Steven J. Enoch

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

A review of the electrophilic reaction chemistry involved in covalent protein binding relevant to toxicity.

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.

A review of the electrophilic reaction chemistry involved in covalent DNA binding

The need to assess the ability of a chemical to act as a mutagen or a genotoxic carcinogen (collectively termed genotoxicity) is one of the primary requirements in regulatory toxicology. 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. A key step in the development of chemical categories for genotoxicity is defining the organic chemistry associated with the formation of a covalent bond between DNA and an exogenous chemical. This organic chemistry is typically defined as structural alerts. To this end, this article has reviewed the literature defining the structural alerts associated with covalent DNA binding. Importantly, this review article also details the mechanistic organic chemistry associated with each of the structural alerts. This information is extremely important in terms of meeting regulatory requirements for the acceptance of the chemical category approach. The structural alerts and associated mechanistic chemistry have been incorporated into the Organisation for Economic Co-operation and Development (OECD) (Q)SAR Application Toolbox.

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.

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.

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.

Mechanistic Category Formation for the Prediction of Respiratory Sensitization

This study investigates the ability of a set of previously published rules for protein binding, developed from skin sensitization data, to group chemicals into mechanistic domains and to develop knowledge on the chemical interactions relating to respiratory sensitization. The results of the analysis showed that 32 of 39 respiratory sensitizing chemicals could be assigned to a mechanistic domain based on the published rules. Analysis of the remaining six chemicals showed them to have electrophilic mechanisms that had not been explicitly covered by the skin sensitization protein binding rules. The study also highlighted the ability to develop subdomains within the mechanistic domains on the basis of simple chemistry principles. These subdomains were developed to allow a calculated electrophilic index to rationalize the differing respiratory sensitizing potentials of the chemicals assigned to them. The study clearly highlights how assigning the most likely mechanism of action for a chemical is useful in building chemical categories (domains) and subcategories (subdomains).

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.