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Sahlin, Ullrika
Publications (10 of 20) Show all publications
Sahlin, U., Jeliazkova, N. & Öberg, T. (2014). Applicability domain dependent predictive uncertainty in QSAR regressions. Molecular Informatics, 33(1), 26-35
Open this publication in new window or tab >>Applicability domain dependent predictive uncertainty in QSAR regressions
2014 (English)In: Molecular Informatics, ISSN 1868-1743, Vol. 33, no 1, p. 26-35Article in journal (Refereed) Published
Abstract [en]

Predictive models used in decision making, such as QSARs in chemical regulation or drug discovery, call for evaluated approaches to quantitatively assess associated uncertainty in predictions. Uncertainty in less reliable predictions may be captured by locally varying predictive errors. In the current study, model-based bootstrapping was combined with analogy reasoning to generate predictive distributions varying in magnitude over a model’s domain of applicability. A resampling experiment based on PLS regressions on four QSAR data sets demonstrated that predictive errors assessed by k nearest neighbour or weighted PRedicted Error Sum of Squares (PRESS) on samples of external test data or by internal cross-validation improved the performance of the uncertainty assessment. Analogy using similarity defined by Euclidean distances, or differences in standard deviation in perturbed predictions, resulted in better performances than similarity defined by distance to, or density of, the training data. Locally assessed predictive distributions had on average at least as good coverage as Gaussian distribution with variance assessed from the PRESS. An R-code is provided that evaluates performances of the suggested algorithms to assess predictive error based on log likelihood scores and empirical coverage graphs, and which applies these to derive confidence intervals or samples from the predictive distributions of query compounds.

National Category
Environmental Sciences
Research subject
Natural Science, Environmental Science
Identifiers
urn:nbn:se:lnu:diva-28032 (URN)10.1002/minf.201200131 (DOI)000346768100004 ()2-s2.0-84895165560 (Scopus ID)
Available from: 2013-08-11 Created: 2013-08-11 Last updated: 2019-11-25Bibliographically approved
Golsteijn, L., Iqbal, M. S., Cassani, S., Hendriks, H. W. M., Kovarich, S., Papa, E., . . . Huijbregts, M. A. J. (2014). Assessing predictive uncertainty in comparative toxicity potentials of triazoles. Environmental Toxicology and Chemistry, 33(2), 293-301
Open this publication in new window or tab >>Assessing predictive uncertainty in comparative toxicity potentials of triazoles
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2014 (English)In: Environmental Toxicology and Chemistry, ISSN 0730-7268, E-ISSN 1552-8618, Vol. 33, no 2, p. 293-301Article in journal (Refereed) Published
Abstract [en]

Comparative toxicity potentials (CTPs) quantify the potential ecotoxicological impacts of chemicals per unit of emission. They are the product of a substance's environmental fate, exposure, and hazardous concentration. When empirical data are lacking, substance properties can be predicted. The goal of the present study was to assess the influence of predictive uncertainty in substance property predictions on the CTPs of triazoles. Physicochemical and toxic properties were predicted with quantitative structure-activity relationships (QSARs), and uncertainty in the predictions was quantified with use of the data underlying the QSARs. Degradation half-lives were based on a probability distribution representing experimental half-lives of triazoles. Uncertainty related to the species' sample size that was present in the prediction of the hazardous aquatic concentration was also included. All parameter uncertainties were treated as probability distributions, and propagated by Monte Carlo simulations. The 90% confidence interval of the CTPs typically spanned nearly 4 orders of magnitude. The CTP uncertainty was mainly determined by uncertainty in soil sorption and soil degradation rates, together with the small number of species sampled. In contrast, uncertainty in species-specific toxicity predictions contributed relatively little. The findings imply that the reliability of CTP predictions for the chemicals studied can be improved particularly by including experimental data for soil sorption and soil degradation, and by developing toxicity QSARs for more species. (c) 2013 SETAC

Keywords
Comparative toxicity potential, Quantitative structure-activity relationship, Uncertainty, Probabilistic modeling, Triazoles
National Category
Environmental Sciences
Research subject
Natural Science, Environmental Science
Identifiers
urn:nbn:se:lnu:diva-32165 (URN)10.1002/etc.2429 (DOI)000329556600005 ()2-s2.0-84892479331 (Scopus ID)
Available from: 2014-02-07 Created: 2014-02-07 Last updated: 2017-12-06Bibliographically approved
Sahlin, U., Golsteijn, L., Iqbal, M. S. & Peijnenburg, W. (2013). Arguments for considering uncertainty in QSAR predictions in hazard and risk assessments. ATLA (Alternatives to Laboratory Animals), 41(1), 91-110
Open this publication in new window or tab >>Arguments for considering uncertainty in QSAR predictions in hazard and risk assessments
2013 (English)In: ATLA (Alternatives to Laboratory Animals), ISSN 0261-1929, Vol. 41, no 1, p. 91-110Article in journal (Refereed) Published
Abstract [en]

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.

