CAS Electrophile (El) Nucleophile (Nu) Parameter Unit Value Error log(Val) 818-61-1 2-Hydroxyethyl acrylate 4-Nitrobenzenethiol t1/2(NBT) min 2.45E-01 144-48-9 2-Iodoacetamide 4-Nitrobenzenethiol t1/2(NBT) min 1.83E-03 2682-20-4 2-Methyl-2H-isothiazolin-3-one 4-Nitrobenzenethiol t1/2(NBT) min 1.60E-03 25567-67-3 3-Chloro-1.2-dinitrobenzene 4-Nitrobenzenethiol t1/2(NBT) min 1.25E-02 2497-21-4 4-Hexen-3-one 4-Nitrobenzenethiol t1/2(NBT) min 2.77E-02 26172-55-4 5-Chloro-2-methyl-4-isothiazolin-3-one 4-Nitrobenzenethiol t1/2(NBT) min 5.83E-05 108-24-7 Acetic anhydride 4-Nitrobenzenethiol t1/2(NBT) min 9.83E-04 107-02-8 Acrolein 4-Nitrobenzenethiol t1/2(NBT) min 8.25E-02 100-39-0 Benzyl bromide 4-Nitrobenzenethiol t1/2(NBT) min 4.67E-05 57-57-8 beta-Propiolactone 4-Nitrobenzenethiol t1/2(NBT) min 1.62E-04 88-11-9 Diethylthiocarbamoyl chloride 4-Nitrobenzenethiol t1/2(NBT) min 1.52E-03 886-38-4 Diphenylcyclopropenone 4-Nitrobenzenethiol t1/2(NBT) min 1.05E-05 140-88-5 Ethyl acrylate 4-Nitrobenzenethiol t1/2(NBT) min 7.70E-01 50-00-0 Formaldehyde 4-Nitrobenzenethiol t1/2(NBT) min 1.25E-03 55965-84-9 Kathon CG 4-Nitrobenzenethiol t1/2(NBT) min 2.17E-04 124-63-0 Methyl sulfonyl chloride 4-Nitrobenzenethiol t1/2(NBT) min 7.67E-04 128-53-0 N-Ethylmaleimide 4-Nitrobenzenethiol t1/2(NBT) min 3.33E-04 Nitrobenzyl bromide 4-Nitrobenzenethiol t1/2(NBT) min 9.83E-06 15646-46-5 Oxazolone 4-Nitrobenzenethiol t1/2(NBT) min 9.00E-06 106-51-4 p-Benzoquinone 4-Nitrobenzenethiol t1/2(NBT) min 7.33E-06 1939-99-7 Phenylmethanesulfonyl chloride 4-Nitrobenzenethiol t1/2(NBT) min 6.00E-03 2892-51-5 Squaric acid 4-Nitrobenzenethiol t1/2(NBT) min 6.12E-02 584-84-9 Toluene 2.4-diisocyanate 4-Nitrobenzenethiol t1/2(NBT) min 4.50E-04 23726-91-2S2 Schwöbel et al.

Several pieces of legislation have led to an increased interest in the use of in silico methods, specifically the formation of chemical categories for the assessment of toxicological endpoints. For a number of endpoints, this requires a detailed knowledge of the electrophilic reaction chemistry that governs the ability of an exogenous chemical to form a covalent adduct. Historically, this chemistry has been defined as compilations of structural alerts without documenting the associated electrophilic chemistry mechanisms. To address this, this article has reviewed the literature defining the structural alerts associated with covalent protein binding and detailed the associated electrophilic reaction chemistry. This information is useful to both toxicologists and regulators when using the chemical category approach to fill data gaps for endpoints involving covalent protein binding. The structural alerts and associated electrophilic reaction chemistry outlined in this review have been incorporated into the OECD (Q)SAR Toolbox, a freely available software tool designed to fill data gaps in a regulatory environment without the need for further animal testing.

Skin sensitisation is a key endpoint under REACH as it is costly and its assessment currently has a high dependency on animal testing. In order to reduce both the cost and the numbers of animals tested, it is likely that (quantitative) structure-activity relationships ((Q)SAR) and read-across methods will be utilised as part of intelligent testing strategies. The majority of skin sensitisers elicit their effect via covalent bond formation with skin proteins. These reactions have been understood in terms of well defined nucleophilic-electrophilic reaction chemistry. Thus, a first step in (Q)SAR analysis is the assignment of a chemical's potential mechanism of action enabling it to be placed in an appropriate reactivity domain. The aim of this study was to design a series of SMARTS patterns capable of defining these reactivity domains. This was carried out using a large database of local lymph node assay (LLNA) results that had had potential mechanisms of action assigned to them using expert knowledge. A simple algorithm was written enabling the SMARTS patterns to be used to screen a database of SMILES strings. The SMARTS patterns were then evaluated using a second, smaller, test set of LLNA results which had also had potential mechanisms of action assigned by experts. The results showed that the SMARTS patterns provided an excellent method of identifying potential electrophilic mechanisms. The findings are supported, in part, by molecular orbital calculations which confirm assignment of reactive mechanism of action. The ability to define a chemical's potential reaction mechanism is likely to be of significant benefit to regulators and risk assessors as it enables category formation and subsequent read-across to be performed.

