Literature DB >> 23614547

Arguments for considering uncertainty in QSAR predictions in hazard and risk assessments.

Ullrika Sahlin1, Laura Golsteijn, M Sarfraz Iqbal, Willie Peijnenburg.   

Abstract

Chemical regulation allows non-in vivo testing (i.e. in silico-derived and in vitro-derived) information to replace experimental values from in vivo studies in hazard and risk assessments. Although non-in vitro testing information on chemical activities or properties is subject to added uncertainty as compared to in vivo testing information, this uncertainty is commonly not (fully) taken into account. Considering uncertainty in predictions from quantitative structure-activity relationships (QSARs), which are a form of non-in vivo testing information, may improve the way that QSARs support chemical safety assessment under the EU Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) system. We argue that it is useful to consider uncertainty in QSAR predictions, as it: a) supports rational decision-making; b) facilitates cautious risk management; c) informs uncertainty analysis in probabilistic risk assessment; d) may aid the evaluation of QSAR predictions in weight-of-evidence approaches; and e) provides a probabilistic model to verify the experimental data used in risk assessment. The discussion is illustrated by using case studies of QSAR integrated hazard and risk assessment from the EU-financed CADASTER project. 2013 FRAME.

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Year:  2013        PMID: 23614547     DOI: 10.1177/026119291304100110

Source DB:  PubMed          Journal:  Altern Lab Anim        ISSN: 0261-1929            Impact factor:   1.303


  1 in total

1.  Assessment of uncertainty in chemical models by Bayesian probabilities: Why, when, how?

Authors:  Ullrika Sahlin
Journal:  J Comput Aided Mol Des       Date:  2014-12-10       Impact factor: 3.686

  1 in total

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