Literature DB >> 23624006

Integration of QSAR models for bioconcentration suitable for REACH.

Andrea Gissi1, Orazio Nicolotti, Angelo Carotti, Domenico Gadaleta, Anna Lombardo, Emilio Benfenati.   

Abstract

QSAR (Quantitative Structure Activity Relationship) models can be a valuable alternative method to replace or reduce animal test required by REACH. In particular, some endpoints such as bioconcentration factor (BCF) are easier to predict and many useful models have been already developed. In this paper we describe how to integrate two popular BCF models to obtain more reliable predictions. In particular, the herein presented integrated model relies on the predictions of two among the most used BCF models (CAESAR and Meylan), together with the Applicability Domain Index (ADI) provided by the software VEGA. Using a set of simple rules, the integrated model selects the most reliable and conservative predictions and discards possible outliers. In this way, for the prediction of the 851 compounds included in the ANTARES BCF dataset, the integrated model discloses a R(2) (coefficient of determination) of 0.80, a RMSE (Root Mean Square Error) of 0.61 log units and a sensitivity of 76%, with a considerable improvement in respect to the CAESAR (R(2)=0.63; RMSE=0.84 log units; sensitivity 55%) and Meylan (R(2)=0.66; RMSE=0.77 log units; sensitivity 65%) without discarding too many predictions (118 out of 851). Importantly, considering solely the compounds within the new integrated ADI, the R(2) increased to 0.92, and the sensitivity to 85%, with a RMSE of 0.44 log units. Finally, the use of properly set safety thresholds applied for monitoring the so called "suspicious" compounds, which are those chemicals predicted in proximity of the border normally accepted to discern non-bioaccumulative from bioaccumulative substances, permitted to obtain an integrated model with sensitivity equal to 100%.
Copyright © 2013 Elsevier B.V. All rights reserved.

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Year:  2013        PMID: 23624006     DOI: 10.1016/j.scitotenv.2013.03.104

Source DB:  PubMed          Journal:  Sci Total Environ        ISSN: 0048-9697            Impact factor:   7.963


  3 in total

1.  An ensemble model of QSAR tools for regulatory risk assessment.

Authors:  Prachi Pradeep; Richard J Povinelli; Shannon White; Stephen J Merrill
Journal:  J Cheminform       Date:  2016-09-22       Impact factor: 5.514

2.  Prediction of bioconcentration factors in fish and invertebrates using machine learning.

Authors:  Thomas H Miller; Matteo D Gallidabino; James I MacRae; Stewart F Owen; Nicolas R Bury; Leon P Barron
Journal:  Sci Total Environ       Date:  2018-08-10       Impact factor: 7.963

3.  In Silico Prediction of Cytochrome P450-Drug Interaction: QSARs for CYP3A4 and CYP2C9.

Authors:  Serena Nembri; Francesca Grisoni; Viviana Consonni; Roberto Todeschini
Journal:  Int J Mol Sci       Date:  2016-06-09       Impact factor: 5.923

  3 in total

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