Search

Kinetic rate constants (kGSH) for the reaction of compounds acting as Michael acceptors with glutathione (GSH) were modelled by quantum chemical transition-state calculations at the B3LYP/6-31G** and B3LYP/TZVP level. The data set included α, β-unsaturated aldehydes, ketones and esters, with double bonds and triple bonds, linear and cyclic systems, both with and without substituents in the α-position. Predicted values for kGSH were found to be in good agreement with experimental kGSH values. Factors affecting rate constants have been elucidated, especially solvent effects and the influence of steric hindrance. Solvent effects were examined by adding explicit solvent molecules to the system and by using a polarizable continuum solvent model. Detailed analysis of transition-state energies shows that the reaction is reversible. The reactive enolic intermediate plays an important role in Michael addition to GSH, while the subsequent keto-enol-tautomerism is not rate limiting. [ABSTRACT FROM AUTHOR]

The aim of this work was to develop a high-quality 1-octanol/water partition coefficient-dependent (log P) baseline quantitative structure-activity relationship (QSAR) for the toxicity (log [image omitted]) of classic non-polar narcotics to Tetrahymena pyriformis, and subsequently use this model to define the domain of applicability for baseline narcosis. The toxicities to T. pyriformis of 514 possible non-polar narcotics were assessed. A QSAR to predict toxicity was created from a training set of 87 classic non-polar narcotics (the saturated alcohols and ketones): log [image omitted] = 0.78 log P-2.01 (n = 87, r2 = 0.96). This model was then used to predict the toxicity of the remaining chemicals. The chemicals from the large dataset which were poorly predicted by the model (i.e. the prediction was > ±0.5 log units from the experimental value) were used to aid the definition of structural categories of chemicals which are not non-polar narcotics. Doing so has enabled the domain for non-polar narcosis to be defined in terms of structural categories. Defining domains of applicability for QSAR models is important if they are to be considered for making predictions of toxicity for regulatory purposes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

An abstract for the article "Application of a Chemical Reactivity Database to Predict Toxicity for Reactive Mechanisms," by J.A.H. Schwöbel, J.C. Madden and M.T.D. Cronin is presented.

It has been documented that less strongly oxidising radicals, including alkylperoxyl radicals, react with carotenoids via hydrogen atom transfer, thereby generating the neutral carotene radical. In this work, we have studied the reaction between the weakly oxidising t-butyl peroxyl radical (TBHP·) and β-carotene in dichloromethane under low oxygen pressure. Despite the rather low value of the reduction potential of TBHP·, we have succeeded in the formation of the cation radical of β-carotene (β-carotene·+). Based on EPR and NIR-IR spectroscopy, the reaction mechanism for this reaction has been assigned to an electron transfer mechanism. This relatively rare mechanism for the reaction between less strongly oxidising species, such as the TBHP· radical and carotenoids has been explained on the basis of DFT and DFT–PCM calculations. [Copyright &y& Elsevier]

Estimating the survival probability of animals released from fisheries can improve the overall understanding of animal biology with implications for fisheries management, conservation and animal welfare. Vitality indicators are simple visual measures of animal condition that change in response to stressors (like fisheries capture) and can be assessed to predict post-release survival. These indicators typically include immediate reflex responses which are typically combined into a score. Vitality indicators are straight-forward and non-invasive metrics that allow users to quantify how close (or far) an animal is from a normal, 'healthy' or baseline state, which in turn can be correlated with outcomes such as survival probability, given appropriate calibration. The literature on using vitality indicators to predict post-release survival of animals has grown rapidly over the past decade. We identified 136 papers that used vitality indicators in a fisheries context. These studies were primarily focused on marine and freshwater fishes, with a few examples using herptiles and crustaceans. The types of vitality indicators are diverse and sometimes taxa-specific (e.g. pinching leg of turtles, spraying water at nictitating membrane of sharks) with the most commonly used indicators being those that assess escape response or righting response given the vulnerability of animals when those reflexes are impaired. By presenting Pacific salmon fisheries as a case study, we propose a framework for using vitality indicators to predict survival across taxa and fisheries., Competing Interests: The authors declare no competing interests. Cooke and Raby are on the editorial board of Conservation Physiology but were not involved in the editorial process., (© The Author(s) 2024. Published by Oxford University Press and the Society for Experimental Biology.)

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., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 The Author(s).)

