Martin Seed

Relationship between chemical structure and the occupational asthma hazard of low molecular weight organic compounds

Aims: 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.

Methods for the prediction of low-molecular-weight occupational respiratory sensitizers.

Purpose of reviewThere is recognition that respiratory sensitization is an occupational hazard of high concern. Despite international regulatory requirements there is no established protocol for the efficient prospective identification of chemical respiratory sensitizers. We review the predictive behaviour of available methods and suggest a possible high-throughput protocol. Recent findingsAnimal or in-vitro tests specific to respiratory exposure and resulting in direct asthma-related outcomes have not been developed, although the use of a local lymph node assay originally designed for skin sensitization has been advocated in a respiratory context. Various methods have been used to develop quantitative structure–activity relationship models for prediction of low-molecular-weight organic chemical respiratory sensitizers. The estimated negative predictive value for all of the published models is 1, but their differences in positive predictive value can be exploited. SummaryThe most pragmatic as well as valid approach for screening large numbers of industrial chemicals for respiratory sensitization hazard is likely to consist of an algorithm starting with quantitative structure–activity relationship models. Further corroboration from animal or human data, however, may be required for chemicals with a positive result by quantitative structure–activity relationship.

Development of mechanism-based structural alerts for respiratory sensitization hazard identification.

This study outlines how mechanistic organic chemistry related to covalent bond formation can be used to rationalize the ability of low molecular weight chemicals to cause respiratory sensitization. The results of an analysis of 104 chemicals which have been reported to cause respiratory sensitization in humans showed that most of the sensitizing chemicals could be distinguished from 82 control chemicals for which no clinical reports of respiratory sensitization exist. This study resulted in the development of a set of mechanism-based structural alerts for chemicals with the potential to cause respiratory sensitization. Their potential for use in a predictive algorithm for this purpose alongside an externally validated quantitative structure-activity relationship model is discussed.

A refined QSAR model for prediction of chemical asthma hazard

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) <1 kDa 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.

Respiratory sensitization: advances in assessing the risk of respiratory inflammation and irritation

Respiratory sensitization provides a case study for a new approach to chemical safety evaluation, as the prevalence of respiratory sensitization has increased considerably over the last decades, but animal and/or human experimental/predictive models are not currently available. Therefore, the goal of a working group was to design a road map to develop an ASAT approach for respiratory sensitisers. This approach should aim at (i) creating a database on respiratory functional biology and toxicology, (ii) applying data analyses to understand the multi-dimensional sensitization response, and how this predisposes to respiratory inflammation and irritation, and (iii) building a systems model out of these analyses, adding pharmacokinetic-pharmacodynamic modeling to predict respiratory responses to low levels of sensitisers. To this end, the best way forward would be to follow an integrated testing approach. Experimental research should be targeted to (i) QSAR-type approaches to relate potential as a respiratory sensitizer to its chemical structure, (ii) in vitro models and (iii) in vitro-in vivo extrapolation/validation.

Do all occupational respiratory sensitisers follow the united airways disease model

The cost-effectiveness of prostanoids in pulmonary arterial hypertension (PAH) has recently been called into question by the National Institute for Health and Clinical Excellence (NICE),1 and the possibility exists that this treatment would not be recommended by this body. This would be the first time that a treatment already in routine clinical practice would be withdrawn as a result of NICE recommendations. Guidelines published by the UK, European and US authorities still advocate prostanoid use in certain patient groups.2–4 Of the disease-targeted therapy available for PAH, only epoprostenol has been shown to improve patient survival in the context of a randomised controlled trial.5 To assess the impact that withdrawal of intravenous epoprostenol in 1997 would …

Progress with Structure–Activity Relationship modelling of occupational chemical respiratory sensitizers

Purpose of reviewThis appraises currently available computer-based (‘in silico’) models relating the molecular structure of low molecular weight compounds to their respiratory sensitization hazard. The present review places focus on the two main applications of such structure--activity relationship (SAR) models: hypotheses on disease mechanisms and toxicological prediction. Recent findingsAnalyses of the chemical structures of low molecular weight organic compounds known to have caused occupational asthma has led to the development of mechanistic alerts usually based on electrophilic reaction chemistry and protein cross-linking potential. Protein cross-linking potential has also been found to be a consistent feature of chemicals that have caused human cases of hypersensitivity pneumonitis. Stepwise iteration of quantitative SAR (QSAR) modelling has shown appreciable improvements in predictivity for occupational asthma hazard and useful prospects for practical application. A good case has also been made for the potential use of structural alert-based mechanistic SARs in predictive toxicology. SummaryFurther understanding of the molecular interactions between chemical respiratory sensitizers and components of human proteins have been obtained from in-vitro and in-silico techniques. There have been developments in both qualitative (mechanistic) SARs and QSARs, which offer potential for use in a predictive algorithm for the toxicological screening of industrial chemicals for respiratory sensitization potential.

Prediction of asthma hazard of thiamine

The cases of occupational asthma caused by thiamine described by Drought et al. (1) provide an example of yet another novel low molecular weight organic asthmagen. For this group of compounds, a quantitative structure-activity relationship model (QSAR) has been developed for prediction of asthma hazard by Jarvis et al. (2). On submitting a chemical structure, the model calculates an asthma hazard index between zero and one indicating the likelihood that the chemical structure has asthmagenic potential. It is freely available on the internet through the following website: http://www.coeh.man.ac.uk/research/asthma (Accessed 1 December 2005). In the cases reported by Drought et al., confirmation of thiamine as the asthmagenic chemical has been obtained through bronchial challenge testing. It also scores a hazard index of 0.95 on this asthma hazard QSAR indicating that it almost certainly has the chemical structural requirements for asthmagenicity. Validation has shown that the QSAR correctly identifies asthmagens with a sensitivity of 86% and specificity 99%. Thiamine (CAS no.59-43-8)

Occupational asthma from tafenoquine in the pharmaceutical industry: implications for QSAR

We report occupational asthma and rhinitis in a formulation pharmacist, employed in the development of tafenoquine. Tafenoquine is a new anti-malarial drug in development; the pure drug substance has an asthma hazard index of zero and previously was not known to be a respiratory sensitizing agent. The implications of this finding for the refinement of quantitative structural analysis of asthmagenic chemicals are discussed.

A computer based asthma hazard prediction model and new molecular weight agents in occupational asthma

Given the high estimated prevalence and financial burden of occupational asthma (OA),1 ,2 prevention is a major concern. However, in the absence of a regulatory screening protocol, the respiratory sensitising potential of a chemical is usually only apparent when it has caused a human case of OA. The purpose of primary prevention is to detect hazardous agents before the occurrence of the issue, in order to avoid exposure of workers.3 Agius et al 4 have developed and validated a computer based asthma hazard prediction model to predict the potential of low molecular weight (LMW) organic agents to cause asthma …