Evaluation of SARs for the prediction of eye irritation/corrosion potential: structural inclusion rules in the BfR decision support system.

The proposed REACH regulation within the European Union (EU) aims to minimise the number of laboratory animals used for human hazard and risk assessment while ensuring adequate protection of human health and the environment. One way to achieve this goal is to develop non-testing methods, such as (quantitative) structure-activity relationships ([Q]SARs), suitable for identifying toxicological hazard from chemical structure and physicochemical properties alone. A database containing data submitted within the EU New Chemicals Notification procedure was compiled by the German Bundesinstitut für Risikobewertung (BfR). On the basis of these data, the BfR built a decision support system (DSS) for the prediction of several toxicological endpoints. For the prediction of eye irritation and corrosion potential, the DSS contains 31 physicochemical exclusion rules evaluated previously by the European Chemicals Bureau (ECB), and 27 inclusion rules that define structural alerts potentially responsible for eye irritation and/or corrosion. This work summarises the results of a study carried out by the ECB to assess the performance of the BfR structural rulebase. The assessment included: (a) evaluation of the structural alerts by using the training set of 1341 substances with experimental data for eye irritation and corrosion; and (b) external validation by using an independent test set of 199 chemicals. Recommendations are made for the further development of the structural rules in order to increase the overall predictivity of the DSS.

Evaluation of SARs for the prediction of skin irritation/corrosion potential: structural inclusion rules in the BfR decision support system.

The German Federal Institute for Risk Assessment (BfR) has developed a Decision Support System (DSS) to assess certain hazardous properties of pure chemicals, including skin and eye irritation/corrosion. The BfR-DSS is a rule-based system that could be used for the regulatory classification of chemicals in the European Union. The system is based on the combined use of two predictive approaches: exclusion rules based on physicochemical cut-off values to identify chemicals that do not exhibit a certain hazard (e.g., skin irritation/corrosion), and inclusion rules based on structural alerts to identify chemicals that do show a particular toxic potential. The aim of the present study was to evaluate the structural inclusion rules implemented in the BfR-DSS for the prediction of skin irritation and corrosion. The following assessments were performed: (a) a confirmation of the structural rules by rederiving them from the original training set (1358 substances), and (b) an external validation by using a test set of 200 chemicals not used in the derivation of the rules. It was found as a result that the test data set did not match the training set relative to the inclusion of structural alerts associated with skin irritation/corrosion, albeit some skin irritants were in the test set.

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 Commission's 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.

Phenothiazines and structurally related compounds as modulators of cancer multidrug resistance.

Phenothiazines and structurally related compounds alongside their other biological activities are able to modulate multidrug resistance (MDR) in tumor cells. The extensive investigations on their MDR modulation effects consist part of the efforts to overcome MDR - the major obstacle in cancer chemotherapy. In this article we try to systematize the results collected in the last two decades in two main aspects. The first one comprises the mechanism of modulation by phenothiazine-type MDR modulators. Two main possible mechanisms of MDR reversal are reviewed: (i) direct interaction with Pgp; (ii) interactions with membrane phospholipids. The second aspect relates to the structural properties of phenothiazines and related compounds responsible for their MDR reversing effect. The structural alerts and physicochemical properties influencing anti-MDR activity are considered as identified by structure--activity (SAR) or quantitative structure--activity relationship (QSAR) studies. Results discussed in the article point to MDR modulation by phenothiazines and related compounds as a complex process in which more than one mechanism are certainly involved. Further investigations in this direction should contribute to elucidation of the possible mechanisms of MDR modulation by these compounds. On the basis of the studies discussed the potential use of phenothiazine-type MDR modulators as a model system in the further investigations of the MDR phenomenon is outlined.

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.

Molecular modeling of triazine type MDR modulators using CoMFA and CoMSIA approaches.

In the present study a series of 30 triazine derivatives was investigated by 3D QSAR methods with respect to their MDR reversing activity in vitro. Two approaches were applied and compared: comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Molecular models with good predictive power were derived using steric, electrostatic and hydrophobic fields of the compounds. The results indicated the dominant role of the electrostatic and hydrophobic fields for MDR reversing activity of the investigated modulators. The obtained statistical parameters (Qcv2, Qpr2) showed that the CoMFA and CoMSIA models have similar predictivity. The CoMSIA models were slightly better than the CoMFA ones and obtained with lower number of principal components. The models were graphically interpreted using CoMFA and CoMSIA contour plots. The structural regions responsible for the differences in anti-MDR activity were analyzed in respect to their electrostatic and hydrophobic nature. An easier interpretation of the CoMSIA contour plots was noticed.