Tatiana I. Netzeva

The Role of the European Chemicals Bureau in Promoting the Regulatory Use of (Q)SAR Methods

Under the proposed REACH (Registration, Evaluation and Authorisation of CHemicals) legislation, (Q)SAR models and grouping methods (chemical categories and read across approaches) are expected to play a significant role in prioritising industrial chemicals for further assessment, and for filling information gaps for the purposes of classification and labelling, risk assessment and the assessment of persistent, bioaccumulative and toxic (PBT) chemicals. The European Chemicals Bureau (ECB), which is part of the European Commissions Joint Research Centre (JRC), has a well-established role in providing independent scientific and technical advice to European policy makers. The ECB also promotes consensus and capacity building on scientific and technical matters among stakeholders in the Member State authorities and industry. To promote the availability and use of (Q)SARs and related estimation methods, the ECB is carrying out a range of activities, including applied research in computational toxicology, the assessment of (Q)SAR models and methods, the development of technical guidance documents and computational tools, and the organisation of training courses. This article provides an overview of ECB activities on computational toxicology, which are intended to promote the development, validation, acceptance and use of (Q)SARs and related estimation methods, both at the European and international levels. †Presented at the 12th International Workshop on Quantitative Structure-Activity Relationships in Environmental Toxicology (QSAR2006), 8–12 May 2006, Lyon, France.

Multivariate Discrimination between Modes of Toxic Action of Phenols

A set of 221 phenols, for which toxicity data to the ciliate Tetrahymena pyriformis were available, was subjected to stepwise linear discriminant analysis (LDA) in order to classify their toxic mechanisms of action. The compounds were a priori grouped into the following four mechanisms according to structural rules: polar narcotics, weak acid respiratory uncouplers, pro-electrophiles and soft electrophiles. Hydrophobicity with and without correction for ionisation (log K o w , log D o w u), acidity constant (pK a ), frontier orbital energies (E L U M O , E H O M O ) and hydrogenbond donor and acceptor counts were used as molecular descriptors. LDA models employing 3-6 variables achieved 86-89% overall correct classification of the four mechanisms, with more varied performance for respiratory uncouplers and pro-electrophiles. For the latter, a separate model was developed that discriminated compounds undergoing metabolic activation from compounds with different mechanisms very accurately. Model validation was performed by evaluating the simulated external prediction through LDA models built from complementary subsets.

The present status of QSAR in toxicology

Abstract The current status of the use of Quantitative Structure–Activity Relationships (QSARs) in toxicology, both environmental (i.e. ecotoxicology) and human health effects, are described with a particular emphasis on the science since 1995. Discussions of ecotoxicity QSARs focus on recent information that relates to separation of effects based on modes of toxic action. Particular attention is given to the response-surface approach to modeling toxic potency of baseline and non-specific soft electrophiles (i.e. the majority of industrial organic chemicals) and the development of rules-based expert systems to aid in the selection of the most appropriate QSAR. In addition the more recent application self-organizing dynamical algorithms such as artificial neural networks to ecotoxicity data is described. Recent QSAR modeling of estrogenicity, an example of receptor-mediated effects, are described with particular emphasis on 2D structural alerts as screening tools and QSARs developed with data for the recombinant yeast assay. In addition the current status of modeling human health effects include mutagenesis and carcinogenesis, developmental toxicity, skin sensitization, and skin and eye irritation is described.

