Relationship between chemical structure and the occupational asthma hazard of low molecular weight organic compounds.
OBJECTIVE
To investigate quantitatively, relationships between chemical structure and reported occupational asthma hazard for low molecular weight (LMW) organic compounds; to develop and validate a model linking asthma hazard with chemical substructure; and to generate mechanistic hypotheses that might explain the relationships.


METHODS
A learning dataset used 78 LMW chemical asthmagens reported in the literature before 1995, and 301 control compounds with recognised occupational exposures and hazards other than respiratory sensitisation. The chemical structures of the asthmagens and control compounds were characterised by the presence of chemical substructure fragments. Odds ratios were calculated for these fragments to determine which were associated with a likelihood of being reported as an occupational asthmagen. Logistic regression modelling was used to identify the independent contribution of these substructures. A post-1995 set of 21 asthmagens and 77 controls were selected to externally validate the model.


RESULTS
Nitrogen or oxygen containing functional groups such as isocyanate, amine, acid anhydride, and carbonyl were associated with an occupational asthma hazard, particularly when the functional group was present twice or more in the same molecule. A logistic regression model using only statistically significant independent variables for occupational asthma hazard correctly assigned 90% of the model development set. The external validation showed a sensitivity of 86% and specificity of 99%.


CONCLUSIONS
Although a wide variety of chemical structures are associated with occupational asthma, bifunctional reactivity is strongly associated with occupational asthma hazard across a range of chemical substructures. This suggests that chemical cross-linking is an important molecular mechanism leading to the development of occupational asthma. The logistic regression model is freely available on the internet and may offer a useful but inexpensive adjunct to the prediction of occupational asthma hazard.
Jarvis
J
J
University Computing Services, The University of Edinburgh, UK.
Seed
M J
MJ
Elton
R
R
Sawyer
L
L
Agius
R
R
D001249:Asthma / Q000139:chemically induced*; D006801:Humans; D008954:Models, Biological; D008970:Molecular Weight; D009584:Nitrogen; D009784:Occupational Diseases / Q000139:chemically induced*; D016273:Occupational Exposure / Q000009:adverse effects; D016017:Odds Ratio; D009930:Organic Chemicals / Q000737:chemistry / Q000633:toxicity*; D010100:Oxygen; D012044:Regression Analysis; D018570:Risk Assessment / Q000379:methods; D013329:Structure-Activity Relationship
D009930:Organic Chemicals; D009584:Nitrogen; D010100:Oxygen