QSAR modeling of the adult and developmental toxicity of glycols, glycol ethers and xylenes to Hydra attenuata

Structure-activity relationship study of trifluoromethylketones: inhibitors of insect juvenile hormone esterase

The juvenile hormone esterase (JHE) regulates juvenile hormone titre in insect hemolymph during its larval development. It has been suggested that JHE could be targeted for use in insect control. This enzyme can also be considered as involved in the phenomenon of endocrine disruption by xenobiotics in beneficial insects. Consequently, there is a need to know the characteristics of the molecules able to act on the JHE. Trifluoromethylketones (TFKs) are the most potent JHE inhibitors found to date and different quantitative structure-activity relationships (QSARs) have been derived for this group of chemicals. In this context, a set of 181 TFKs (118 active and 63 inactive compounds), tested on Trichoplusia ni for their JHE inhibition activity and described by physico-chemical descriptors, was split into different training and test sets to derive structure-activity relationship (SAR) models from support vector classification (SVC). A SVC model including 88 descriptors and derived from a Gaussian kernel was selected for its predictive performances. Another model computed only with 13 descriptors was also selected due to its mechanistic interpretability. This study clearly illustrates the difficulty in capturing the essential structural characteristics of the TFKs explaining their JHE inhibitory activity.

Developmental toxicity prediction

Developmental toxicity may be estimated using commercial and noncommercial software that is already available in the market and/or literature, or models may be built from scratch using both commercial and noncommercial software packages. In this chapter, commonly available software programs that can predict the developmental toxicity of chemicals are described. In addition, a method for developing qualitative structure-activity relationship (SAR) models to predict the developmental toxicity of chemicals qualitatively (yes/no prediction) and quantitative structure-activity relationship (QSAR) models to predict quantitative estimates (e.g., LOAEL) of developmental toxicants is also described in this chapter. Additional information described in this chapter include methods to predict physicochemical properties of chemicals that can be used as descriptor variables in the model building process, statistical methods that be used to build QSAR models as well as methods to validate the models that are developed. Most of the methods described in this chapter can be used to develop models for health endpoints other than developmental toxicity as well.

Methods for building QSARs

Structure-activity relationship (SAR) and quantitative structure-activity relationship (QSAR) models are increasingly used in toxicology, ecotoxicology, and pharmacology for predicting the activity of the molecules from their physicochemical properties and/or their structural characteristics. However, the design of such models has many traps for unwary practitioners. Consequently, the purpose of this chapter is to give a practical guide for the computation of SAR and QSAR models, point out problems that may be encountered, and suggest ways of solving them. Attempts are also made to see how these models can be validated and interpreted.

Integrated Decision-tree Testing Strategies for Developmental and Reproductive Toxicity with Respect to the Requirements of the EU REACH Legislation

Liverpool John Moores University and FRAME conducted a research project, sponsored by Defra, on the status of alternatives to animal testing with regard to the European Union REACH (Registration, Evaluation and Authorisation of Chemicals) system for the safety testing and risk assessment of chemicals. The project covered all the main toxicity endpoints associated with the REACH system. This paper focuses on the prospects for the use of alternative methods (both in vitro and in silico) in developmental and reproductive toxicity testing. It considers many tests based on primary cells and cell lines, and the available expert systems and QSARs for developmental and reproductive toxicity, and also covers tests for endocrine disruption. Ways in which reduction and refinement measures can be used are also discussed, particularly the use of an enhanced one-generation reproductive study, which could potentially replace the two-generation study, and therefore considerably reduce the number of animals required in reproductive toxicity. Decision-tree style integrated testing strategies are also proposed for developmental and reproductive toxicity and for endocrine disruption, followed by a number of recommendations for the future facilitation of developmental and reproductive toxicity testing, with respect to human risk assessment.

