1,721,183 research outputs found
Results from a Data Mining Approach to Predictive Toxicology. The Case of Pesticide Data
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Clustering and classification techniques to assess aquatic toxicity
No full textProc. IEEE ,ISBN, , Septembe
An Automated Group Contribution Method in Predicting Aquatic Toxicity: The Diatomic Fragment Approach
No full textWe developed a group contribution method (GCM) to correlate acute toxicity (96 h LC50) for the fathead minnow (Pimephales promelas) for 607 organic chemicals. Unlike most of the existing methods, the new one makes no use of predefined groups as descriptors. A simple general rule is proposed to break down any molecule into diatomic fragments. The entire data set was partitioned three times. Each time, a training set and a test set were obtained with a ratio of 2:1. For each partition quantitative structure-activity relationship, models were developed using Powell’s minimization method, multilinear regression, neural networks, and partial least squares. The GCM method achieved a good correlation of the data for both training and test sets, regardless of the partition considered. The method is therefore robust and can
be generally applied. Further model improvements are described
Application of a Fragment–based Model to the Prediction of the Genotoxicity of Aromatic Amines
No full textInvestigating landfill leachate toxicity in vitro : a review of cell models and endpoints
No full textLandfill leachate is a complex mixture characterized by high toxicity and able to contaminate soils and waters surrounding the dumpsite, especially in developing countries where engineered landfills are still rare. Leachate pollution can severely damage natural ecosystems and harm human health. Traditionally, the hazard assessment of leachate is based on physicochemical characterization but the toxicity is not considered. In the last few decades, different bioassays have been used to assess the toxicity of this complex matrix, including human-related in vitro models. This article reviews the cell bioassays successfully used for the risk assessment of leachate and to evaluate the efficiency of toxicity removal of several processes for detoxification of this wastewater. Articles from 2003 to 2018 are covered, focusing mainly on studies that used human cell lines, highlighting the usefulness and adequacy of in vitro models for assessing the hazard involved with exposure to leachate, particularly as an integrative supporting tool for chemical-based risk assessment. Leachate is generally toxic, mutagenic, genotoxic and estrogenic in vitro, and these effects can be measured in the cells exposed to already low concentrations, confirming the serious hazard of this wastewater for human health
Top-Priority Fragment QSAR Approach in Predicting Pesticide Aquatic Toxicity
No full textIn the framework of pesticide risk assessment, a fragment-based QSAR approach is presented to correlate LC50-96 h acute toxicity to the rainbow trout (Oncorhynchus mykiss). While there are other fragment based modeling routes, our approach exploits the possibility of prioritizing fragments’ contributions to toxicity. On the assumption that one fragment might be mainly responsible for the molecular toxicity, we developed a three-stage modeling strategy to select the most important moieties and to establish their priorities at a molecular level. This strategy was tested on a heterogeneous dataset containing 282 pesticides, collected under the EU-funded project Demetra. Quantitative toxicity prediction yielded good results for the training set (R2TR = 0.85) and the test set (R2TS = 0.75). The advantages and limitations of the
current priority strategy are examined
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