Computational approaches are increasingly used to predict toxicity due, in part, to pressures to find alternatives to animal testing. Read-across is the "new paradigm" which aims to predict toxicity by identifying similar, data rich, source compounds. This assumes that similar molecules tend to exhibit similar activities i.e. molecular similarity is integral to read-across. Various of molecular fingerprints and similarity measures may be used to calculate molecular similarity. This study investigated the value and concordance of the Tanimoto similarity values calculated using six widely used fingerprints within six toxicological datasets. There was considerable variability in the similarity values calculated from the various molecular fingerprints for diverse compounds, although they were reasonably concordant for homologous series acting via a common mechanism. The results suggest generic fingerprint-derived similarities are likely to be optimally predictive for local datasets, i.e. following sub-categorisation. Thus, for read-across, generic fingerprint-derived similarities are likely to be most predictive after chemicals are placed into categories (or groups), then similarity is calculated within those categories, rather than for a whole chemically diverse dataset., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Introduction: The cost of in vivo and in vitro screening of ADME properties of compounds has motivated efforts to develop a range of in silico models. At the heart of the development of any computational model are the data; high quality data are essential for developing robust and accurate models. The characteristics of a dataset, such as its availability, size, format and type of chemical identifiers used, influence the modelability of the data. Areas covered: This review explores the usefulness of publicly available ADME datasets for researchers to use in the development of predictive models. More than 140 ADME datasets were collated from publicly available resources and the modelability of 31 selected datasets were assessed using specific criteria derived in this study. Expert opinion: Publicly available datasets differ significantly in information content and presentation. From a modelling perspective, datasets should be of adequate size, available in a user-friendly format with all chemical structures associated with one or more chemical identifiers suitable for automated processing (e.g. CAS number, SMILES string or InChIKey). Recommendations for assessing dataset suitability for modelling and publishing data in an appropriate format are discussed.

Nowadays nanotechnology is one of the most promising areas of science. The number and quantity of synthesized nanomaterials increase exponentially, therefore it is reasonable to expect that comprehensive risk assessment based only on empirical testing of all novel engineered nanoparticles (NPs) will very soon become impossible. Hence, the development of computational methods complementary to experimentation is very important. Quantitative structure-property relationship (QSPR) and quantitative structure-activity relationship (QSAR) models widely used in pharmaceutical chemistry and environmental science can also be modified and adopted for nanotechnology to predict physico-chemical properties and toxicity of empirically untested nanomaterials. All QSPR/QSAR modelling activities are based on experimentally derived data. It is important that, within a given data set, all values should be consistent, of high quality and measured according to a standardized protocol. Unfortunately, the amount of such data available for engineered nanoparticles in various data sources (i.e. databases and the literature) is very limited and seldom measured with a standardized protocol. Therefore, we have proposed a framework for collecting and evaluating the existing data, with the focus on possible applications for computational evaluation of properties and biological activities of nanomaterials.

Existing toxicological data may be used for a variety of purposes such as hazard and risk assessment or toxicity prediction. The potential use of such data is, in part, dependent upon their quality. Consideration of data quality is of key importance with respect to the application of chemicals legislation such as REACH. Whether data are being used to make regulatory decisions or build computational models, the quality of the output is reflected by the quality of the data employed. Therefore, the need to assess data quality is an important requirement for making a decision or prediction with an appropriate level of confidence. This study considers the biological and chemical factors that may impact upon toxicological data quality and discusses the assessment of data quality. Four general quality criteria are introduced and existing data quality assessment schemes are discussed. Two case study datasets of skin sensitization data are assessed for quality providing a comparison of existing assessment methods. This study also discusses the limitations and difficulties encountered during quality assessment, including the use of differing quality schemes and the global versus chemical-specific assessments of quality. Finally, a number of recommendations are made to aid future data quality assessments.

A database was collated of published experimental logarithmic values for the relative retention factors (log k(IAM)) measured using an immobilized artificial membrane column and high-performance liquid chromatography (IAM HPLC). Log k(IAM) is an alternative measure of hydrophobicity to the octanol/water partition coefficient (log K(OW)). While there are several accepted methods to measure log K(OW), no standardized method exists to determine log k(IAM). The database of collated log k(IAM) values includes 13 key experimental parameters and contains 1,686 values for 555 compounds, which are predominantly polar organic compounds and include drug molecules and surfactants. These compounds are acidic, basic, and neutral and both ionized and un-ionized under the conditions of analysis. The data compiled demonstrated experimental variability for each experimental parameter considered, including column stationary phase, pH, temperature, and mobile phase. Reducing the experimental variability allowed for greater consistency in the datasets., (Copyright © 2011 SETAC.)

It has been estimated that reproductive and developmental toxicity tests will account for a significant proportion of the testing costs associated with REACH compliance. Consequently, the use of alternative methods to predict developmental toxicity is an attractive prospect. The present study evaluates a number of computational models and tools which can be used to aid assessment of developmental toxicity potential. The performance and limitations of traditional (quantitative) structure-activity relationship ((Q)SARs) modelling, structural alert-based expert system prediction and chemical profiling approaches are discussed. In addition, the use of category formation and read-across is also addressed. This study demonstrates the limited success of current modelling methods when used in isolation. However, the study also indicates that when used in combination, in a weight-of-evidence approach, better use may be made of the limited toxicity data available and predictivity improved. Recommendations are provided as to how this area could be further developed in the future., (Copyright 2009 Elsevier Inc. All rights reserved.)

