Assessment of effect levels of chemicals from quantitative structure-activity relationship (QSAR) models. I. Chronic lowest-observed-adverse-effect level (LOAEL).
With the multitude of new chemicals being synthesized and the paucity of long-term test data on chemicals that could be introduced into the environment, innovative approaches must be developed to determine the health and environmental effects of chemicals. Research was conducted to employ quantitative structure-activity relationship (QSAR) techniques to study the feasibility of developing models to estimate the noncarcinogenic toxicity of chemicals that are not addressed in the literature by relevant studies. A database of lowest-observed-adverse effect level (LOAEL) was assembled by extracting toxicity information from 104 U.S. EPA documents, 124 National Cancer Institute/National Toxicology Program (NCI/NTP) reports, and 6 current reports from the literature. A regression model, based on 234 chemicals of diverse structures and chemical classes including both alicyclic and aromatic compounds, was developed to assess the chronic oral LOAELs in rats. The model was incorporated into an automated computer package. Initial testing of this model indicates it has application to a wide range of chemicals. For about 55% of the compounds in the data set, the estimated LOAELs are within a factor of 2 of the observed LOAELs. For over 93%, they are within a factor of 5. Because of the paucity or absence of long-term toxicity data, the public health and risk assessment community could utilize such QSAR models to determine initial estimates of toxicity for the ever-increasing numbers of chemicals that lack complete pertinent data. However, this and other such models should be used only by expert toxicologists who must objectively look at the estimates thus generated in light of the overall weight of evidence of the available toxicologic information of the subject chemical(s).
Mumtaz
M M
MM
U.S. Environmental Protection Agency, Cincinnati, OH, USA.
Knauf
L A
LA
Reisman
D J
DJ
Peirano
W B
WB
DeRosa
C T
CT
Gombar
V K
VK
Enslein
K
K
Carter
J R
JR
Blake
B W
BW
Huque
K I
KI
D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D016208:Databases, Factual; D005240:Feasibility Studies; D015386:Hazardous Substances / Q000633:toxicity*; D015233:Models, Statistical; D051381:Rats; D018570:Risk Assessment; D013329:Structure-Activity Relationship; D013997:Time Factors; D018675:Toxicity Tests
D015386:Hazardous Substances