A refined QSAR model for prediction of chemical asthma hazard

Computer-Aided Discovery and Redesign for Respiratory Sensitization: A Tiered Mechanistic Model to Deliver Robust Performance Across a Diverse Chemical Space

Asthma is among the most common occupational diseases with considerable public health and economic costs. Chemicals that induce hypersensitivity in the airways can cause respiratory distress and comorbidities with respiratory infections such as COVID. Robust predictive models for this end point are still elusive due to the lack of an experimental benchmark and the over-reliance of existing in silico tools on structural alerts and structural (vs chemical) similarities. The Computer-Aided Discovery and REdesign (CADRE) platform is a proven strategy for providing robust computational predictions for hazard end points using a tiered hybrid system of expert rules, molecular simulations, and quantum mechanics calculations. The recently developed CADRE model for respiratory sensitization is based on a highly curated data set of structurally diverse chemicals with high-fidelity biological data. The model evaluates absorption kinetics in lung mucosa using Monte Carlo simulations, assigns reactive centers in a molecule and possible biotransformations via expert rules, and determines subsequent reactivity with cell proteins via quantum-mechanics calculations using a multi-tiered regression. The model affords an accuracy above 0.90, with a series of external validations based on literature data in the range of 0.88-0.95. The model is applicable to all low-molecular-weight organics and can inform not only chemical substitution but also chemical redesign to advance development of safer alternatives.

An Explainable Supervised Machine Learning Model for Predicting Respiratory Toxicity of Chemicals Using Optimal Molecular Descriptors

Respiratory toxicity is a serious public health concern caused by the adverse effects of drugs or chemicals, so the pharmaceutical and chemical industries demand reliable and precise computational tools to assess the respiratory toxicity of compounds. The purpose of this study is to develop quantitative structure-activity relationship models for a large dataset of chemical compounds associated with respiratory system toxicity. First, several feature selection techniques are explored to find the optimal subset of molecular descriptors for efficient modeling. Then, eight different machine learning algorithms are utilized to construct respiratory toxicity prediction models. The support vector machine classifier outperforms all other optimized models in 10-fold cross-validation. Additionally, it outperforms the prior study by 2% in prediction accuracy and 4% in MCC. The best SVM model achieves a prediction accuracy of 86.2% and a MCC of 0.722 on the test set. The proposed SVM model predictions are explained using the SHapley Additive exPlanations approach, which prioritizes the relevance of key modeling descriptors influencing the prediction of respiratory toxicity. Thus, our proposed model would be incredibly beneficial in the early stages of drug development for predicting and understanding potential respiratory toxic compounds.

Identifying a reference list of respiratory sensitizers for the evaluation of novel approaches to study respiratory sensitization

The induction of immunological responses that trigger bio-physiological symptoms in the respiratory tract following repeated exposure to a substance, is known as respiratory sensitization. The inducing compound is known as a respiratory sensitizer. While respiratory sensitization by high molecular weight (HMW) materials is recognized and extensively studied, much less information is available regarding low molecular weight (LMW) materials as respiratory sensitizers. Variability of symptoms presented in humans from such exposures, limited availability of (and access to) documented reports, and the absence of standardized and validated test models, hinders the identification of true respiratory sensitizers. This review aims to sort suspected LMW respiratory sensitizers based on available compelling, reasonable, inadequate, or questionable evidence in humans from occupational exposures and use this information to compose a reference list of reported chemical respiratory sensitizers for scientific research purposes. A list of 97 reported respiratory sensitizers was generated from six sources, and 52 LMW organic chemicals were identified, reviewed, and assigned to the four evidence categories. Less than 10 chemicals were confirmed with compelling evidence for induction of respiratory sensitization in humans from occupational exposures. Here, we propose the reference list for developing novel research on respiratory sensitization.

