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• W2113134442 abstract "In the regulatory assessment of chemicals, the use of in silico prediction methods such as (quantitative) structure-activity relationship models ([Q]SARs), is increasingly required or encouraged, in order to increase the efficiency and effectiveness of the risk assessment process, and to minimise the reliance on animal testing. The main question for the assessor concerns the usefulness of the prediction approach, which can be broken down into the practical applicability of the method and the adequacy of the predictions. A framework for assessing and documenting (Q)SAR models and their predictions has been established at the European and international levels. Exactly how the framework is applied in practice will depend on the provisions of the specific legislation and the context in which the nontesting data are being used. This report describes the current framework for documenting (Q)SAR models and their predictions, and discuses how it might be built upon to provide more detailed guidance on the use of (Q)SAR predictions in regulatory decision making. The proposed framework is illustrated by using selected pesticide active compounds as examples. LIST OF ABBREVIATIONS AD Applicability domain ADME Absorption, Distribution, Metabolism and Elimination ANN Artificial Neural Network CRD UK Chemicals Regulations Directorate DfW Derek for Windows ECHA European Chemicals Agency EFSA European Food Safety Authority EU European Union GCMP Good Computer Modelling Practice JRC Joint Research Centre k-NN K Nearest Neighbours LOAEL Lowest Observed Adverse Effect Level LOO Leave-one-out LMO Leave-many-out OECD Organisation for Economic Cooperation and Development PBBK Physiologically Based Biokinetic (model) PPP Plant Protection Product QMRF QSAR Model Reporting Format QPRF QSAR Prediction Reporting Format QSAR Quantitative Structure-Activity Relationship REACH Registration, Evaluation, Authorisation and Restriction of Chemicals ROC Receiver Operating Characteristic SAR Structure-Activity Relationship SVM Support Vector Machine (computational method) TTC Threshold of Toxicological Concern" @default.
• W2113134442 created "2016-06-24" @default.
• W2113134442 creator A5010347371 @default.
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• W2113134442 date "2011-01-01" @default.
• W2113134442 modified "2023-09-23" @default.
• W2113134442 title "A Framework for assessing in silico Toxicity Predictions: Case Studies with selected Pesticides" @default.
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• W2113134442 doi "https://doi.org/10.2788/29048" @default.
• W2113134442 hasPublicationYear "2011" @default.
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