Integrated Decision-tree Testing Strategies for Developmental and Reproductive Toxicity with Respect to the Requirements of the EU REACH Legislation

Liverpool John Moores University and FRAME conducted a research project, sponsored by Defra, on the status of alternatives to animal testing with regard to the European Union REACH (Registration, Evaluation and Authorisation of Chemicals) system for the safety testing and risk assessment of chemicals. The project covered all the main toxicity endpoints associated with the REACH system. This paper focuses on the prospects for the use of alternative methods (both in vitro and in silico) in developmental and reproductive toxicity testing. It considers many tests based on primary cells and cell lines, and the available expert systems and QSARs for developmental and reproductive toxicity, and also covers tests for endocrine disruption. Ways in which reduction and refinement measures can be used are also discussed, particularly the use of an enhanced one-generation reproductive study, which could potentially replace the two-generation study, and therefore considerably reduce the number of animals required in reproductive toxicity. Decision-tree style integrated testing strategies are also proposed for developmental and reproductive toxicity and for endocrine disruption, followed by a number of recommendations for the future facilitation of developmental and reproductive toxicity testing, with respect to human risk assessment.

A new strategy for using supervised artificial neural networks in QSAR

A new type of environmental QSAR model is presented for the common situation in which the biological activity of molecules mainly depends on their 1-octanol/water partition coefficient (log P). In a first step, a classical regression equation with log P is derived. The residuals obtained with this simple linear equation are then modeled from a supervised artificial neural network including different molecular descriptors as input neurons. Finally, results produced by the linear and nonlinear models are both considered for calculating the activity values, which are compared with the initial actual activity values. A heterogeneous database of 569 organic compounds with 96-h LC50s measured to the fathead minnow (Pimephales promelas), randomly divided into a training set of 484 chemicals and a testing set of 85 chemicals, was used as illustrative example to show the potentialities of this new modeling strategy Finally, practical suggestions are given for designing this type of hybrid QSAR model.

Linear versus nonlinear QSAR modeling of the toxicity of phenol derivatives to Tetrahymena pyriformis

Quantitative structure-activity relationship (QSAR) models were derived from a structurally heterogeneous set of 200 phenol derivatives for which the 50% growth inhibition concentration (IGC(50)) values to the ciliated protozoan Tetrahymena pyriformis were available. Each molecule was described by means of physicochemical descriptors and structural features. Partial least squares (PLS) regression analysis and a three-layer perceptron were used as statistical engine. The performances of the linear and nonlinear models were estimated from an external testing set of 50 chemicals. Despite hard constraints voluntarily imposed in the design of the neural network models, they provided better simulation results than the PLS models.

The utility of structure-activity relationship (SAR) models for prediction and covariate selection in developmental toxicity: comparative analysis of logistic regression and decision tree models

Structure-activity relationship (SAR) models can be used to predict the biological activity of potential developmental toxicants whose adverse effects include death, structural abnormalities, altered growth and functional deficiencies in the developing organism. Physico-chemical descriptors of spatial, electronic and lipophilic properties were used to derive SAR models by two modeling approaches, logistic regression and Classification and Regression Tree (CART), using a new developmental database of 293 chemicals (FDA/TERIS). Both single models and ensembles of models (termed bagging) were derived to predict toxicity. Assessment of the empirical distributions of the prediction measures was performed by repeated random partitioning of the data set. Results showed that both the decision tree and logistic regression derived developmental SAR models exhibited modest prediction accuracy. Bagging tended to enhance the prediction accuracy and reduced the variability of prediction measures compared to the single model for CART-based models but not consistently for logistic-based models. Prediction accuracy of single logistic-based models was higher than single CART-based models but bagged CART-based models were more predictive. Descriptor selection in SAR for the understanding of the developmental mechanism was highly dependent on the modeling approach. Although prediction accuracy was similar in the two modeling approaches, there was inconsistency in the model descriptors.

PLS-QSAR of the adult and developmental toxicity of chemicals to Hydra attenuata

Autocorrelation descriptors encoding lipophilicity, molar refractivity, the H-bonding acceptor and donor ability of the molecules and also indicator variables were used to describe 30 organic chemicals characterized by their adult and developmental toxicities to Hydra attenuata. A PLS regression analysis was successfully employed to derive a QSAR model allowing the simulation of both endpoints. Comparisons were made with orthogonal regression analysis and different nonlinear regression analyses.