A Bayesian network model for biomarker-based dose response.
A Bayesian network model was developed to integrate diverse types of data to conduct an exposure-dose-response assessment for benzene-induced acute myeloid leukemia (AML). The network approach was used to evaluate and compare individual biomarkers and quantitatively link the biomarkers along the exposure-disease continuum. The network was used to perform the biomarker-based dose-response analysis, and various other approaches to the dose-response analysis were conducted for comparison. The network-derived benchmark concentration was approximately an order of magnitude lower than that from the usual exposure concentration versus response approach, which suggests that the presence of more information in the low-dose region (where changes in biomarkers are detectable but effects on AML mortality are not) helps inform the description of the AML response at lower exposures. This work provides a quantitative approach for linking changes in biomarkers of effect both to exposure information and to changes in disease response. Such linkage can provide a scientifically valid point of departure that incorporates precursor dose-response information without being dependent on the difficult issue of a definition of adversity for precursors.
Hack
C Eric
CE
Toxicology Excellence for Risk Assessment (TERA), Cincinnati, OH, USA. charles.hack@wpafb.af.mil
Haber
Lynne T
LT
Maier
Andrew
A
Shulte
Paul
P
Fowler
Bruce
B
Lotz
W Gregory
WG
Savage
Russell E
RE
D000393:Air Pollutants / Q000633:toxicity; D001499:Bayes Theorem; D001554:Benzene / Q000008:administration & dosage* / Q000633:toxicity*; D015415:Biomarkers / Q000032:analysis*; D004305:Dose-Response Relationship, Drug; D006801:Humans; D015470:Leukemia, Myeloid, Acute / Q000139:chemically induced; D009010:Monte Carlo Method; D018570:Risk Assessment / Q000706:statistics & numerical data*
D000393:Air Pollutants; D015415:Biomarkers; D001554:Benzene