initials: JC
authors.lastname: Lambert
authors.forename: Ila
authors.initials: I
authors.lastname: Cote
authors.forename: Longlong
authors.initials: L
authors.lastname: Yang
authors.forename: Eric
authors.initials: E
authors.lastname: Healy
authors.forename: Michael B
authors.initials: MB
authors.lastname: Black
authors.forename: Harvey J
authors.initials: HJ
authors.lastname: Clewell
authors.forename: Bruce C
authors.initials: BC
authors.lastname: Allen
authors.forename: Melvin E
authors.initials: ME
authors.lastname: Andersen
mesh_terms: D000818:Animals; D015197:Carcinogenicity Tests / Q000379:methods*; D002273:Carcinogens / Q000737:chemistry / Q000633:toxicity*; D004305:Dose-Response Relationship, Drug; D023381:Endpoint Determination; D005260:Female; D008297:Male; D009374:Neoplasms, Experimental / Q000139:chemically induced / Q000235:genetics / Q000378:metabolism; D009928:Organ Specificity; D051381:Rats; D011916:Rats, Inbred F344; D017207:Rats, Sprague-Dawley; D018570:Risk Assessment / Q000379:methods*; D015398:Signal Transduction / Q000187:drug effects; D059467:Transcriptome / Q000187:drug effects
chemical_list: D002273:Carcinogens
keywords: bioinformatics; dose response; microarray; risk assessment; safety evaluation; toxicogenomics