The OSIRIS Weight of Evidence approach: ITS for skin sensitisation.

Within the EU FP6 project OSIRIS approaches to Integrated Testing Strategies (ITSs) were developed, with the aim to facilitate the use of non-test and non-animal testing information in regulatory risk assessment of chemicals. This paper describes an analytical Weight-of-Evidence (WoE) approach to an ITS for the endpoint of skin sensitisation. It specifically addresses the European chemicals legislation REACH, but the concept is readily applicable to ITS and WoE procedures in other regulatory frameworks, and for other toxicological endpoints. Bayesian statistics are applied to estimate the reliability of a conclusion on the sensitisation potential of a chemical, combining evidence from different information sources such as QSAR model predictions, in vitro and in vivo test results. The methodology allows for adaptation of the weight of individual information sources to account for the different levels of reliability of the individual ITS components. The calculated reliability of the WoE conclusion gives an objective, transparent and reproducible measure to decide if the information requirements for data evaluation are satisfied. Furthermore, in case the WoE is not sufficient, it gives the possibility to evaluate a priori if and how it will be possible to fulfil the information requirements with additional tests and/or model predictions.

OSIRIS, a quest for proof of principle for integrated testing strategies of chemicals for four human health endpoints.

Chemical substances policies in Europe are aiming towards chemical safety and at the same time a reduction in animal testing. These goals are alleged to be reachable by mining as many relevant data as possible, evaluate these data with regard to validity, reliability and relevance, and use of these data in so-called Integrated Testing Strategies (ITS). This paper offers an overview of four human health endpoints that were part of the EU-funded OSIRIS project, aiming to develop ITS fit for the EU chemicals legislation REACH. The endpoints considered cover their categorical as well as continuous characteristics: skin sensitisation, repeated dose toxicity, mutagenicity and carcinogenicity. Detailed papers are published elsewhere in this volume. The stepwise ITS approach developed takes advantage of existing information, groups information about similar substances and integrates exposure considerations. The different and possibly contradictory information is weighted and the respective uncertainties taken into account in a weight of evidence (WoE) approach. In case of data gaps, the ITS proposes the most appropriate method to acquire the missing information. Each building block for the ITS, i.e. each in vivo test, in vitro test, (Q)SAR model or human evidence, is evaluated with regard to quality.

The OSIRIS Weight of Evidence approach: ITS mutagenicity and ITS carcinogenicity.

Risk assessment of chemicals usually implies data evaluation of in vivo tests in rodents to conclude on their hazards. The FP7 European project OSIRIS has developed integrated testing strategies (ITS) for relevant toxicological endpoints to avoid unnecessary animal testing and thus to reduce time and costs. This paper describes the implementation of ITS mutagenicity and carcinogenicity in the public OSIRIS webtool. The data requirements of REACH formed the basis for these ITS. The main goal was to implement procedures to reach a conclusion on the adequacy and validity of available data. For the mutagenicity ITS a quantitative Weight of Evidence approach based on Bayesian statistics was developed and implemented. The approach allows an overall quality assessment of all available data for the five types of mutagenicity data requirements: in vitro bacterial mutagenicity, in vitro and in vivo chromosome aberration, in vitro and in vivo mammalian mutagenicity. For the carcinogenicity ITS a tool was developed to evaluate the quality of studies not conforming (entirely) to guidelines. In a tiered approach three quality aspects are assessed: documentation (reliability), study design (adequacy) and scope of examination (validity). The quality assessment is based on expert and data driven quantitative Weight of Evidence.

The OSIRIS Weight of Evidence approach: ITS for the endpoints repeated-dose toxicity (RepDose ITS).

