Using vitality indicators to predict survival of aquatic animals released from fisheries.

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.

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.

RNase L Antiviral Activity Is Not a Critical Component of the Oas1b-Mediated Flavivirus Resistance Phenotype.

In mice, resistance to central nervous system (CNS) disease induced by members of the genus Flavivirus is conferred by an allele of the 2'-5' oligoadenylate synthetase 1b gene that encodes the inactive full-length protein (Oas1b-FL). The susceptibility allele encodes a C-terminally truncated protein (Oas1b-tr). We show that the efficiency of neuron infection in the brains of resistant and susceptible mice is similar after an intracranial inoculation of two flaviviruses, but amplification of viral proteins and double-stranded RNA (dsRNA) is inhibited in infected neurons in resistant mouse brains at later times. Active OAS proteins detect cytoplasmic dsRNA and synthesize short 2'-5'-linked oligoadenylates (2'-5'A) that interact with the latent endonuclease RNase L, causing it to dimerize and cleave single-stranded RNAs. To evaluate the contribution of RNase L to the resistance phenotype in vivo, we created a line of resistant RNase L-/- mice. Evidence of RNase L activation in infected RNase L+/+ mice was indicated by higher levels of viral RNA in the brains of infected RNase L-/- mice. Activation of type I interferon (IFN) signaling was detected in both resistant and susceptible brains, but Oas1a and Oas1b mRNA levels were lower in RNase L+/+ mice of both types, suggesting that activated RNase L also has a proflaviviral effect. Inhibition of virus replication was robust in resistant RNase L-/- mice, indicating that activated RNase L is not a critical factor in mediating this phenotype.IMPORTANCE The mouse genome encodes a family of Oas proteins that synthesize 2'-5'A in response to dsRNA. 2'-5'A activates the endonuclease RNase L to cleave single-stranded viral and cellular RNAs. The inactive, full-length Oas1b protein confers flavivirus-specific disease resistance. Although similar numbers of neurons were infected in resistant and susceptible brains after an intracranial virus infection, viral components amplified only in susceptible brains at later times. A line of resistant RNase L-/- mice was used to evaluate the contribution of RNase L to the resistance phenotype in vivo Activation of RNase L antiviral activity by flavivirus infection was indicated by increased viral RNA levels in the brains of RNase L-/- mice. Oas1a and Oas1b mRNA levels were higher in infected RNase L-/- mice, indicating that activated RNase L also have a proflaviviral affect. However, the resistance phenotype was equally robust in RNase L-/- and RNase L+/+ mice.

Next generation physiologically based kinetic (NG-PBK) models in support of regulatory decision making.

The fields of toxicology and chemical risk assessment seek to reduce, and eventually replace, the use of animals for the prediction of toxicity in humans. In this context, physiologically based kinetic (PBK) modelling based on in vitro and in silico kinetic data has the potential to a play significant role in reducing animal testing, by providing a methodology capable of incorporating in vitro human data to facilitate the development of in vitro to in vivo extrapolation of hazard information. In the present article, we discuss the challenges in: 1) applying PBK modelling to support regulatory decision making under the toxicology and risk-assessment paradigm shift towards animal replacement; 2) constructing PBK models without in vivo animal kinetic data, while relying solely on in vitro or in silico methods for model parameterization; and 3) assessing the validity and credibility of PBK models built largely using non-animal data. The strengths, uncertainties, and limitations of PBK models developed using in vitro or in silico data are discussed in an effort to establish a higher degree of confidence in the application of such models in a regulatory context. The article summarises the outcome of an expert workshop hosted by the European Commission Joint Research Centre (EC-JRC) - European Union Reference Laboratory for Alternatives to Animal Testing (EURL ECVAM), on "Physiologically-Based Kinetic modelling in risk assessment - reaching a whole new level in regulatory decision-making" held in Ispra, Italy, in November 2016, along with results from an international survey conducted in 2017 and recently reported activities occurring within the PBK modelling field. The discussions presented herein highlight the potential applications of next generation (NG)-PBK modelling, based on new data streams.

Molecular fingerprint-derived similarity measures for toxicological read-across: Recommendations for optimal use.

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.

Characterisation of data resources for in silico modelling: benchmark datasets for ADME properties.

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.

Proposal of an in silico profiler for categorisation of repeat dose toxicity data of hair dyes.

