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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.

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.

No abstract

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.