Judith C. Madden

Measurement and Estimation of Electrophilic Reactivity for Predictive Toxicology

Measurement and Estimation of Electrophilic Reactivity for Predictive Toxicology Johannes A. H. Schw€obel, Yana K. Koleva, Steven J. Enoch, Fania Bajot,MarkHewitt, Judith C.Madden, David W. Roberts, Terry W. Schultz, and Mark T. D. Cronin* School of Pharmacy and Chemistry, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, England College of Veterinary Medicine, Department of Comparative Medicine, The University of Tennessee, 2407 River Drive, Knoxville, Tennessee 37996-4543, United States

Prediction of Michael-Type Acceptor Reactivity toward Glutathione

A model has been developed to predict the kinetic rate constants (k(GSH)) of α,β-unsaturated Michael acceptor compounds for their reaction with glutathione (GSH). The model uses the local charge-limited electrophilicity index ω(q) [Wondrousch, D., et al. (2010) J. Phys. Chem. Lett. 1, 1605-1610] at the β-carbon atom as a descriptor of reactivity, a descriptor for resonance stabilization of the transition state, and one for steric hindrance at the reaction sites involved. Overall, the Michael addition model performs well (r² = 0.91; rms = 0.34). It includes various classes of compounds with double and triple bonds, linear and cyclic systems, and compounds with and without substituents in the α-position. Comparison of experimental and predicted rate constants demonstrates even better performance of the model for individual classes of compounds (e.g., for aldehydes, r² = 0.97 and rms = 0.15; for ketones, r² = 0.95 and rms = 0.35). The model also allows for the prediction of the RC₅₀ values from the Schultz chemoassay, the accuracy being close to the interlaboratory experimental error. Furthermore, k(GSH) and associated RC₅₀ values can be predicted in cases where experimental measurements are not possible or restricted, for example, because of low solubility or high volatility. The model has the potential to provide information to assist in the assessment and categorization of toxicants and in the application of integrated testing strategies.

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 chemicals 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 chemicals 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.

Pharmaceuticals in the environment: good practice in predicting acute ecotoxicological effects.

Improvements in analytical techniques have led to an increased awareness of the presence of pharmaceuticals in the environment. Concern is now raised as to the potential adverse effects these compounds may have on non-target organisms, particularly under conditions of chronic exposure. There is a paucity of experimental ecotoxicity data available for pharmaceuticals, hence the use of in silico tools to predict toxicity is a pragmatic option. Previous studies have used the ECOSAR program to predict environmental toxicity of pharmaceuticals, however, these models were developed using industrial chemicals and the applicability of the models to predict effects of pharmaceuticals should be carefully considered. In this study ECOSAR was used to assign 364 diverse pharmaceuticals to recognised chemical classes and hence predict their aquatic toxicity. Confidence in the predictions was assessed in terms of whether the assigned class was realistically representative of the pharmaceutical in question. The correlation between experimentally determined toxicity values (where these were available) and those predicted by ECOSAR was investigated in terms of confidence in the prediction. ECOSAR was shown to make reasonable predictions for certain pharmaceuticals considered to be within the applicability domain of the models, but predictions were less reliable for compounds judged to fall outwith the domain of the models. This study is not critical of ECOSAR or the class based approach to predicting toxicity, but demonstrates the importance of using expert judgement to ascertain whether or not use of a particular model is appropriate when the specific chemistry of a query compound is considered.

Assessing the exposure risk and impacts of pharmaceuticals in the environment on individuals and ecosystems

The use of human and veterinary pharmaceuticals is increasing. Over the past decade, there has been a proliferation of research into potential environmental impacts of pharmaceuticals in the environment. A Royal Society-supported seminar brought together experts from diverse scientific fields to discuss the risks posed by pharmaceuticals to wildlife. Recent analytical advances have revealed that pharmaceuticals are entering habitats via water, sewage, manure and animal carcases, and dispersing through food chains. Pharmaceuticals are designed to alter physiology at low doses and so can be particularly potent contaminants. The near extinction of Asian vultures following exposure to diclofenac is the key example where exposure to a pharmaceutical caused a population-level impact on non-target wildlife. However, more subtle changes to behaviour and physiology are rarely studied and poorly understood. Grand challenges for the future include developing more realistic exposure assessments for wildlife, assessing the impacts of mixtures of pharmaceuticals in combination with other environmental stressors and estimating the risks from pharmaceutical manufacturing and usage in developing countries. We concluded that an integration of diverse approaches is required to predict ‘unexpected’ risks; specifically, ecologically relevant, often long-term and non-lethal, consequences of pharmaceuticals in the environment for wildlife and ecosystems.

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.

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.

Structure-based modelling in reproductive toxicology: (Q)SARs for the placental barrier†

The replacement of animal testing for endpoints such as reproductive toxicity is a long-term goal. This study describes the possibilities of using simple (quantitative) structure-activity relationships ((Q)SARs) to predict whether a molecule may cross the placental membrane. The concept is straightforward, if a molecule is not able to cross the placental barrier, then it will not be a reproductive toxicant. Such a model could be placed at the start of any integrated testing strategy. To develop these models the literature was reviewed to obtain data relating to the transfer of molecules across the placenta. A reasonable number of data were obtained and are suitable for the modelling of the ability of a molecule to cross the placenta. Clearance or transfer indices data were sought due to their ability to eliminate inter-placental variation by standardising drug clearance to the reference compound antipyrine. Modelling of the permeability data indicates that (Q)SARs with reasonable statistical fit can be developed for the ability of molecules to cross the placental barrier membrane. Analysis of the models indicates that molecular size, hydrophobicity and hydrogen-bonding ability are molecular properties that may govern the ability of a molecule to cross the placental barrier. †Presented at the 12th International Workshop on Quantitative Structure-Activity Relationships in Environmental Toxicology (QSAR2006), 8–12 May 2006, Lyon, France.

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.

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.