This study outlines the analysis of 94 chemicals with repeat dose toxicity data taken from Scientific Committee on Consumer Safety opinions for commonly used hair dyes in the European Union. Structural similarity was applied to group these chemicals into categories. Subsequent mechanistic analysis suggested that toxicity to mitochondria is potentially a key driver of repeat dose toxicity for chemicals within each of the categories. The mechanistic hypothesis allowed for an in silico profiler consisting of four mechanism-based structural alerts to be proposed. These structural alerts related to a number of important chemical classes such as quinones, anthraquinones, substituted nitrobenzenes and aromatic azos. This in silico profiler is intended for grouping chemicals into mechanism-based categories within the adverse outcome pathway paradigm.

Evaluation criteria for the quality of published experimental data on nanomaterials and their usefulness for QSAR modelling.

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.

Hepatotoxicity: a scheme for generating chemical categories for read-across, structural alerts and insights into mechanism(s) of action.

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.

Prediction of immobilised artificial membrane chromatography retention factors using theoretical molecular fragments and structural features.

Many in silico alternatives to aquatic toxicity tests rely on hydrophobicity-based quantitative structure-activity relationships (QSARs). Hydrophobicity is often estimated as log P, where P is the octanol-water partition coefficient. Immobilised artificial membrane (IAM) high performance liquid chromatography (HPLC) may be a more biologically relevant alternative to log P. The aim of this study was to investigate the applicability of a theoretical structural fragment and feature-based method to predict log k IAM (the logarithm of the retention index determined by IAM-HPLC) values. This will allow the prediction of log k IAM based on chemical structure alone. The use of structural fragment values to predict log P was first proposed in the 1970s. The application of a similar method using fragment values to predict log k IAM is a novel approach. Values of log k IAM were determined for 22 aliphatic and 42 aromatic compounds using an optimised and robust IAM-HPLC assay. The method developed shows good predictive performance using leave-one-out cross validation and application to an external validation set not seen a priori by the training set also generated good predictive values. The ability to predict log k IAM without the need for practical measurement will allow for the increased use of QSARs based on this descriptor.

Assessing toxicological data quality: basic principles, existing schemes and current limitations.

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.

Database of published retention factors for immobilized artificial membrane HPLC and an assessment of the effect of experimental variability.

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.

Assessment of methods to define the applicability domain of structural alert models.

It is important that in silico models for use in chemical safety legislation, such as REACH, are compliant with the OECD Principles for the Validation of (Q)SARs. Structural alert models can be useful under these circumstances but lack an adequately defined applicability domain. This paper examines several methods of domain definition for structural alert models with the aim of assessing which were the most useful. Specifically, these methods were the use of fragments, chemical descriptor ranges, structural similarity, and specific applicability domain definition software. Structural alerts for mutagenicity in Derek for Windows (DfW) were used as examples, and Ames test data were used to define and test the domain of chemical space where the alerts produce reliable results. The usefulness of each domain was assessed on the criterion that confidence in the correctness of predictions should be greater inside the domain than outside it. By using a combination of structural similarity and chemical fragments a domain was produced where the majority of correct positive predictions for mutagenicity were within the domain and a large proportion of the incorrect positive predictions outside it. However this was not found for the negative predictions; there was little difference between the percentage of true and false predictions for inactivity which were found as either within or outside the applicability domain. A hypothesis for the occurrence of this difference between positive and negative predictions is that differences in structure between training and test compounds are more likely to remove the toxic potential of a compound containing a structural alert than to add an unknown mechanism of action (structural alert) to a molecule which does not already contain an alert. This could be especially true for well studied end points such as the Ames assay where the majority of mechanisms of action are likely to be known.

Examination of Michael addition reactivity towards glutathione by transition-state calculations.

Kinetic rate constants (k(GSH)) 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, ß-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 k(GSH) were found to be in good agreement with experimental k(GSH) 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.

Integrating (Q)SAR models, expert systems and read-across approaches for the prediction of developmental toxicity.

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.

In silico prediction of aqueous solubility: the solubility challenge.

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.

Definition of the structural domain of the baseline non-polar narcosis model for Tetrahymena pyriformis.

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 IGC(50)(-1)) 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 IGC(50)(-1) = 0.78 log P-2.01 (n = 87, r(2) = 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.

Identification of mechanisms of toxic action for skin sensitisation using a SMARTS pattern based approach.

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.

Classification of chemicals according to mechanism of aquatic toxicity: an evaluation of the implementation of the Verhaar scheme in Toxtree.

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.

An evaluation of global QSAR models for the prediction of the toxicity of phenols to Tetrahymena pyriformis.

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.