The Use of (Q)SAR Methods in the Context of REACH

Lahl, Uwe; Gundert‐Remy, Ursula

doi:10.1080/15376510701857288

Cited by 30 publications

(17 citation statements)

References 33 publications

(33 reference statements)

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“…In our work, the predicted results for the same endpoints (B and P) obtained from the PBT Profiler and the Danish (Q)SAR database were compared against the EPI Suite since these tools incorporate the same estimation software used by the EPI programme. As would be expected the predictions found in both the Danish (Q)SAR database and the PBT Profiler were in line with the EPI Suite prediction with the surprising exception of thiourea (4). In this case the predictions from the EPI Suite, in particular BIOWIN, are significantly different from those obtained from the Danish (Q)SAR even though they also used the EPIWIN models, see Table 2.…”

supporting

confidence: 82%

“…However, these costs as well as the number of animals required for laboratory testing can be reduced if reliable predictive tools such as (quantitative) structure-activity relationships ((Q)SAR) models are used instead. REACH regulation clearly encourages the use of (Q)SAR to fill the information gaps for the purposes of classification and labelling, risk assessment and for the initial identification of potential PBT properties when sufficient experimental data is not available [4,5]. In addition, (Q)SARs has been used by EU regulators to identify PBT and very persistent, very bioaccumulative (vPvB) substances [6].…”

Section: Introductionmentioning

confidence: 99%

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Comparative PBT screening using (Q)SAR tools within REACH legislation

Zachary¹,

Greenway²

2009

SAR and QSAR in Environmental Research

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Small to medium sized enterprises (SMEs) in the EU are facing challenges due to the introduction of new legislation designed to protect consumers and the environment, REACH (Registration, Evaluation, Authorisation and Restriction of CHemicals). There can be high costs associated with implementing REACH because data on mammalian toxicity, environmental toxicity and environmental fate properties is required and if this data is obtained experimentally the cost is significant. These costs can be reduced if reliable quantitative structure-activity relationships ((Q)SAR) models are instead used to obtain the required information. In this paper we investigate how easily freely available (Q)SAR models can be applied for persistent, bioaccumulative and toxic (PBT) screening of 17 chemicals of interest to SMEs. In this study the PBT predictions obtained from the more user-friendly PBT Profiler and the Danish(Q)SAR database for the chemicals were compared with the results taken directly from the EPI Suite software. It was found that these widely used (Q)SAR databases might have some errors and examples are provided. It was concluded that extra care must be taken when considering the use of these databases for PBT screening. In addition, to increase the likelihood of a correct prediction, data estimates from various (Q)SAR models relevant to the PBT endpoints must be compared.

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supporting

confidence: 82%

Section: Introductionmentioning

confidence: 99%

Comparative PBT screening using (Q)SAR tools within REACH legislation

Zachary¹,

Greenway²

2009

SAR and QSAR in Environmental Research

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“…The authors of [ 51 ] developed a QSAR model based on 546 organic molecules, to predict acute aquatic toxicity towards Daphnia magna, which is the organism preferred for short-term aquatic toxicity testing according to REACH [ 52 ]. Ad hoc-designed workflows were used for data curation and filtering, as well as for the extraction of LC 50 data, which in this case is defined to be the concentration that causes death in 50 % of test Daphnia magna over a test duration of 48 h. For modelling purposes the −log(LC 50 ) values were considered as the dependent variable to be predicted.…”