In silico approaches in organ toxicity hazard assessment: Current status and future needs for predicting heart, kidney and lung toxicities

The kidneys, heart and lungs are vital organ systems evaluated as part of acute or chronic toxicity assessments. New methodologies are being developed to predict these adverse effects based on in vitro and in silico approaches. This paper reviews the current state of the art in predicting these organ toxicities. It outlines the biological basis, processes and endpoints for kidney toxicity, pulmonary toxicity, respiratory irritation and sensitization as well as functional and structural cardiac toxicities. The review also covers current experimental approaches, including off-target panels from secondary pharmacology batteries. Current in silico approaches for prediction of these effects and mechanisms are described as well as obstacles to the use of in silico methods. Ultimately, a commonly accepted protocol for performing such assessment would be a valuable resource to expand the use of such approaches across different regulatory and industrial applications. However, a number of factors impede their widespread deployment including a lack of a comprehensive mechanistic understanding, limited in vitro testing approaches and limited in vivo databases suitable for modeling, a limited understanding of how to incorporate absorption, distribution, metabolism, and excretion (ADME) considerations into the overall process, a lack of in silico models designed to predict a safe dose and an accepted framework for organizing the key characteristics of these organ toxicants.

Sentinel approach to detect emerging causes of work-related respiratory diseases

BACKGROUND

The world of work is continually changing, and this could result in new and emerging risks being introduced, including those that may cause work-related respiratory diseases (WRRD).

AIMS

To describe recently emerging and new cases of WRRD and the relevant methodology using data in a national occupational respiratory disease surveillance scheme in the UK.

METHODS

Incident cases of respiratory diseases reported by physicians to the Surveillance of Work-related and Occupational Respiratory Disease (SWORD) between January 2015 and December 2017 were included. Potentially emerging respiratory hazards manifesting in SWORD data were identified with the following hierarchical approach: (i) new respiratory disease not previously associated with work, (ii) specific exposure/agent not previously associated with WRRD and (iii) industry and/or occupation not previously associated with WRRD.

RESULTS

A total of 1215 cases of WRRD were reported to SWORD between January 2015 and December 2017. No new WRRD were identified. Thirteen potentially emerging causes of occupational asthma were identified, including exposures to agents such as ninhydrin. Four potential new causes were identified for interstitial pneumonia, which included wood and brass dust. Two potentially emerging causes of WRRD were identified for inhalational accidents (green coffee and nitrocellulose), hypersensitivity pneumonitis (diphenylmethane diisocyanate and salami mould), rhinitis (morphine and Amaranthus quitensis) and sarcoidosis (prions and horses).

CONCLUSIONS

Continuous monitoring and reporting of any new work-related disease is a critical function of any occupational disease reporting scheme. Potential emerging causes of work-related health risks have been identified by using a simple and systematic way of detecting emerging causes of WRRDs.

Impact of Identification of Clinical Phenotypes in Occupational Asthma

Phenotypic differences and similarities in the spectrum of occupational asthma (OA) subtypes reflect the underlying mechanisms of the diverse forms of the disease, and these phenotypes provide information as to diagnostic steps and approaches to management. In large part, the phenotype reflects the existence of immunologic mechanisms and the presence or absence of a specific IgE-antibody response to a work sensitizer. However, further differences occur between OA from high- and low-molecular-weight sensitizers (chemical sensitizers), which potentially might be relevant for nonoccupational asthma. Chemical sensitizers cause a specific response that is more likely to be a late asthmatic response and specific IgE can be identified only in a minority. Irritant-induced asthma is most easily recognized when it occurs with 1 or more high-level respiratory irritant exposure(s) but is also possible with chronic low-level exposures as in cleaners, farmers, and woodworkers, as suggested from epidemiologic studies. OA chronic obstructive pulmonary disease overlap is more common in older patients and with OA from low-molecular-weight sensitizers. Removal from exposure to the causative agent is currently advised for those with OA from sensitization: further studies with omalizumab and other biologic agents are needed to determine whether these might allow return to the same exposure.

Modeling and insights into molecular basis of low molecular weight respiratory sensitizers

Respiratory sensitization has been considered an important toxicological endpoint, because of the severe risk to human health. A great part of sensitization events were caused by low molecular weight (< 1000) respiratory sensitizers in the past decades. However, there is currently no widely accepted test method that can identify prospective low molecular weight respiratory sensitisers. Herein, we performed the study of modeling and insights into molecular basis of low molecular weight respiratory sensitizers with a high-quality data set containing 136 respiratory sensitizers and 518 nonsensitizers. We built a number of classification models by using OCHEM tools, and a consensus model was developed based on the ten best individual models. The consensus model showed good predictive ability with a balanced accuracy of 0.78 and 0.85 on fivefold cross-validation and external validation, respectively. The readers can predict the respiratory sensitization of organic compounds via https://ochem.eu/article/114857 . The effect of several molecular properties on respiratory sensitization was also evaluated. The results indicated that these properties differ significantly between respiratory sensitizers and nonsensitizers. Furthermore, 14 privileged substructures responsible for respiratory sensitization were identified. We hope the models and the findings could provide useful help for environmental risk assessment.