National Category
Biological Sciences
Research subject
Natural Science, Environmental Science
Identifiers
urn:nbn:se:lnu:diva-27735 (URN)000330896200028 ()23614547 (PubMedID)2-s2.0-84877120272 (Scopus ID)
Available from: 2013-08-02 Created: 2013-08-02 Last updated: 2017-05-08Bibliographically approved
Cassani, S., Kovarich, S., Papa, E., Roy, P. P., Rahmberg, M., Nilsson, S., . . . Gramatica, P. (2013). Evaluation of CADASTER QSAR Models for the Aquatic Toxicity of (Benzo)triazoles and Prioritisation by Consensus Prediction. ATLA (Alternatives to Laboratory Animals), 41(1), 49-64
Open this publication in new window or tab >>Evaluation of CADASTER QSAR Models for the Aquatic Toxicity of (Benzo)triazoles and Prioritisation by Consensus Prediction
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2013 (English)In: ATLA (Alternatives to Laboratory Animals), ISSN 0261-1929, Vol. 41, no 1, p. 49-64Article in journal (Refereed) Published
Abstract [en]

QSAR regression models of the toxicity of triazoles and benzotriazoles ([B] TAZs) to an alga (Pseudokirchneriella subcapitata), Daphnia magna and a fish (Onchorhynchus mykiss), were developed by five partners in the FP7-EU Project, CADASTER. The models were developed by different methods - Ordinary Least Squares (OLS), Partial Least Squares (PLS), Bayesian regularised regression and Associative Neural Network (ASNN) - by using various molecular descriptors (DRAGON, PaDEL-Descriptor and QSPR-THESAURUS web). In addition, different procedures were used for variable selection, validation and applicability domain inspection. The predictions of the models developed, as well as those obtained in a consensus approach by averaging the data predicted from each model, were compared with the results of experimental tests that were performed by two CADASTER partners. The individual and consensus models were able to correctly predict the toxicity classes of the chemicals tested in the CADASTER project, confirming the utility of the QSAR approach. The models were also used for the prediction of aquatic toxicity of over 300 (B)TAZs, many of which are included in the REACH pre-registration list, and were without experimental data. This highlights the importance of QSAR models for the screening and prioritisation of untested chemicals, in order to reduce and focus experimental testing.

Keywords
aquatic toxicity, applicability domain, (benzo)triazoles, consensus, QSAR, REACH, validation
National Category
Environmental Sciences
Research subject
Natural Science, Environmental Science
Identifiers
urn:nbn:se:lnu:diva-56674 (URN)000330896200025 ()23614544 (PubMedID)2-s2.0-84877117871 (Scopus ID)
Available from: 2016-09-22 Created: 2016-09-22 Last updated: 2017-05-08Bibliographically approved
Durjava, M. K., Kolar, B., Arnus, L., Papa, E., Kovarich, S., Sahlin, U. & Peijnenburg, W. (2013). Experimental Assessment of the Environmental Fate and Effects of Triazoles and Benzotriazole. ATLA (Alternatives to Laboratory Animals), 41(1), 65-75
Open this publication in new window or tab >>Experimental Assessment of the Environmental Fate and Effects of Triazoles and Benzotriazole
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2013 (English)In: ATLA (Alternatives to Laboratory Animals), ISSN 0261-1929, Vol. 41, no 1, p. 65-75Article in journal (Refereed) Published
Abstract [en]

The environmental fate and effects of triazoles and benzotriazoles are of concern within the context of chemical regulation. As part of an intelligent testing strategy, experimental tests were performed on endpoints that are relevant for risk assessment. The experimental tests included the assessment of eco-toxicity to an alga, a daphnid and zebrafish embryos, and the assessment of ready biodegradability. Triazole and benzotriazole compounds were selected for testing, based on existing toxicity data for vertebrate and invertebrate species, as well as on the principal component analysis of molecular descriptors aimed at selecting the minimum number of test compounds in order to maximise the chemical domain spanned for both compound classes. The experimental results show that variation in the toxicities of triazoles and benzotriazole across species was relatively minor; in general, the largest factor was approximately 20. The study conducted indicated that triazoles are not readily biodegradable.

