Arguments for considering uncertainty in QSAR predictions in hazard and risk assessments.
(2013) In ATLA: Alternatives To Laboratory Animals 41(1). p.91-110- 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... (More)
- 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3733390
- author
- Sahlin, Ullrika LU ; Golsteijn, Laura ; Iqbal, M Sarfraz and Peijnenburg, Willie
- organization
- publishing date
- 2013
- type
- Contribution to journal
- publication status
- published
- subject
- in
- ATLA: Alternatives To Laboratory Animals
- volume
- 41
- issue
- 1
- pages
- 91 - 110
- publisher
- SAGE Publications
- external identifiers
-
- pmid:23614547
- wos:000330896200028
- scopus:84877120272
- ISSN
- 0261-1929
- project
- Uncertainty and Evidence Lab
- language
- English
- LU publication?
- yes
- id
- ad06d3ee-e35a-4980-8813-65975d04da72 (old id 3733390)
- date added to LUP
- 2016-04-01 12:54:43
- date last changed
- 2022-03-29 04:27:14
@article{ad06d3ee-e35a-4980-8813-65975d04da72,
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.}},
author = {{Sahlin, Ullrika and Golsteijn, Laura and Iqbal, M Sarfraz and Peijnenburg, Willie}},
issn = {{0261-1929}},
language = {{eng}},
number = {{1}},
pages = {{91--110}},
publisher = {{SAGE Publications}},
series = {{ATLA: Alternatives To Laboratory Animals}},
title = {{Arguments for considering uncertainty in QSAR predictions in hazard and risk assessments.}},
volume = {{41}},
year = {{2013}},
}