A number of mechanisms have been identified that can lead to (acute) aquatic toxicity. The assignment of compounds to a particular mechanism of action is important in the development and utilisation of (quantitative) structure-activity relationships ((Q)SARs) for ecotoxicity. Assignment to a mechanism can be difficult; however in 1992 Verhaar et al. published a series of structural rules which aimed to classify compounds according to mechanism of action. Recent interest has seen the Verhaar rules coded into freely available software such as Toxtree available from the European Chemicals Bureau. To date, a complete critical evaluation of these rules has been lacking. Therefore, the aim of this study was to evaluate the Toxtree implementation of the Verhaar rules using two well characterised aquatic toxicity datasets (Pimephales promelas and Tetrahymena pyriformis phenol databases) for which mechanisms of toxic action are well established. The present study highlights rule, and possible coding, errors that may lead to misclassifications. Improvements to both the rules and prediction architecture are suggested. In particular further rules to improve predictions for polar narcosis (class 2) are suggested.

This study presents an analysis of the ability of a two-parameter response surface, a multiple linear regression and a neural network model to produce global quantitative structure-activity relationships (QSARs) to predict the toxic potency of phenols to Tetrahymena pyriformis. The phenolic toxicity data set analysed is characterised by multiple mechanisms of toxic action. The study aimed to evaluate the confidence that can be applied to the modelling of the differing mechanisms of action. Assessment of confidence was decided in terms of whether the statistics for the global models reflect the ability of the QSARs to model the individual mechanisms of toxic action present in the data set. The results showed that the global statistics only reflected the ability of models to predict the two non-covalent mechanisms (polar narcosis and respiratory uncoupling), with the metabolically transformed and electrophilic mechanism (pre-electrophiles and soft electrophiles) being modelled poorly by all three model building methods. The results confirm the difficulty in modelling electrophilic mechanisms of toxic action. The results also highlight the fact that this poor predictivity is often 'hidden' in good statistical fit of some global models. In particular these results emphasise that for practical predictive purposes the mechanistic applicability domain is required to give confidence to estimated toxicity values.

There is a great need to predict the antioxidant properties of molecules such as carotenoids. These compounds are of great interest due to their contribution to various important biological and industrial processes, including toxicity and fate. In our study, redox potentials were compiled from several literature sources. Redox potential values ranged from 537.2 mV for zeaxanthin up to 691.5 mV for beta-carotene; they correspond to the formation of cation radicals, using the standard calomel electrode (SCE). The redox potential values were measured using conventional electrochemical techniques, cyclic voltammetry and Osteryoung square-wave voltammetry. A quantitative structure-activity relationship (QSAR) was developed to model and consequently to predict the values of redox potential. The predicted values of redox potential for four external carotenoids, namely beta-carotene, zeaxanthin, cantaxanthin and astaxanthin, are presented and discussed. They indicate the dependence of redox potential on structure, donor and acceptor groups and polarisability.

Adaptive responses of bacteria that involve sensing the presence of other bacteria are often critical for proliferation and the expression of virulence characteristics. The autoinducer II (AI-2) pathway has recently been shown to be a mechanism for sensing other bacteria that is highly conserved among diverse bacterial species, including Gram-positive pathogens. However, a role for this pathway in the regulation of virulence factors in Gram-positive pathogens has yet to be established. In this study, we have inactivated luxS, an essential component of the AI-2 pathway, in the Gram-positive pathogen Streptococcus pyogenes. Analyses of the resulting mutants revealed the aberrant expression of several virulence properties that are regulated in response to growth phase, including enhanced haemolytic activity, and a dramatic reduction in the expression of secreted proteolytic activity. This latter defect was associated with a reduced ability to secrete and process the precursor of the cysteine protease (SpeB) as well as a difference in the timing of expression of the protease. Enhanced haemolytic activity of the luxS strain was also shown to be linked with an increased expression of the haemolysin S-associated gene sagA. Disruptions of luxS in these mutants also produced a media-dependent growth defect. Finally, an allelic replacement analysis of an S. pyogenes strain with a naturally occurring insertion of IS1239 in luxS suggested a mechanism for modulation of virulence during infection. Results from this study suggest that luxS makes an important contribution to the regulation of S. pyogenes virulence factors.

Type III secretion for injection of effector proteins into host cells has not been described for Gram-positive bacteria despite their importance in disease. Here, we describe an injection pathway for the Gram-positive pathogen Streptococcus pyogenes that utilizes streptolysin O (SLO), a cholesterol-dependent cytolysin. The data support a model in which an effector is translocated through the SLO pore by a polarized process. The effector, SPN (S. pyogenes NAD-glycohydrolase), is capable of producing the potent second messenger cyclic ADP-ribose, and SLO and SPN act synergistically to trigger cytotoxicity. These data provide a novel paradigm for the function of the cholesterol-dependent cytolysin family and its wide distribution suggests that cytolysin-mediated translocation (CMT) may be the equivalent of type III secretion for Gram-positive pathogens.