Keywords
algae, aquatic toxicity, benzotriazoles, daphnids, ready biodegradability, triazoles, zebrafish embryos
National Category
Environmental Sciences
Research subject
Natural Science, Environmental Science
Identifiers
urn:nbn:se:lnu:diva-56675 (URN)000330896200026 ()23614545 (PubMedID)2-s2.0-84877144622 (Scopus ID)
Available from: 2016-09-22 Created: 2016-09-22 Last updated: 2017-05-08Bibliographically approved
Söderström, M., Boldemann, C., Sahlin, U., Mårtensson, F., Raustorp, A. & Blennow, M. (2013). The quality of the outdoor environment influences childrens health- a cross sectional study of preschools. Acta Paediatrica, 102(1), 83-91
Open this publication in new window or tab >>The quality of the outdoor environment influences childrens health- a cross sectional study of preschools
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2013 (English)In: Acta Paediatrica, ISSN 0803-5253, E-ISSN 1651-2227, Vol. 102, no 1, p. 83-91Article in journal (Refereed) Published
Abstract [en]

Aim To test how the quality of the outdoor environment of child day care centres (DCCs) influences children's health. Methods The environment was assessed using the Outdoor Play Environmental Categories (OPEC) tool, time spent outdoors and physical activity as measured by pedometer. 172/253 (68%) of children aged 3.05.9 from nine DCCs participated in Southern Sweden. Health data collected were body mass index, waist circumference, saliva cortisol, length of night sleep during study, and symptoms and well-being which were scored (1-week diary 121 parent responders). Also, parent-rated well-being and health of their child were scored (questionnaire, 132 parent responders). MANOVA, ANOVA and principal component analyses were performed to identify impacts of the outdoor environment on health. Results High-quality outdoor environment at DCCs is associated with several health aspects in children such as leaner body, longer night sleep, better well-being and higher mid-morning saliva cortisol levels. Conclusion The quality of the outdoor environment at DCCs influenced the health and well-being of preschool children and should be given more attention among health care professionals and community planners.

National Category
Pediatrics Other Health Sciences
Research subject
Social Sciences, Sport Science
Identifiers
urn:nbn:se:lnu:diva-22480 (URN)10.1111/apa.12047 (DOI)000312313200028 ()23035750 (PubMedID)2-s2.0-84871019308 (Scopus ID)
Projects
Kidscape
Available from: 2012-11-14 Created: 2012-11-14 Last updated: 2019-01-23Bibliographically approved
Sahlin, U. (2013). Uncertainty in QSAR Predictions. ATLA (Alternatives to Laboratory Animals), 41(1), 111-125
Open this publication in new window or tab >>Uncertainty in QSAR Predictions
2013 (English)In: ATLA (Alternatives to Laboratory Animals), ISSN 0261-1929, Vol. 41, no 1, p. 111-125Article in journal (Refereed) Published
Abstract [en]

It is relevant to consider uncertainty in individual predictions when quantitative structure-activity (or property) relationships (QSARs) are used to support decisions of high societal concern. Successful communication of uncertainty in the integration of QSARs in chemical safety assessment under the EU Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) system can be facilitated by a common understanding of how to define, characterise, assess and evaluate uncertainty in QSAR predictions. A QSAR prediction is, compared to experimental estimates, subject to added uncertainty that comes from the use of a model instead of empirically-based estimates. A framework is provided to aid the distinction between different types of uncertainty in a QSAR prediction: quantitative, i.e. for regressions related to the error in a prediction and characterised by a predictive distribution; and qualitative, by expressing our confidence in the model for predicting a particular compound based on a quantitative measure of predictive reliability. It is possible to assess a quantitative (i.e. probabilistic) predictive distribution, given the supervised learning algorithm, the underlying QSAR data, a probability model for uncertainty and a statistical principle for inference. The integration of QSARs into risk assessment may be facilitated by the inclusion of the assessment of predictive error and predictive reliability into the "unambiguous algorithm", as outlined in the second OECD principle.

Keywords
applicability domain, knowledge-based uncertainty, probabilistic risk assessment, regression, uncertainty analysis
National Category
Environmental Sciences
Research subject
Natural Science, Environmental Science
Identifiers
urn:nbn:se:lnu:diva-56676 (URN)000330896200029 ()23614548 (PubMedID)2-s2.0-84877123509 (Scopus ID)
Available from: 2016-09-22 Created: 2016-09-22 Last updated: 2017-05-08Bibliographically approved
Iqbal, M. S., Golsteijn, L., Öberg, T., Sahlin, U., Papa, E., Kovarich, S. & Huijbregts, M. A. J. (2013). Understanding quantitative structure-property relationships uncertainty in environmental fate modeling. Environmental Toxicology and Chemistry, 32(5), 1069-1076
Open this publication in new window or tab >>Understanding quantitative structure-property relationships uncertainty in environmental fate modeling
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2013 (English)In: Environmental Toxicology and Chemistry, ISSN 0730-7268, E-ISSN 1552-8618, Vol. 32, no 5, p. 1069-1076Article in journal (Refereed) Published
Abstract [en]

