Mark Hewitt

2-Heteroarylimino-5-benzylidene-4-thiazolidinones analogues of 2-thiazolylimino-5-benzylidene-4-thiazolidinones with antimicrobial activity: synthesis and structure-activity relationship.

2-Heteroarylimino-5-benzylidene-4-thiazolidinones, unsubstituted or carrying hydroxy, methoxy, nitro and chloro groups on the benzene ring, were synthesised and assayed in vitro for their antimicrobial activity against gram positive and gram negative bacteria, yeasts and mould. The antimicrobial activity of the 2-benzo[d]thiazolyl- and of the 2-benzo[d]isothiazolyl-imino-5-benzylidene-4-thiazolidinones is, on the whole, lower in comparison with the high activity detected for the derivatives of the 2-thiazolylimino-5-benzylidene-4-thiazolidinone class. Nevertheless most of the benzo[d]thiazole analogues display good inhibition of the growth of gram positive bacilli and staphylococci, including methicillin-resistant Staphylococcus strains. Among the 2-benzo[d]isothiazole analogues a few derivatives show a strong and selective activity against bacilli. Moreover, it is worth noting that the replacement of the thiazole nucleus for the benzo[d]thiazole bicyclic system in the parent 2-(benzo[d]thiazol-2-ylimino)thiazolidin-4-one leads to significant antifungal properties against both yeasts and moulds, properties not shown by the analogous 2-thiazolyl- and 2-benzo[d]isothiazolyl-imino)thiazolidin-4-ones. The structure-activity relationship of 33 analogues possessing the 2-heteroarylimino-4-thiazolidinone structure is analysed through QSAR models.

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

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.

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.

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

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.

Evaluation of the local anaesthetic activity of 3-aminobenzo[d]isothiazole derivatives using the rat sciatic nerve model

On the basis of computer prediction of biological activity by PASS and toxicity by DEREK, the most promising 32-alkylaminoacyl derivatives of 3-aminobenzo[d]isothiazole were selected for possible local anaesthetic action. This action was evaluated using an in vitro preparation of the isolated sciatic nerve of the rat and compared with lidocaine which was used as a reference compound. QSAR studies showed that the polarizability, polarity and molecular shape of molecules have a positive influence on their local anaesthetic activity, while contributions of aromatic CH and singly bonded nitrogen are negative. Since the estimated PASS probabilities to find local anaesthetic activity in the most active compounds are less than 50%, these compounds may be considered to be possible NCEs.

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.