Phenotyping Occupational Asthma Caused by Acrylates in a Multicenter Cohort Study

BACKGROUND

While acrylates are well-known skin sensitizers, they are not classified as respiratory sensitizers although several cases of acrylate-induced occupational asthma (OA) have been reported.

OBJECTIVE

To evaluate the characteristics of acrylate-induced OA in a large series of cases and compare those with OA induced by other low-molecular-weight (LMW) agents.

METHODS

Jobs and exposures, clinical and functional characteristics, and markers of airway inflammation were analyzed in an international, multicenter, retrospective cohort of subjects with OA ascertained by a positive inhalation challenge to acrylates (n = 55) or other LMW agents (n = 418) including isocyanates (n = 125).

RESULTS

Acrylate-containing glues were the most prevalent products, and industrial manufacturing, dental work, and beauty care were typical occupations causing OA. Work-related rhinitis was more common in acrylate-than in isocyanate-induced asthma (P < .001). The increase in postchallenge fractional exhaled nitric oxide was significantly greater in acrylate-induced OA (26.0; 8.2 to 38.0 parts per billion [ppb]) than in OA induced by other LMW agents (3.0; -1.0 to 10.0 ppb; P < .001) or isocyanates (5.0; 2.0 to 16.0 ppb; P = .010). Multivariable models confirmed that OA induced by acrylates was significantly and independently associated with a postchallenge increase in fractional exhaled nitric oxide (≥17.5 ppb).

CONCLUSIONS

Acrylate-induced OA shows specific characteristics, concomitant work-related rhinitis, and exposure-related increases in fractional exhaled nitric oxide, suggesting that acrylates may induce asthma through different immunologic mechanisms compared with mechanisms through which other LMW agents may induce asthma. Our findings reinforce the need for a reevaluation of the hazard classification of acrylates, and further investigation of the pathophysiological mechanisms underlying their respiratory sensitizing potential.

Occupational and work-related respiratory disease attributed to cleaning products

OBJECTIVES

Exposure to cleaning products has been associated with adverse respiratory outcomes. This study aimed to investigate the medically reported incidence, trends in incidence and occupational determinants of work-related respiratory disorders attributed to cleaning agents and to explore the role of 'Quantitative Structure Activity Relationships' (QSAR) in corroborating the identification of chemical respiratory sensitisers.

METHODS

Respiratory diagnoses attributed to cleaning agents were extracted from The Health and Occupation Research (THOR) surveillance network, 1989-2017. Incidence, trends in incidence and incidence rate ratios by occupation were investigated. Agents were classified by chemical type and QSAR hazard indices were determined for specific organic chemicals.

RESULTS

Approximately 6% (779 cases) of the (non-asbestos) THOR respiratory cases were attributed to cleaning agents. Diagnoses were predominantly asthma (58%) and inhalation accidents (27%) with frequently reported chemical categories being aldehydes (30%) and chlorine/its releasers (26%). No significant trend in asthma incidence (1999-2017) was observed (annual average change of -1.1% (95% CI -4.4 to 2.4)). This contrasted with a statistically significant annual decline in asthma incidence (-6.8% (95% CI -8.0 to -5.6)) for non-cleaning agents. There was a large variation in risk between occupations. 7 of the 15 organic chemicals specifically identified had a QSAR generated hazard index consistent with being a respiratory sensitiser.

CONCLUSION

Specific occupations appear to be at increased risk of adverse respiratory outcomes attributed to cleaning agents. While exposure to agents such as glutaraldehyde have been addressed, other exposures, such as to chlorine, remain important. Chemical features of the cleaning agents helped distinguish between sensitising and irritant agents.

Occupational asthma caused by an epoxy amine hardener

We describe a 43-year-old epoxy floor layer who developed work-related asthma while exposed to an epoxy hardener based on isophorone diamine (IPDA). Challenge exposures to the curing of the epoxy resin system and subsequently to the polyfunctional amine hardener containing IPDA both elicited delayed asthmatic reactions. This report further indicates that exposure to epoxy hardeners containing polyfunctional amines should be considered as a potential cause of occupational asthma. Appropriate work hygiene measures should be implemented to minimize airborne exposure to these volatile compounds.

