Occupational Asthma Reference

Jarvis J, Seed MJ, Stocks SJ, Agius RM, A refined QSAR model for prediction of chemical asthma hazard, Occup Med, 2015;65:659-666,10.1093/occmed/kqv105

Keywords: Asthma Hazard Index. QSAR, UK, key

Known Authors

Raymond Agius, Centre for Occupational and Environmental Health, Manchester University Raymond Agius

Martin Seed, Manchester University Martin Seed

If you would like to become a known author and have your picture displayed along with your papers then please get in touch from the contact page. Known authors can choose to receive emails when their papers receive comments.

Abstract

Background
A previously developed quantitative structure–activity relationship (QSAR) model has been extern ally validated as a good predictor of chemical asthma hazard (sensitivity: 79–86%, specificity: 93–99%).

Aims
To develop and validate a second version of this model.

Methods
Learning dataset asthmagenic chemicals with molecular weight (MW) <1kDa were identified from reports published in the peer-reviewed literature before the end of 2012. Control chemicals for which no reported case(s) of occupational asthma had been identified were selected at random from UK and US occupational exposure limit tables. MW banding was used in an attempt to categorically match the control group for MW distribution of the asthmagens. About 10% of chemicals in each MW category were excluded for use as an external validation set. An independent researcher utilized a logistic regression approach to compare the molecular descriptors present in asthmagens and controls. The resulting equation generated a hazard index (HI), with a value between zero and one, as an estimate of the probability that the chemical had asthmagenic potential. The HI was determined for each compound in the external validation set.

Results
The model development sets comprised 99 chemical asthmagens and 204 controls. The external validation showed that using a cut-point HI of 0.39, 9/10 asthmagenic (sensitivity: 90%) and 23/24 non-asthmagenic (specificity: 96%) compounds were correctly predicted. The new QSAR model showed a better receiver operating characteristic plot than the original.

Conclusions
QSAR refinement by iteration has resulted in an improved model for the prediction of chemical asthma hazard.

Plain text: Background A previously developed quantitative structure-activity relationship (QSAR) model has been extern ally validated as a good predictor of chemical asthma hazard (sensitivity: 79-86%, specificity: 93-99%). Aims To develop and validate a second version of this model. Methods Learning dataset asthmagenic chemicals with molecular weight (MW) <1kDa were identified from reports published in the peer-reviewed literature before the end of 2012. Control chemicals for which no reported case(s) of occupational asthma had been identified were selected at random from UK and US occupational exposure limit tables. MW banding was used in an attempt to categorically match the control group for MW distribution of the asthmagens. About 10% of chemicals in each MW category were excluded for use as an external validation set. An independent researcher utilized a logistic regression approach to compare the molecular descriptors present in asthmagens and controls. The resulting equation generated a hazard index (HI), with a value between zero and one, as an estimate of the probability that the chemical had asthmagenic potential. The HI was determined for each compound in the external validation set. Results The model development sets comprised 99 chemical asthmagens and 204 controls. The external validation showed that using a cut-point HI of 0.39, 9/10 asthmagenic (sensitivity: 90%) and 23/24 non-asthmagenic (specificity: 96%) compounds were correctly predicted. The new QSAR model showed a better receiver operating characteristic plot than the original. Conclusions QSAR refinement by iteration has resulted in an improved model for the prediction of chemical asthma hazard.

Full Text

Full text of this reference not available

Please Log In or Register to add the full text to this reference

Associated Questions

There are no associations for this paper.

Please Log In or Register to put forward this reference as evidence to a question.

Comments

An improvement of the already key method for predicting alergenicity in low molecular weight chemicals. Should be used by all those assessing risk in exposed workers or identifying likely causes of occupational asthma in individuals
11/23/2015

Please sign in or register to add your thoughts.


Oasys and occupational asthma smoke logo