Development of an in vitro test to identify respiratory sensitizers in bronchial epithelial cells using gene expression profiling

Identification of true chemical respiratory allergens: Current status, limitations and recommendations

Asthma in the workplace is an important occupational health issue. It comprises various subtypes: occupational asthma (OA; both allergic asthma and irritant-induced asthma) and work-exacerbated asthma (WEA). Current regulatory paradigms for the management of OA are not fit for purpose. There is therefore an important unmet need, for the purposes of both effective human health protection and appropriate and proportionate regulation, that sub-types of work-related asthma can be accurately identified and classified, and that chemical respiratory allergens that drive allergic asthma can be differentiated according to potency. In this article presently available strategies for the diagnosis and characterisation of asthma in the workplace are described and critically evaluated. These include human health studies, clinical investigations and experimental approaches (structure-activity relationships, assessments of chemical reactivity, experimental animal studies and in vitro methods). Each of these approaches has limitations with respect to providing a clear discrimination between OA and WEA, and between allergen-induced and irritant-induced asthma. Against this background the needs for improved characterisation of work-related asthma, in the context of more appropriate regulation is discussed.

Beyond dermal exposure: The respiratory tract as a target organ in hazard assessments of cosmetic ingredients

Dermal contact is the main route of exposure for most cosmetics; however, inhalation exposure could be significant for some formulations (e.g., aerosols, powders). Current cosmetic regulations do not require specific tests addressing respiratory irritation and sensitisation, and despite the prohibition of animal testing for cosmetics, no alternative methods have been validated to assess these endpoints to date. Inhalation hazard is mainly determined based on existing human and animal evidence, read-across, and extrapolation of data from different target organs or tissues, such as the skin. However, because of mechanistic differences, effects on the skin cannot predict effects on the respiratory tract, which indicates a substantial need for the development of new approach methodologies addressing respiratory endpoints for inhalable chemicals in general. Cosmetics might present a particularly significant need for risk assessments of inhalation exposure to provide a more accurate toxicological evaluation and ensure consumer safety. This review describes the differences in the mechanisms of irritation and sensitisation between the skin and the respiratory tract, the progress that has already been made, and what still needs to be done to fill the gap in the inhalation risk assessment of cosmetic ingredients.

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.

How to assess respiratory sensitization of low molecular weight chemicals?

There are no validated and regulatory accepted (animal) models to test for respiratory sensitization of low molecular weight (LMW) chemicals. Since several decades such chemicals are classified as respiratory sensitizers almost exclusively based on observations in workers. However, both respiratory allergens (in which process the immune system is involved) as well as asthmagens (no involvement of the immune system) may induce the same type of respiratory symptoms. Correct classification is very important from a health's perspective point of view. On the other hand, over-classification is not preferable in view of high costs to overdue workplace engineering controls or the chemical ultimately being banned due to Authorities' decisions. It would therefore be very beneficial if respiratory sensitizers can be correctly identified and distinguished from skin sensitizers and non-sensitizers/respiratory irritants. The purpose of this paper is to consider whether LMW chemicals can be correctly identified based on the currently available screening methods in workers, and/or via in silico, in vitro and/or in vivo testing. Collectively, based on the available information further effort is still needed to be able to correctly identify respiratory sensitizers and to distinguish these from skin sensitizers and irritants, not at least because of the far-reaching consequences once a chemical is classified as a respiratory sensitizer.

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.

Chemical-induced asthma and the role of clinical, toxicological, exposure and epidemiological research in regulatory and hazard characterization approaches

Uncertainties in understanding all potential modes-of-action for asthma induction and elicitation hinders design of hazard characterization and risk assessment methods that adequately screen and protect against hazardous chemical exposures. To address this challenge and identify current research needs, the University of Cincinnati and the American Cleaning Institute hosted a webinar series to discuss the current state-of-science regarding chemical-induced asthma. The general consensus is that the available database, comprised of data collected from routine clinical and validated toxicological tests, is inadequate for predicting or determining causal relationships between exposures and asthma induction for most allergens. More research is needed to understand the mechanism of asthma induction and elicitation in the context of specific chemical exposures and exposure patterns, and the impact of population variability and patient phenotypes. Validated tools to predict respiratory sensitization and to translate irritancy assays to asthma potency are needed, in addition to diagnostic biomarkers that assess and differentiate allergy versus irritant-based asthmatic responses. Diagnostic methods that encompass the diverse etiologies of asthmatic responses and incorporate robust exposure measurements capable of capturing different temporal patterns of complex chemical mixtures are needed. In the absence of ideal tools, risk assessors apply hazard-based safety assessment methods, in conjunction with active risk management, to limit potential asthma concerns, proactively identify new concerns, and ensure deployment of approaches to mitigate asthma-related risks.

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.