Experimental verification of structural alerts for the protein binding of sulfur-containing compounds

Machine learning-assisted data filtering and QSAR models for prediction of chemical acute toxicity on rat and mouse

Machine learning (ML) methods provide a new opportunity to build quantitative structure-activity relationship (QSAR) models for predicting chemicals' toxicity based on large toxicity data sets, but they are limited in insufficient model robustness due to poor data set quality for chemicals with certain structures. To address this issue and improve model robustness, we built a large data set on rat oral acute toxicity for thousands of chemicals, then used ML to filter chemicals favorable for regression models (CFRM). In comparison to chemicals not favorable for regression models (CNRM), CFRM accounted for 67% of chemicals in the original data set, and had a higher structural similarity and a smaller toxicity distribution in 2-4 log₁₀ (mg/kg). The performance of established regression models for CFRM was greatly improved, with root-mean-square deviations (RMSE) in the range of 0.45-0.48 log₁₀ (mg/kg). Classification models were built for CNRM using all chemicals in the original data set, and the area under receiver operating characteristic (AUROC) reached 0.75-0.76. The proposed strategy was successfully applied to a mouse oral acute data set, yielding RMSE and AUROC in the range of 0.36-0.38 log₁₀ (mg/kg) and 0.79, respectively.

Assessment of commercial polymers with and without reactive groups using amino acid derivative reactivity assay based on both molar concentration approach and gravimetric approach

The amino acid derivative reactivity assay (ADRA), an alternative method for testing skin sensitization, has been established based on the molar concentration approach. However, the additional development of gravimetric concentration and fluorescence detection methods has expanded its range of application to mixtures, which cannot be evaluated using the conventional testing method, the direct peptide reactivity assay (DPRA). Although polymers are generally treated as mixtures, there have been no reports of actual polymer evaluations using alternative methods owing to their insolubility. Therefore, in this study, we evaluated skin sensitization potential of polymers, which is difficult to predict, using ADRA. As polymers have molecular weights ranging from several thousand to more than several tens of thousand Daltons, they are unlikely to cause skin sensitization due to their extremely low penetration into the skin, according to the 500-Da rule. However, if highly reactive functional groups remain at the ends or side chains of polymers, relatively low-molecular-weight polymer components may penetrate the skin to cause sensitization. Polymers can be roughly classified into three major types based on the features of their constituent monomers; we investigated the sensitization capacity of each type of polymer. Polymers with alert sensitization structures at their ends were classified as skin sensitizers, whereas those with no residual reactive groups were classified as nonsensitizers. Although polymers with a glycidyl group need to be evaluated carefully, we concluded that ADRA (0.5 mg/ml) is generally sufficient for polymer hazard assessment.

A scheme to evaluate structural alerts to predict toxicity - Assessing confidence by characterising uncertainties

Structure-activity relationships (SARs) in toxicology have enabled the formation of structural rules which, when coded as structural alerts, are essential tools in in silico toxicology. Whilst other in silico methods have approaches for their evaluation, there is no formal process to assess the confidence that may be associated with a structural alert. This investigation proposes twelve criteria to assess the uncertainty associated with structural alerts, allowing for an assessment of confidence. The criteria are based around the stated purpose, description of the chemistry, toxicology and mechanism, performance and coverage, as well as corroborating and supporting evidence of the alert. Alerts can be given a confidence assessment and score, enabling the identification of areas where more information may be beneficial. The scheme to evaluate structural alerts was placed in the context of various use cases for industrial and regulatory applications. The analysis of alerts, and consideration of the evaluation scheme, identifies the different characteristics an alert may have, such as being highly specific or generic. These characteristics may determine when an alert can be used for specific uses such as identification of analogues for read-across or hazard identification.

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Structural alerts are useful tools for predictive toxicology.

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12 criteria to evaluate structural alerts have been identified.

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A strategy to determine confidence of structural alerts is presented.

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Different use cases require different characteristics of structural alerts.

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A Scheme to Evaluate Structural Alerts to Predict Toxicity – Assessing Confidence By Characterising Uncertainties.

COSMOS next generation - A public knowledge base leveraging chemical and biological data to support the regulatory assessment of chemicals

The COSMOS Database (DB) was originally established to provide reliable data for cosmetics-related chemicals within the COSMOS Project funded as part of the SEURAT-1 Research Initiative. The database has subsequently been maintained and developed further into COSMOS Next Generation (NG), a combination of database and in silico tools, essential components of a knowledge base. COSMOS DB provided a cosmetics inventory as well as other regulatory inventories, accompanied by assessment results and in vitro and in vivo toxicity data. In addition to data content curation, much effort was dedicated to data governance - data authorisation, characterisation of quality, documentation of meta information, and control of data use. Through this effort, COSMOS DB was able to merge and fuse data of various types from different sources. Building on the previous effort, the COSMOS Minimum Inclusion (MINIS) criteria for a toxicity database were further expanded to quantify the reliability of studies. COSMOS NG features multiple fingerprints for analysing structure similarity, and new tools to calculate molecular properties and screen chemicals with endpoint-related public profilers, such as DNA and protein binders, liver alerts and genotoxic alerts. The publicly available COSMOS NG enables users to compile information and execute analyses such as category formation and read-across. This paper provides a step-by-step guided workflow for a simple read-across case, starting from a target structure and culminating in an estimation of a NOAEL confidence interval. Given its strong technical foundation, inclusion of quality-reviewed data, and provision of tools designed to facilitate communication between users, COSMOS NG is a first step towards building a toxicological knowledge hub leveraging many public data systems for chemical safety evaluation. We continue to monitor the feedback from the user community at support@mn-am.com.

