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ATLA 41.1, March 2013

//ATLA 41.1, March 2013

ATLA Volume 41 No 1, published March 2013

Editorial: Computational Toxicology is Now Inseparable from Experimental Toxicology

Mark T.D. Cronin

Abstract

The developing challenge is not that we have too few data to model, but that we now need to consider how to curate the data, store them in useful and usable databases, determine those data that may be considered ‘reliable’, and cope with duplicate and contradictory data.
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News & Views

ATLA staff writer

Abstract

Free Access to Early Issues of ATLA
New Alternatives Research Centre
New Chair in Animal Replacement Science
ARDF Research Grants
Spread of Influenza Studied in Human Volunteers
Human Studies for Depression Treatment
Tumour Extravasation Studied In Vitro
Laboratory Equipment Materials Affect Cell Viability
Mouse Models Poorly Mimic Human Inflammatory Diseases
Chimps Seem to Enjoy Puzzles
EU Ban on Cosmetics Testing
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CAAT News & Views

ATLA staff writer

Abstract

CAAT to Receive $2-million Sub-grant from the US Defense Threat Reduction Agency
CAAT Presentations at the European Parliament
International Symposium on Endocrine Active Substances (EASs)
CAAT Addresses ‘Clinical Trial and Error: Beauty and the Beast’
Middle School Students Hold Fundraiser for CAAT
Job Opening: Post-doctoral Researcher (Human Toxome Project)
New CAAT Supporters and Advisory Board Members
Recent Publications
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Exemplification of the Implementation of Alternatives to Experimental Testing in Chemical Risk Assessment — Case Studies from the CADASTER Project

Willie J.G.M. Peijnenburg and Igor V. Tetko

Abstract

In this special issue of ATLA, a compilation of contributions from the second CADASTER Workshop in Munich (7–9 October, 2012) regarding the implementation of alternatives to experimental (in vivo) data in risk and hazard assessment, is provided.
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Species Sensitivity Distribution Estimation from Uncertain (QSAR-based) Effects Data

Tom Aldenberg and Emiel Rorije

Abstract

In environmental risk assessment, Species Sensitivity Distributions (SSDs) can be applied to estimate a PNEC (Predicted No-Effect Concentration) for a chemical substance, when sufficient data on species toxicities are available. The European Chemicals Agency (ECHA) recommendation is 10 biological species. The question addressed in this paper, is whether QSAR-predicted toxicities can be included in SSD based PNEC estimates, and whether any modifications need to be made to account for the uncertainty in the QSAR-model estimates. This problem is addressed from a probabilistic modelling point of view. From classical analysis of variation (ANOVA), we review how the error-in-data SSD problem is similar to separation into between-group and within-group variance. ECHA guidance suggests averaging similar endpoint data for a species, which is consistent with group means, as in ANOVA. This exercise reveals that error-indata reduces the estimation of the between species variation, i.e. the SSD variance, rather than enlarging it. A Bayesian analysis permits the assessment of the uncertainty of the SSD mean and variance parameters for given values of mean species toxicity error. This requires a hierarchical model. Prototyping this model for an artificial five-species data set seems to suggest that the influence of data error is relatively minor. Moreover, when neglecting this data error, a slightly conservative estimate of the SSD results. Hence, we suggest including (model-predicted) data as model point estimates and handling the SSD as usual. The Bayesian simulation of the error-in-data SSD leads to predictive distributions, being an average of posterior spaghetti plot densities or cumulative distributions. We derive new predictive extrapolation constants with several improvements over previous median uncertainty log10HC5 estimates, in that they are easily calculable from spreadsheet Student-t functions and based on a more realistic uniform prior for the SSD standard deviation. Other advantages are that they are single-number extrapolation constants and they are more sensitive to small sample size.
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Evaluation of CADASTER QSAR Models for the Aquatic Toxicity of (Benzo)triazoles and Prioritisation by Consensus Prediction

Stefano Cassani, Simona Kovarich, Ester Papa, Partha Pratim Roy, Magnus Rahmberg, Sara Nilsson, Ullrika Sahlin, Nina Jeliazkova, Nikolay Kochev, Ognyan Pukalov, Igor V. Tetko, Stefan Brandmaier, Mojca Kos Durjava, Boris Kolar, Willie Peijnenburg, and Paola Gramatica

Abstract

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 QSPRTHESAURUS 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.
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From Descriptors to Predicted Properties: Experimental Design by Using Applicability Domain Estimation

Stefan Brandmaier, Sergii Novotarskyi, Iurii Sushko and Igor V. Tetko

Abstract

The importance of reliable methods for representative sub-sampling in terms of experimental
design and risk assessment within the European Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) system is crucial. We developed experimental design approaches, by utilising predicted properties and the ‘distance to model’ parameter, to estimate the benefits of certain compounds to the quality of a resulting model. A statistical evaluation of four regression data sets and one classification data set showed that the adaptive concept of iteratively refining the representation of the chemical space contributes to a more efficient and more reliable selection in comparison to traditional approaches. The evaluation of compounds with regard to the uncertainty and the correlation of prediction is beneficial, and in particular, for regression data sets of sufficient size, whereas the use of predicted properties to define
the chemical space is beneficial for classification models.
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Experimental Assessment of the Environmental Fate and Effects of Triazoles and Benzotriazole

Mojca Kos Durjava, Boris Kolar, Lovro Arnus, Ester Papa, Simona Kovarich, Ullrika Sahlin
and Willie Peijnenburg4,5

Abstract

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 ecotoxicity 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.
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Read-across Estimates of Aquatic Toxicity for Selected Fragrances

Emiel Rorije, Tom Aldenberg and Willie Peijnenburg

Abstract

Read-across as a non-animal testing alternative for the generation of risk assessment data can be useful in those cases where quantitative structure–activity relationship (QSAR) models are not available, or are less well developed. This paper provides read-across case studies for the estimation of the aquatic toxicity of five different fragrance substances, and proposes a pragmatic approach for expressing uncertainty in read-across estimates. The aquatic toxicity estimates and their uncertainties are subsequently used to estimate fresh water compartment Predicted No-Effect Concentrations (PNECs), with their two-sided 90% Confidence Intervals (CIs). These PNECs can be used directly in risk assessment. The results of the musk fragrance read-across cases (musk xylene, musk ketone and galaxolide) are compared to experimentally derived PNEC values. The read-across estimates made by using similarity in a hypothesised mechanism of action for (acute) toxicity of musk xylene gave a PNEC of 2μg/L (90% CI 0.0004–13.5μg/L) with the Species Sensitivity Distribution (SSD) approach. This estimated value is 1.8 times above the experimentally-based fresh water PNEC of 1.1μg/L. For musk ketone and galaxolide, the PNEC values based on the SSD approach and employing a toxicity mechanism-based read-across were 2.0 times greater, and 4.9 times below the experimentally derived PNEC values, respectively.
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Arguments for Considering Uncertainty in QSAR Predictions in Hazard and Risk Assessments

Ullrika Sahlin, Laura Golsteijn, M. Sarfraz Iqbal1 and 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.
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