Section: Resultsmentioning

confidence: 99%

RRegrs: an R package for computer-aided model selection with multiple regression models

et al. 2015

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BackgroundPredictive regression models can be created with many different modelling approaches. Choices need to be made for data set splitting, cross-validation methods, specific regression parameters and best model criteria, as they all affect the accuracy and efficiency of the produced predictive models, and therefore, raising model reproducibility and comparison issues. Cheminformatics and bioinformatics are extensively using predictive modelling and exhibit a need for standardization of these methodologies in order to assist model selection and speed up the process of predictive model development. A tool accessible to all users, irrespectively of their statistical knowledge, would be valuable if it tests several simple and complex regression models and validation schemes, produce unified reports, and offer the option to be integrated into more extensive studies. Additionally, such methodology should be implemented as a free programming package, in order to be continuously adapted and redistributed by others.ResultsWe propose an integrated framework for creating multiple regression models, called RRegrs. The tool offers the option of ten simple and complex regression methods combined with repeated 10-fold and leave-one-out cross-validation. Methods include Multiple Linear regression, Generalized Linear Model with Stepwise Feature Selection, Partial Least Squares regression, Lasso regression, and Support Vector Machines Recursive Feature Elimination. The new framework is an automated fully validated procedure which produces standardized reports to quickly oversee the impact of choices in modelling algorithms and assess the model and cross-validation results. The methodology was implemented as an open source R package, available at https://www.github.com/enanomapper/RRegrs, by reusing and extending on the caret package.ConclusionThe universality of the new methodology is demonstrated using five standard data sets from different scientific fields. Its efficiency in cheminformatics and QSAR modelling is shown with three use cases: proteomics data for surface-modified gold nanoparticles, nano-metal oxides descriptor data, and molecular descriptors for acute aquatic toxicity data. The results show that for all data sets RRegrs reports models with equal or better performance for both training and test sets than those reported in the original publications. Its good performance as well as its adaptability in terms of parameter optimization could make RRegrs a popular framework to assist the initial exploration of predictive models, and with that, the design of more comprehensive in silico screening applications.Graphical abstractRRegrs is a computer-aided model selection framework for R multiple regression models; this is a fully validated procedure with application to QSAR modelling

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“…QSARs are useful in various risk assessment contexts: for evaluating experimental data, for further testing to refine the PEC/PNEC ratio, for estimation of input parameters in risk assessment and in identifying data needs on effects of potential concerns 4. Intelligent testing strategies that make use of tools such as QSARs are being sought to speed up these assessments, to reduce costs and to minimize animal testing 5. QSAR models inevitably include uncertainties that need to be characterized, not least since many regulators advocate the use of probabilistic risk assessment,3a, 6 which is the third and last level in a CSA 3b…”

Section: Introductionmentioning

confidence: 99%

A Risk Assessment Perspective of Current Practice in Characterizing Uncertainties in QSAR Regression Predictions

Sahlin

Filipsson

Öberg

2011

Molecular Informatics

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The European REACH legislation accepts the use of non-testing methods, such as QSARs, to inform chemical risk assessment. In this paper, we aim to initiate a discussion on the characterization of predictive uncertainty from QSAR regressions. For the purpose of decision making, we discuss applications from the perspective of applying QSARs to support probabilistic risk assessment. Predictive uncertainty is characterized by a wide variety of methods, ranging from pure expert judgement based on variability in experimental data, through data-driven statistical inference, to the use of probabilistic QSAR models. Model uncertainty is dealt with by assessing confidence in predictions and by building consensus models. The characterization of predictive uncertainty would benefit from a probabilistic formulation of QSAR models (e.g. generalized linear models, conditional density estimators or Bayesian models). This would allow predictive uncertainty to be quantified as probability distributions, such as Bayesian predictive posteriors, and likelihood-based methods to address model uncertainty. QSAR regression models with point estimates as output may be turned into a probabilistic framework without any loss of validity from a chemical point of view. A QSAR model for use in probabilistic risk assessment needs to be validated for its ability to make reliable predictions and to quantify associated uncertainty.

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The Use of (Q)SAR Methods in the Context of REACH

Cited by 30 publications

References 33 publications

Comparative PBT screening using (Q)SAR tools within REACH legislation

Comparative PBT screening using (Q)SAR tools within REACH legislation

RRegrs: an R package for computer-aided model selection with multiple regression models

A Risk Assessment Perspective of Current Practice in Characterizing Uncertainties in QSAR Regression Predictions

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