Toward a class-independent quantitative structure−activity relationship model for uncouplers of oxidative phosphorylation

A mechanistically based quantitative structure-activity relationship (QSAR) for the uncoupling activity of weak organic acids has been derived. The analysis of earlier experimental studies suggested that the limiting step in the uncoupling process is the rate with which anions can cross the membrane and that this rate is determined by the height of the energy barrier encountered in the hydrophobic membrane core. We use this mechanistic understanding to develop a predictive model for uncoupling. The translocation rate constants of anions correlate well with the free energy difference between the energy well and the energy barrier, Delta G well-barrier,A (-) , in the membrane calculated by a novel approach to describe internal partitioning in the membrane. An existing data set of 21 phenols measured in an in vitro test system specific for uncouplers was extended by 14 highly diverse compounds. A simple regression model based on the experimental membrane-water partition coefficient and Delta G well-barrier,A (-) showed good predictive power and had meaningful regression coefficients. To establish uncoupler QSARs independent of chemical class, it is necessary to calculate the descriptors for the charged species, as the analogous descriptors of the neutral species showed almost no correlation with the translocation rate constants of anions. The substitution of experimental with calculated partition coefficients resulted in a decrease of the model fit. A particular strength of the current model is the accurate calculation of excess toxicity, which makes it a suitable tool for database screening. The applicability domain, limitations of the model, and ideas for future research are critically discussed.

Validation of counter propagation neural network models for predictive toxicology according to the OECD principles: a case study

The OECD has proposed five principles for validation of QSAR models used for regulatory purposes. Here we present a case study investigating how these principles can be applied to models based on Kohonen and counter propagation neural networks. The study is based on a counter propagation network model that has been built using toxicity data in fish fathead minnow for 541 compounds. The study demonstrates that most, if not all, of the OECD criteria may be met when modeling using this neural network approach.

QSAR analysis of the toxicity of aromatic compounds to Chlorella vulgaris in a novel short-term assay

The use of alternative toxicity tests and computational prediction models is widely accepted to fill experimental data gaps and to prioritize chemicals for more expensive and time-consuming assessment. A novel short-term toxicity test using the alga Chlorella vulgaris was utilized in this study to produce acute aquatic toxicity data for 65 aromatic compounds. The compounds tested included phenols, anilines, nitrobenzenes, benzaldehydes and other poly-substituted benzenes. The toxicity data were employed in the development of quantitative structure-activity relationships (QSARs). Using multiple regression (MLR) and partial least squares (PLS) analyses, statistically significant, transparent and interpretable QSARs were developed using a small number of physicochemical descriptors. A two-descriptor model was developed using MLR (log(1/EC50)=0.73 log Kow-0.59 Elumo-1.91; n=65, r2=0.84, r2CV=0.82, s=0.43) and a four-descriptor model using PLS (log(1/EC50)=0.40 log Kow-0.23 Elumo+9.84 Amax+0.20 0chiv-5.40; n=65, r2=0.86, q2=0.84, RMSEE=0.40). The latter model was obtained by stepwise elimination of variables from a set of 102 calculated descriptors. Both models were validated successfully by simulating external prediction through the use of complementary subsets. The two factors, which were identified as being critical for the acute algal toxicity of this set of compounds were hydrophobicity and electrophilicity.

Validation of a QSAR model for acute toxicity

In the present study, a quantitative structure – activity relationship (QSAR) model has been developed for predicting acute toxicity to the fathead minnow (Pimephales promelas), the aim being to demonstrate how statistical validation and domain definition are both required to establish model validity and to provide reliable predictions. A dataset of 408 heterogeneous chemicals was modelled by a diverse set of theoretical molecular descriptors by using multivariate linear regression (MLR) and Genetic Algorithm – Variable Subset Selection (GA-VSS). This QSAR model was developed to generate reliable predictions of toxicity for organic chemicals not yet tested, so particular emphasis was given to statistical validity and applicability domain. External validation was performed by using OECD Screening Information Data Set (SIDS) data for 177 High Production Volume (HPV) chemicals, and a good predictivity was obtained (  = 72.1). The model was evaluated according to the OECD principles for QSAR validation, and compliance with all five principles was established. The model could therefore be useful for the regulatory assessment of chemicals. For example, it could be used to fill data gaps within its chemical domain and contribute to the prioritization of chemicals for aquatic toxicity testing. †Presented at CMTPI 2005: Computational Methods in Toxicology and Pharmacology Integrating Internet resources (Shanghai, China, October 29–November 1 2005).