In cases in which experimental data on chemical-specific input parameters are lacking, chemical regulations allow the use of alternatives to testing, such as in silico predictions based on quantitative structure–property relationships (QSPRs). Such predictions are often given as point estimates; however, little is known about the extent to which uncertainties associated with QSPR predictions contribute to uncertainty in fate assessments. In the present study, QSPR-induced uncertainty in overall persistence (POV) and long-range transport potential (LRTP) was studied by integrating QSPRs into probabilistic assessments of five polybrominated diphenyl ethers (PBDEs), using the multimedia fate model Simplebox. The uncertainty analysis considered QSPR predictions of the fate input parameters' melting point, water solubility, vapor pressure, organic carbon–water partition coefficient, hydroxyl radical degradation, biodegradation, and photolytic degradation. Uncertainty in POV and LRTP was dominated by the uncertainty in direct photolysis and the biodegradation half-life in water. However, the QSPRs developed specifically for PBDEs had a relatively low contribution to uncertainty. These findings suggest that the reliability of the ranking of PBDEs on the basis of POV and LRTP can be substantially improved by developing better QSPRs to estimate degradation properties. The present study demonstrates the use of uncertainty and sensitivity analyses in nontesting strategies and highlights the need for guidance when compounds fall outside the applicability domain of a QSPR.

National Category
Environmental Sciences
Research subject
Natural Science, Environmental Science
Identifiers
urn:nbn:se:lnu:diva-23272 (URN)10.1002/etc.2167 (DOI)000317852700013 ()2-s2.0-84876412109 (Scopus ID)
Available from: 2013-01-04 Created: 2013-01-04 Last updated: 2017-12-06Bibliographically approved
Brandmaier, S., Sahlin, U., Tetko, I. & Öberg, T. (2012). PLS-Optimal: A stepwise D-Optimal design based on latent variables. Journal of Chemical Information and Modeling, 52(4), 975-983
Open this publication in new window or tab >>PLS-Optimal: A stepwise D-Optimal design based on latent variables
2012 (English)In: Journal of Chemical Information and Modeling, ISSN 1549-9596, Vol. 52, no 4, p. 975-983Article in journal (Refereed) Published
Abstract [en]

Several applications, such as risk assessment within REACH or drug discovery, require reliable methods for the design of experiments and efficient testing strategies. Keeping the number of experiments as low as possible is important from both a financial and an ethical point of view, as exhaustive testing of compounds requires significant financial resources and animal lives. With a large initial set of compounds, experimental design techniques can be used to select a representative subset for testing. Once measured, these compounds can be used to develop quantitative structure–activity relationship models to predict properties of the remaining compounds. This reduces the required resources and time. D-Optimal design is frequently used to select an optimal set of compounds by analyzing data variance. We developed a new sequential approach to apply a D-Optimal design to latent variables derived from a partial least squares (PLS) model instead of principal components. The stepwise procedure selects a new set of molecules to be measured after each previous measurement cycle. We show that application of the D-Optimal selection generates models with a significantly improved performance on four different data sets with end points relevant for REACH. Compared to those derived from principal components, PLS models derived from the selection on latent variables had a lower root-mean-square error and a higher Q2 and R2. This improvement is statistically significant, especially for the small number of compounds selected.

National Category
Environmental Sciences Probability Theory and Statistics
Research subject
Natural Science, Environmental Science
Identifiers
urn:nbn:se:lnu:diva-18208 (URN)10.1021/ci3000198 (DOI)2-s2.0-84862021618 (Scopus ID)
Available from: 2012-04-03 Created: 2012-04-03 Last updated: 2016-11-15Bibliographically approved
Iqbal, M. S., Golsteijn, L., Öberg, T., Sahlin, U., Papa, E., Kovarich, S. & Huijbregts, M. A. J. (2012). The influence of uncertainty in quantitative structure-property relationships on persistence and long-range transport potential: the case of polybrominated diphenyl ethers (PBDEs). In: : . Paper presented at CADASTER Workshop on the development and application of QSAR models with respect to the REACH guidelines, Munich, Germany September 7-9.
Open this publication in new window or tab >>The influence of uncertainty in quantitative structure-property relationships on persistence and long-range transport potential: the case of polybrominated diphenyl ethers (PBDEs)
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2012 (English)Conference paper, Poster (with or without abstract) (Other academic)
National Category
Environmental Sciences
Research subject
Natural Science, Environmental Science
Identifiers
urn:nbn:se:lnu:diva-24427 (URN)
Conference
CADASTER Workshop on the development and application of QSAR models with respect to the REACH guidelines, Munich, Germany September 7-9
Available from: 2013-02-18 Created: 2013-02-18 Last updated: 2016-11-15Bibliographically approved
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