Development of an in silico profiler for respiratory sensitisation

Chemical respiratory sensitization-Current status of mechanistic understanding, knowledge gaps and possible identification methods of sensitizers

Respiratory sensitization is a complex immunological process eventually leading to hypersensitivity following re-exposure to the chemical. A frequent consequence is occupational asthma, which may occur after long latency periods. Although chemical-induced respiratory hypersensitivity has been known for decades, there are currently no comprehensive and validated approaches available for the prospective identification of chemicals that induce respiratory sensitization, while the expectations of new approach methodologies (NAMs) are high. A great hope is that due to a better understanding of the molecular key events, new methods can be developed now. However, this is a big challenge due to the different chemical classes to which respiratory sensitizers belong, as well as because of the complexity of the response and the late manifestation of symptoms. In this review article, the current information on respiratory sensitization related processes is summarized by introducing it in the available adverse outcome pathway (AOP) concept. Potentially useful models for prediction are discussed. Knowledge gaps and gaps of regulatory concern are identified.

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.

A Review of In Silico Tools as Alternatives to Animal Testing: Principles, Resources and Applications

Across the spectrum of industrial sectors, including pharmaceuticals, chemicals, personal care products, food additives and their associated regulatory agencies, there is a need to develop robust and reliable methods to reduce or replace animal testing. It is generally recognised that no single alternative method will be able to provide a one-to-one replacement for assays based on more complex toxicological endpoints. Hence, information from a combination of techniques is required. A greater understanding of the time and concentration-dependent mechanisms, underlying the interactions between chemicals and biological systems, and the sequence of events that can lead to apical effects, will help to move forward the science of reducing and replacing animal experiments. In silico modelling, in vitro assays, high-throughput screening, organ-on-a-chip technology, omics and mathematical biology, can provide complementary information to develop a complete picture of the potential response of an organism to a chemical stressor. Adverse outcome pathways (AOPs) and systems biology frameworks enable relevant information from diverse sources to be logically integrated. While individual researchers do not need to be experts across all disciplines, it is useful to have a fundamental understanding of what other areas of science have to offer, and how knowledge can be integrated with other disciplines. The purpose of this review is to provide those who are unfamiliar with predictive in silico tools, with a fundamental understanding of the underlying theory. Current applications, software, barriers to acceptance, new developments and the use of integrated approaches are all discussed, with additional resources being signposted for each of the topics.

In Silico Prediction of Organ Level Toxicity: Linking Chemistry to Adverse Effects

In silico methods to predict toxicity include the use of (Quantitative) Structure-Activity Relationships ((Q)SARs) as well as grouping (category formation) allowing for read-across. A challenging area for in silico modelling is the prediction of chronic toxicity and the No Observed (Adverse) Effect Level (NO(A)EL) in particular. A proposed solution to the prediction of chronic toxicity is to consider organ level effects, as opposed to modelling the NO(A)EL itself. This review has focussed on the use of structural alerts to identify potential liver toxicants. In silico profilers, or groups of structural alerts, have been developed based on mechanisms of action and informed by current knowledge of Adverse Outcome Pathways. These profilers are robust and can be coded computationally to allow for prediction. However, they do not cover all mechanisms or modes of liver toxicity and recommendations for the improvement of these approaches are given.

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.

Can the Direct Peptide Reactivity Assay Be Used for the Identification of Respiratory Sensitization Potential of Chemicals?

Prospective identification of low molecular weight respiratory sensitizers is difficult due to the current lack of adequate test methods. The direct peptide reactivity assay (DPRA) seems to be a promising method to determine the sensitization potential of chemicals because it determines the intrinsic characteristic of sensitizers to bind to proteins. It is already applied in the field of skin sensitization, and adaptation to respiratory sensitization has started recently. This article further evaluates the ability of the DPRA to predict the respiratory sensitization potential of chemicals. In addition, the added value of applying High Performance Liquid Chromatography (HPLC)-MS and measurements after 20 minutes and 24 hours of incubation was evaluated. Eighteen respiratory sensitizers (10 haptens, 3 prehaptens, and 5 prohaptens) and 14 nonsensitizers were tested with 2-model peptides. Based on peptide depletion, a prediction model was proposed for the identification of (respiratory) sensitizers. Application of mass spectrometry and measurements at 2 time-points increased prediction accuracy of the assay by resolving discordant results. The prediction model correctly identified all haptens and prehaptens as sensitizers. The 5 prohaptens were not identified as sensitizers, most likely due to lack of metabolic activity in the DPRA. All but 1 nonsensitizer was correctly predicted. The model, therefore, shows an accuracy of 78% for the tested dataset. Unfortunately, this assay cannot be used to distinguish respiratory from skin sensitizers. To make this distinction, the DPRA needs to be combined with other test methods that are able to identify respiratory sensitizers.