Development of an Enhanced Mechanistically Driven Mode of Action Classification Scheme for Adverse Effects on Environmental Species

This study developed a novel classification scheme to assign chemicals to a verifiable mechanism of (eco-)toxicological action to allow for grouping, read-across, and in silico model generation. The new classification scheme unifies and extends existing schemes and has, at its heart, direct reference to molecular initiating events (MIEs) promoting adverse outcomes. The scheme is based on three broad domains of toxic action representing nonspecific toxicity (e.g., narcosis), reactive mechanisms (e.g., electrophilicity and free radical action), and specific mechanisms (e.g., associated with enzyme inhibition). The scheme is organized at three further levels of detail beyond broad domains to separate out the mechanistic group, specific mechanism, and the MIEs responsible. The novelty of this approach comes from the reference to taxonomic diversity within the classification, transparency, quality of supporting evidence relating to MIEs, and that it can be updated readily.

Antiplasmodial activity of sulfonylhydrazones: in vitro and in silico approaches

Malaria is still a life-threatening public health issue, and the upsurge of resistant strains requires continuous generation of active molecules. In this work, 35 sulfonylhydrazone derivatives were synthesized and evaluated against Plasmodium falciparum chloroquine-sensitive (3D7) and resistant (W2) strains. The most promising compound, 5b, had an IC₅₀ of 0.22 μM against W2 and was less cytotoxic and 26-fold more selective than chloroquine. The structure-activity relationship model, statistical analysis and molecular modeling studies suggested that antiplasmodial activity was related to hydrogen bond acceptor count, molecular weight and partition coefficient of octanol/water and displacement of frontier orbitals to the heteroaromatic ring beside the imine bond. This study demonstrates that the synthesized molecules with a simple scaffold allow the hit-to-lead process for new antimalarials to commence.

Ethics of animal research in human disease remediation, its institutional teaching; and alternatives to animal experimentation

Animals have been used in research and teaching for a long time. However, clear ethical guidelines and pertinent legislation were instated only in the past few decades, even in developed countries with Judeo-Christian ethical roots. We compactly cover the basics of animal research ethics, ethical reviewing and compliance guidelines for animal experimentation across the developed world, "our" fundamentals of institutional animal research ethics teaching, and emerging alternatives to animal research. This treatise was meticulously constructed for scientists interested/involved in animal research. Herein, we discuss key animal ethics principles - Replacement/Reduction/Refinement. Despite similar undergirding principles across developed countries, ethical reviewing and compliance guidelines for animal experimentation vary. The chronology and evolution of mandatory institutional ethical reviewing of animal experimentation (in its pioneering nations) are summarised. This is followed by a concise rendition of the fundamentals of teaching animal research ethics in institutions. With the advent of newer methodologies in human cell-culturing, novel/emerging methods aim to minimise, if not avoid the usage of animals in experimentation. Relevant to this, we discuss key extant/emerging alternatives to animal use in research; including organs on chips, human-derived three-dimensional tissue models, human blood derivates, microdosing, and computer modelling of various hues.

Adverse Outcome Pathway (AOP) Informed Modeling of Aquatic Toxicology: QSARs, Read-Across, and Interspecies Verification of Modes of Action

Alternative approaches have been promoted to reduce the number of vertebrate and invertebrate animals required for the assessment of the potential of compounds to cause harm to the aquatic environment. A key philosophy in the development of alternatives is a greater understanding of the relevant adverse outcome pathway (AOP). One alternative method is the fish embryo toxicity (FET) assay. Although the trends in potency have been shown to be equivalent in embryo and adult assays, a detailed mechanistic analysis of the toxicity data has yet to be performed; such analysis is vital for a full understanding of the AOP. The research presented herein used an updated implementation of the Verhaar scheme to categorize compounds into AOP-informed categories. These were then used in mechanistic (quantitative) structure-activity relationship ((Q)SAR) analysis to show that the descriptors governing the distinct mechanisms of acute fish toxicity are capable of modeling data from the FET assay. The results show that compounds do appear to exhibit the same mechanisms of toxicity across life stages. Thus, this mechanistic analysis supports the argument that the FET assay is a suitable alternative testing strategy for the specified mechanisms and that understanding the AOPs is useful for toxicity prediction across test systems.