An Official American Thoracic Society Workshop Report: Presentations and Discussion of the Sixth Jack Pepys Workshop on Asthma in the Workplace

The Sixth Jack Pepys Workshop on Asthma in the Workplace focused on six key themes regarding the recognition and assessment of work-related asthma and airway diseases: (1) cleaning agents and disinfectants (including in swimming pools) as irritants and sensitizers: how to evaluate types of bronchial reactions and reduce risks; (2) population-based studies of occupational obstructive diseases: use of databanks, advantages and pitfalls, what strategies to deal with biases and confounding?; (3) damp environments, dilapidated buildings, recycling processes, and molds, an increasing problem: mechanisms, how to assess causality and diagnosis; (4) diagnosis of occupational asthma and rhinitis: how useful are recombinant allergens (component-resolved diagnosis), metabolomics, and other new tests?; (5) how does exposure to gas, dust, and fumes enhance sensitization and asthma?; and (6) how to determine probability of occupational causality in chronic obstructive pulmonary disease: epidemiological and clinical, confirmation, and compensation aspects. A summary of the presentations and discussion is provided in this proceedings document. Increased knowledge has been gained in each topic over the past few years, but there remain aspects of controversy and uncertainty requiring further research.

In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts

During drug development, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future.

Current and new challenges in occupational lung diseases

Occupational lung diseases are an important public health issue and are avoidable through preventive interventions in the workplace. Up-to-date knowledge about changes in exposure to occupational hazards as a result of technological and industrial developments is essential to the design and implementation of efficient and effective workplace preventive measures. New occupational agents with unknown respiratory health effects are constantly introduced to the market and require periodic health surveillance among exposed workers to detect early signs of adverse respiratory effects. In addition, the ageing workforce, many of whom have pre-existing respiratory conditions, poses new challenges in terms of the diagnosis and management of occupational lung diseases. Primary preventive interventions aimed to reduce exposure levels in the workplace remain pivotal for elimination of the occupational lung disease burden. To achieve this goal there is still a clear need for setting standard occupational exposure limits based on transparent evidence-based methodology, in particular for carcinogens and sensitising agents that expose large working populations to risk. The present overview, focused on the occupational lung disease burden in Europe, proposes directions for all parties involved in the prevention of occupational lung disease, from researchers and occupational and respiratory health professionals to workers and employers.

3-(Bromomethyl)-2-chloro-4-(methylsulfonyl)- benzoic acid: a new cause of sensitiser induced occupational asthma, rhinitis and urticaria

OBJECTIVES

3-(Bromomethyl)-2-chloro-4-(methylsulfonyl)-benzoic acid (BCMBA) has not previously been identified as a respiratory sensitiser. We detected two cases who presented respiratory and urticaria symptoms related to BCMBA and had positive skin prick tests to the agent. Subsequently, we conducted outbreak investigations at the BCMBA-producing factory and performed clinical examinations to confirm occupational diseases.

METHODS

The outbreak investigations included observations of work processes, assessment of exposure, a medical survey with a questionnaire and skin prick tests with 0.5% BCMBA water solution on 85 exposed workers and 9 unexposed workers. We used specific inhalation or nasal challenge and open skin application test to investigate BCMBA-related occupational asthma, rhinitis and contact urticaria.

RESULTS

We identified nine workers with respiratory and/or skin symptoms and positive skin prick tests to BCMBA in a chemical factory. A survey among chemical factory workers indicated a BCMBA-related sensitisation rate of 8% among all exposed workers; the rate was highest (25%) among production workers in the production hall. Sensitisation was detected only in workers with the estimated highest exposure levels. Six cases of occupational asthma, rhinitis and/or contact urticaria caused by BCMBA were confirmed with challenge tests. Asthma-provoking doses in specific inhalation challenges were very low (0.03% or 0.3% BCMBA in lactose).

CONCLUSIONS

We identified a new low molecular weight agent causing occupational asthma, rhinitis and contact urticaria. A typical clinical picture of allergic diseases and positive skin prick tests suggest underlying IgE-mediated disease mechanisms. Stringent exposure control measures are needed in order to prevent BCMBA-related diseases.

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

PURPOSE OF REVIEW

This 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 FINDINGS

Analyses 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.

