Regulatory use of (Q)SARs in toxicological hazard assessment strategies

In 2001, the European Commission published a policy statement ("White Paper") on future chemicals regulation and risk reduction that proposed the use of non-animal test systems and tailor-made testing approaches, including (Q)SARs, to reduce financial costs and the number of test animals employed. The authors have compiled a database containing data submitted within the EU chemicals notification procedure. From these data, (Q)SARs for the prediction of local irritation/corrosion and/or sensitisation potential were developed and published. These (Q)SARs, together with an expert system supporting their use, will be submitted for official validation and application within regulatory hazard assessment strategies. The main features are: two sets of structural alerts for the prediction of skin sensitisation hazard classification as defined by the European risk phrase R43, comprising 15 rules for chemical substructures deemed to be sensitising by direct action with cells or proteins, and three rules for substructures acting indirectly, i.e., requiring biochemical transformation; a decision support system (DSS) for the prediction of skin and/or eye lesion potential built from information extracted from our database. This DSS combines SARs defining reactive chemical substructures relevant for local lesions to be classified, and QSARs for the prediction of the absence of such a potential. The role of the BfR database, and (Q)SARs derived from it, in the use of current and future (EU) testing strategies for irritation and sensitisation is discussed.     

An evaluation of selected (Q)SARs/expert systems for predicting skin sensitisation potential

Predictive testing to characterise substances for their skin sensitisation potential has historically been based on animal models such as the Local Lymph Node Assay (LLNA) and the Guinea Pig Maximisation Test (GPMT). In recent years, EU regulations, have provided a strong incentive to develop non-animal alternatives, such as expert systems software. Here we selected three different types of expert systems: VEGA (statistical), Derek Nexus (knowledge-based) and TIMES-SS (hybrid), and evaluated their performance using two large sets of animal data: one set of 1249 substances from eChemportal and a second set of 515 substances from NICEATM. A model was considered successful at predicting skin sensitisation potential if it had at least the same balanced accuracy as the LLNA and the GPMT had in predicting the other outcomes, which ranged from 79% to 86%. We found that the highest balanced accuracy of any of the expert systems evaluated was 65% when making global predictions. For substances within the domain of TIMES-SS, however, balanced accuracies for the two datasets were found to be 79% and 82%. In those cases where a chemical was within the TIMES-SS domain, the TIMES-SS skin sensitisation hazard prediction had the same confidence as the result from LLNA or GPMT.     

Validity and validation of expert (Q)SAR systems

At a recent workshop in Setubal (Portugal) principles were drafted to assess the suitability of (quantitative) structure–activity relationships ((Q)SARs) for assessing the hazards and risks of chemicals. In the present study we applied some of the Setubal principles to test the validity of three (Q)SAR expert systems and validate the results. These principles include a mechanistic basis, the availability of a training set and validation. ECOSAR, BIOWIN and DEREK for Windows have a mechanistic or empirical basis. ECOSAR has a training set for each QSAR. For half of the structural fragments the number of chemicals in the training set is >4. Based on structural fragments and log Kow, ECOSAR uses linear regression to predict ecotoxicity. Validating ECOSAR for three ‘valid’ classes results in predictivity of ?≥?64%. BIOWIN uses (non-)linear regressions to predict the probability of biodegradability based on fragments and molecular weight. It has a large training set and predicts non-ready biodegradability well. DEREK for Windows predictions are supported by a mechanistic rationale and literature references. The structural alerts in this program have been developed with a training set of positive and negative toxicity data. However, to support the prediction only a limited number of chemicals in the training set is presented to the user. DEREK for Windows predicts effects by ‘if-then’ reasoning. The program predicts best for mutagenicity and carcinogenicity. Each structural fragment in ECOSAR and DEREK for Windows needs to be evaluated and validated separately.