Quantitative consensus of bioaccumulation models for integrated testing strategies.

A quantitative consensus model based on bioconcentration factor (BCF) predictions obtained from five quantitative structure-activity relationship models was developed for bioaccumulation assessment as an integrated testing approach for waiving. Three categories were considered: non-bioaccumulative, bioaccumulative and very bioaccumulative. Five in silico BCF models were selected and included into a quantitative consensus model by means of the continuous formulation of Bayes' theorem. The discrete likelihoods commonly used in the qualitative Bayesian model were substituted by probability density functions to reduce the loss of information that occurred when continuous BCF values were distributed across the three bioaccumulation categories. Results showed that the continuous Bayesian model yielded the best classification predictions compared not only to the discrete Bayesian model, but also to the individual BCF models. The proposed quantitative consensus model proved to be a suitable approach for integrated testing strategies for continuous endpoints of environmental interest.

[1]  Jon A Arnot,et al.  Bioaccumulation of organic contaminants in humans: a multimedia perspective and the importance of biotransformation. , 2011, Environmental science & technology.

[2]  Emilio Benfenati,et al.  Assessment and validation of the CAESAR predictive model for bioconcentration factor (BCF) in fish , 2010, Chemistry Central journal.

[3]  Robert S. Boethling,et al.  Improved method for estimating bioconcentration/bioaccumulation factor from octanol/water partition coefficient , 1999 .

[4]  S. Giove,et al.  Regional risk assessment for contaminated sites part 1: vulnerability assessment by multicriteria decision analysis. , 2011, Environment international.

[5]  Emilio Benfenati,et al.  A new hybrid system of QSAR models for predicting bioconcentration factors (BCF). , 2008, Chemosphere.

[6]  E Benfenati,et al.  A new bioconcentration factor model based on SMILES and indices of presence of atoms. , 2010, European journal of medicinal chemistry.

[7]  Scott M. Williams,et al.  A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction , 2007, Genetic epidemiology.

[8]  Sebastian Hoffmann,et al.  Integrated Testing Strategy (ITS) - Opportunities to better use existing data and guide future testing in toxicology. , 2010, ALTEX.

[9]  Robert Rallo,et al.  Uncertainty reduction in environmental data with conflicting information. , 2009, Environmental science & technology.

[10]  Frank A. P. C. Gobas,et al.  A review of bioconcentration factor (BCF) and bioaccumulation factor (BAF) assessments for organic chemicals in aquatic organisms , 2006 .

[11]  Gabriela Espinosa,et al.  Using an Ensemble of Neural Based QSARs for the Prediction of Toxicological Properties of Chemical Contaminants , 2005 .

[12]  I. Moriguchi,et al.  Binding of carprofen to human and bovine serum albumins. , 1994, Chemical & pharmaceutical bulletin.

[13]  P. Howard,et al.  Identifying new persistent and bioaccumulative organics among chemicals in commerce. , 2010, Environmental science & technology.

[14]  Peter D. Turney.  Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm , 1994, J. Artif. Intell. Res..

[15]  Pedro M. Domingos.  MetaCost: a general method for making classifiers cost-sensitive , 1999, KDD '99.

[16]  S Dimitrov,et al.  Base-line model for identifying the bioaccumulation potential of chemicals , 2005, SAR and QSAR in environmental research.

[17]  Frank A. P. C. Gobas,et al.  A Generic QSAR for Assessing the Bioaccumulation Potential of Organic Chemicals in Aquatic Food Webs , 2003 .

[18]  Wenjing Fu,et al.  Methods for estimating the bioconcentration factor of ionizable organic chemicals , 2009, Environmental toxicology and chemistry.

[19]  T. A. DelValls,et al.  Using a classical weight-of-evidence approach for 4-years' monitoring of the impact of an accidental oil spill on sediment quality. , 2008, Environment international.

[20]  Stan Szpakowicz,et al.  Beyond Accuracy, F-Score and ROC: A Family of Discriminant Measures for Performance Evaluation , 2006, Australian Conference on Artificial Intelligence.

[21]  Jon A Arnot,et al.  A database of fish biotransformation rates for organic chemicals , 2008, Environmental toxicology and chemistry.

[22]  F. Onorati,et al.  A multidisciplinary weight of evidence approach for classifying polluted sediments: Integrating sediment chemistry, bioavailability, biomarkers responses and bioassays. , 2012, Environment international.

[23]  Ralph Kühne,et al.  Stepwise discrimination between four modes of toxic action of phenols in the Tetrahymena pyriformis assay. , 2003, Chemical research in toxicology.

[24]  Marie Laure Delignette-Muller,et al.  A Bayesian approach to analyzing ecotoxicological data. , 2008, Environmental science & technology.

[25]  M. Schuhmacher,et al.  A spatial multicriteria decision making tool to define the best agricultural areas for sewage sludge amendment. , 2012, Environment international.

[26]  S Dimitrov,et al.  Quantitative prediction of biodegradability, metabolite distribution and toxicity of stable metabolites , 2002, SAR and QSAR in environmental research.

[27]  S. Ashraf,et al.  Image data fusion for the remote sensing of freshwater environments , 2012 .

[28]  S. Hirono,et al.  Simple Method of Calculating Octanol/Water Partition Coefficient. , 1992 .