SUMMARY

Further 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.

Review of Diagnostic Challenges in Occupational Asthma

PURPOSE OF REVIEW

Occupational asthma (OA) is one of the most frequent occupational diseases and its diagnosis is often difficult. This review summarizes its current diagnostic challenges.

RECENT FINDINGS

OA is associated with significant health and socio-economic burden. It is underdiagnosed and physicians need to adopt a stepwise approach to confirm the diagnosis. Although early removal from exposure to the offending agent is associated with a better prognosis, physicians should try to confirm the diagnosis of work-related asthma before taking a worker off work. A proper occupational and medical history is very important but is not enough to make the diagnosis of OA. Objective evidence of work-related asthma is required and this represents a serious challenge to most physicians. Measurement of non-specific bronchial responsiveness (NSBR) and spirometry may confirm the diagnosis of asthma but do not confirm the diagnosis of OA. Serial monitoring of peak expiratory flows (PEF), NSBR, and airway inflammation at and off work may confirm the diagnosis of OA but are often difficult to perform. Confirming sensitization by skin prick tests or specific IgE may help to support the diagnosis of OA. Specific inhalation challenges (SIC) in the lab or at work are considered the reference standard but are of limited access. Medical surveillance programs along with primary prevention (reducing exposure) may help to reduce the burden of OA, but the ideal program has yet to be defined. The diagnostic workup of OA remains a challenge and needs a rigorous stepwise evaluation.

ADMET Evaluation in Drug Discovery. Part 17: Development of Quantitative and Qualitative Prediction Models for Chemical-Induced Respiratory Toxicity

As a dangerous end point, respiratory toxicity can cause serious adverse health effects and even death. Meanwhile, it is a common and traditional issue in occupational and environmental protection. Pharmaceutical and chemical industries have a strong urge to develop precise and convenient computational tools to evaluate the respiratory toxicity of compounds as early as possible. Most of the reported theoretical models were developed based on the respiratory toxicity data sets with one single symptom, such as respiratory sensitization, and therefore these models may not afford reliable predictions for toxic compounds with other respiratory symptoms, such as pneumonia or rhinitis. Here, based on a diverse data set of mouse intraperitoneal respiratory toxicity characterized by multiple symptoms, a number of quantitative and qualitative predictions models with high reliability were developed by machine learning approaches. First, a four-tier dimension reduction strategy was employed to find an optimal set of 20 molecular descriptors for model building. Then, six machine learning approaches were used to develop the prediction models, including relevance vector machine (RVM), support vector machine (SVM), regularized random forest (RRF), extreme gradient boosting (XGBoost), naïve Bayes (NB), and linear discriminant analysis (LDA). Among all of the models, the SVM regression model shows the most accurate quantitative predictions for the test set (q²_ext = 0.707), and the XGBoost classification model achieves the most accurate qualitative predictions for the test set (MCC of 0.644, AUC of 0.893, and global accuracy of 82.62%). The application domains were analyzed, and all of the tested compounds fall within the application domain coverage. We also examined the structural features of the compounds and important fragments with large prediction errors. In conclusion, the SVM regression model and the XGBoost classification model can be employed as accurate prediction tools for respiratory toxicity.

Occupational lung diseases: from old and novel exposures to effective preventive strategies

Occupational exposure is an important, global cause of respiratory disease. Unlike many other non-communicable lung diseases, the proximal causes of many occupational lung diseases are well understood and they should be amenable to control with use of established and effective approaches. Therefore, the risks arising from exposure to silica and asbestos are well known, as are the means of their prevention. Although the incidence of occupational lung disease has decreased in many countries, in parts of the world undergoing rapid economic transition and population growth-often with large informal and unregulated workforces-occupational exposures continue to impose a heavy burden of disease. The incidence of interstitial and malignant lung diseases remains unacceptably high because control measures are not implemented or exposures arise in novel ways. With the advent of innovative technologies, new threats are continually introduced to the workplace (eg, indium compounds and vicinal diketones). In developed countries, work-related asthma is the commonest occupational lung disease of short latency. Although generic control measures to reduce the risk of developing or exacerbating asthma are well recognised, there is still uncertainty, for example, with regards to the management of workers who develop asthma but remain in the same job. In this Review, we provide recommendations for research, surveillance, and other action for reducing the burden of occupational lung diseases.