Read across is a powerful tool to predict toxicity from structure: It relies on "obvious" chemical similarities to allow for interpolation of activity. This study has extended the read across concept within a known mechanism of action to be quantitative. The chemicals that have been chosen are skin sensitizers and are considered to elicit this response by direct interaction through a direct-acting Michael type addition electrophilic mechanism of action. The Michael addition domain is well-defined for skin sensitizers; however, developing quantitative models for predicting potency within the domain has proven to be difficult. This study highlights the ability of an electrophilicity index (omega) to be used as a measure of similarity for sensitizing chemicals acting through the Michael addition mechanism. The index is shown to offer a chemically interpretable qualitative ranking of the chemicals within the Michael acceptor domain, enabling potentially nonsensitizing and extremely sensitizing chemicals to be easily identified. This study also demonstrates the utility of omega to make predictions of skin sensitization using a mechanism-based read across model. Predictions were made for 19 chemicals within the Michael acceptor domain, with the majority being in good agreement with the experimentally determined values. The mechanism-based read across predictions are in keeping with the OECD principles of transparency and simplicity for quantitative structure-activity relationships and are likely to be of significant benefit to regulators and risk assessors.

The dissolution of a chemical into water is a process fundamental to both chemistry and biology. The persistence of a chemical within the environment and the effects of a chemical within the body are dependent primarily upon aqueous solubility. With the well-documented limitations hindering the accurate experimental determination of aqueous solubility, the utilization of predictive methods have been widely investigated and employed. The setting of a solubility challenge by this journal proved an excellent opportunity to explore several different modeling methods, utilizing a supplied dataset of high-quality aqueous solubility measurements. Four contrasting approaches (simple linear regression, artificial neural networks, category formation, and available in silico models) were utilized within our laboratory and the quality of these predictions was assessed. These were chosen to span the multitude of modeling methods now in use, while also allowing for the evaluation of existing commercial solubility models. The conclusions of this study were surprising, in that a simple linear regression approach proved to be superior over more complex modeling methods. Possible explanations for this observation are discussed and also recommendations are made for future solubility prediction.

Certain types of low molecular weight chemicals have the ability to cause respiratory sensitization via haptenation of carrier proteins. It has been suggested that such chemicals must contain multiple "reactive" functional groups to elicit an immune response. In contrast to the well-developed electrophilic reaction chemistry ideas detailing the initial haptenation event for skin sensitization, no detailed mechanistic chemistry analysis has been performed for respiratory sensitization. The aim of this study, therefore, was to perform an electrophilic reaction chemistry analysis to explain the differing respiratory sensitizing potentials of 16 chemicals containing both single and multiple functional groups. The analysis has been supported by quantum chemical calculations probing the electrophilicities of the reactive chemicals. These calculations suggest that within each mechanistic category differing "reactivity thresholds" exist that must be passed for respiratory sensitization to occur. In addition, this study highlights how such mechanistically driven category formation could be used as an in silico hazard identification tool.

The ability of a compound to cause adverse effects to the liver is one of the most common reasons for drug development failures and the withdrawal of drugs from the market. Such adverse effects can vary tremendously in severity, leading to an array of possible drug-induced liver injuries (DILIs). As a result, it is not surprising that drug development has evolved into a complex and multifaceted process including methods aiming to identify potential liver toxicities. Unfortunately, hepatotoxicity remains one of the most complex and poorly understood areas of human toxicity; thus it is a significant challenge to identify potential hepatotoxins. The performance of existing methods to identify hepatotoxicity requires improvement. The current study details a scheme for generating chemical categories and the development of structural alerts able to identify potential hepatotoxins. The study utilized a diverse 951-compound dataset and used structural similarity methods to produce a number of structurally restricted categories. From these categories, 16 structural alerts associated with observed human hepatotoxicity were developed. Furthermore, the mechanism(s) by which these compounds cause hepatotoxicity were investigated and a mechanistic rationale was proposed, where possible, to yield mechanistically supported structural alerts. Alerts of this nature have the potential to be used in the screening of compounds to highlight potential hepatotoxicity, whilst the chemical categories themselves are important in applying read-across approaches. The scheme presented in this study also has the potential to act as a knowledge generator serving as an excellent starting platform from which to conduct additional toxicological studies.

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.