Prediction of estrogenicity: validation of a classification model.

(Q)SAR models can be used to reduce animal testing as well as to minimise the testing costs. In particular, classification models have been widely used for estimating endpoints with binary activity. The aim of the present study was to develop and validate a classification-based quantitative structure-activity relationship (QSAR) model for endocrine disruption, based on interpretable mechanistic descriptors related to estrogenic gene activation. The model predicts the presence or absence of estrogenic activity according to a pre-defined cut-off in activity as determined in a recombinant yeast assay. The experimental data was obtained from the literature. A two-descriptor classification model was developed that has the form of a decision tree. The predictivity of the model was evaluated by using an external test set and by taking into account the limitations associated with the applicability domain (AD) of the model. The AD was determined as coverage of the model descriptor space. After removing the compounds present in the training set and the compounds outside of the AD, the overall accuracy of classification of the test chemicals was used to assess the predictivity of the model. In addition, the model was shown to meet the OECD Principles for (Q)SAR Validation, making it potentially useful for regulatory purposes. †Presented at CMTPI 2005: Computational Methods in Toxicology and Pharmacology Integrating Internet resources (Shanghai, China, October 29–November 1 2005).

Comparison of the applicability domain of a quantitative structure-activity relationship for estrogenicity with a large chemical inventory.

The aim of the present study was to illustrate that it is possible and relatively straightforward to compare the domain of applicability of a quantitative structure-activity relationship (QSAR) model in terms of its physicochemical descriptors with a large inventory of chemicals. A training set of 105 chemicals with data for relative estrogenic gene activation, obtained in a recombinant yeast assay, was used to develop the QSAR. A binary classification model for predicting active versus inactive chemicals was developed using classification tree analysis and two descriptors with a clear physicochemical meaning (octanol-water partition coefficient, or log Kow, and the number of hydrogen bond donors, or n(Hdon)). The model demonstrated a high overall accuracy (90.5%), with a sensitivity of 95.9% and a specificity of 78.1%. The robustness of the model was evaluated using the leave-many-out cross-validation technique, whereas the predictivity was assessed using an artificial external test set composed of 12 compounds. The domain of the QSAR training set was compared with the chemical space covered by the European Inventory of Existing Commercial Chemical Substances (EINECS), as incorporated in the CDB-EC software, in the log Kow / n(Hdon) plane. The results showed that the training set and, therefore, the applicability domain of the QSAR model covers a small part of the physicochemical domain of the inventory, even though a simple method for defining the applicability domain (ranges in the descriptor space) was used. However, a large number of compounds are located within the narrow descriptor window.

Skin sensitisation – Moving forward with non-animal testing strategies for regulatory purposes in the EU

In a previous EPAA-Cefic LRI workshop in 2011, issues surrounding the use and interpretation of results from the local lymph node assay were addressed. At the beginning of 2013 a second joint workshop focused greater attention on the opportunities to make use of non-animal test data, not least since a number of in vitro assays have progressed to an advanced position in terms of their formal validation. It is already recognised that information produced from non-animal assays can be used in regulatory decision-making, notably in terms of classifying a substance as a skin sensitiser. The evolution into a full replacement for hazard identification, where the decision is not to classify, requires the generation of confidence in the in vitro alternative, e.g. via formal validation, the existence of peer reviewed publications and the knowledge that the assay(s) are founded on key elements of the Adverse Outcome Pathway for skin sensitisation. It is foreseen that the validated in vitro assays and relevant QSAR models can be organised into formal testing strategies to be applied for regulatory purposes by the industry. To facilitate progress, the European Partnership for Alternative Approaches to animal testing (EPAA) provided the platform for cross-industry and regulatory dialogue, enabling an essential and open debate on the acceptability of an in vitro based integrated strategy. Based on these considerations, a follow up activity was agreed upon to explore an example of an Integrated Testing Strategy for skin sensitisation hazard identification purposes in the context of REACH submissions.