In the FP6 European project OSIRIS, Integrated Testing Strategies (ITSs) for relevant toxicological endpoints were developed to avoid new animal testing and thus to reduce time and costs. The present paper describes the development of an ITS for repeated-dose toxicity called RepDose ITS which evaluates the conditions under which in vivo non-guideline studies are reliable. In a tiered approach three aspects of these "non-guideline" studies are assessed: the documentation of the study (reliability), the quality of the study design (adequacy) and the scope of examination (validity). The reliability is addressed by the method "Knock-out criteria", which consists of four essential criteria for repeated-dose toxicity studies. A second tool, termed QUANTOS (Quality Assessment of Non-guideline Toxicity Studies), evaluates and weights the adequacy of the study by using intra-criterion and inter-criteria weighting. Finally, the Coverage approach calculates a probability that the detected Lowest-Observed-Effect-Level (LOEL) is similar to the LOEL of a guideline study dependent on the examined targets and organs of the non-guideline study. If the validity and adequacy of the non-guideline study are insufficient for risk assessment, the ITS proposes to apply category approach or the Threshold of Toxicological Concern (TTC) concept, and only as a last resort new animal-testing.

Species Sensitivity Distribution estimation from uncertain (QSAR-based) effects data.

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-in data 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.

Read-across estimates of aquatic toxicity for selected fragrances.

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.

Towards optimization of chemical testing under REACH: a Bayesian network approach to Integrated Testing Strategies.

Integrated Testing Strategies (ITSs) are considered tools for guiding resource efficient decision-making on chemical hazard and risk management. Originating in the mid-nineties from research initiatives on minimizing animal use in toxicity testing, ITS development still lacks a methodologically consistent framework for incorporating all relevant information, for updating and reducing uncertainty across testing stages, and for handling conditionally dependent evidence. This paper presents a conceptual and methodological proposal for improving ITS development. We discuss methodological shortcomings of current ITS approaches, and we identify conceptual requirements for ITS development and optimization. First, ITS development should be based on probabilistic methods in order to quantify and update various uncertainties across testing stages. Second, reasoning should reflect a set of logic rules for consistently combining probabilities of related events. Third, inference should be hypothesis-driven and should reflect causal relationships in order to coherently guide decision-making across testing stages. To meet these requirements, we propose an information-theoretic approach to ITS development, the "ITS inference framework", which can be made operational by using Bayesian networks. As an illustration, we examine a simple two-test battery for assessing rodent carcinogenicity. Finally, we demonstrate how running the Bayesian network reveals a quantitative measure of Weight-of-Evidence.

Tracing simultaneous cadmium accumulation from different uptake routes in brown crab Cancer pagurus by the use of stable isotopes.

High concentrations of cadmium in brown crab are an issue of food safety, and large variations between different areas have been found. To investigate the relative importance of dietary and aqueous uptake regarding the overall accumulation in brown crab, we used stable isotopes to trace the uptake from both routes simultaneously in the same animals. We demonstrated that the analytical challenges regarding background concentrations of natural isotope distribution and polyatomic interferences in the different matrices can be overcome with an appropriate analytical setup and modern mathematical corrections using a computer software. Cadmium was accumulated via both routes and was found in all measured organs at the end of the exposure phase. The obtained data were used to establish accumulation curves for both uptake routes and estimate accumulation parameters for hepatopancreas, as the most important organ in crab regarding total cadmium body burden. Using the estimated parameters in combination with naturally relevant cadmium concentrations in seawater and diet in a model, allowed us to predict the relative importance of the aqueous and dietary uptake route to the total hepatopancreas burden. According to the prediction, the dietary route is the main route of uptake in brown crab with a minimum of 98% of the accumulated cadmium in hepatopancreas originating from diet. Future studies addressing the source and accumulation of cadmium in crab should therefore focus on the uptake from feed and factors connected to foraging.

QSAR applicabilty domain estimation by projection of the training set descriptor space: a review.

As the use of Quantitative Structure Activity Relationship (QSAR) models for chemical management increases, the reliability of the predictions from such models is a matter of growing concern. The OECD QSAR Validation Principles recommend that a model should be used within its applicability domain (AD). The Setubal Workshop report provided conceptual guidance on defining a (Q)SAR AD, but it is difficult to use directly. The practical application of the AD concept requires an operational definition that permits the design of an automatic (computerised), quantitative procedure to determine a models AD. An attempt is made to address this need, and methods and criteria for estimating AD through training set interpolation in descriptor space are reviewed. It is proposed that response space should be included in the training set representation. Thus, training set chemicals are points in n-dimensional descriptor space and m-dimensional model response space. Four major approaches for estimating interpolation regions in a multivariate space are reviewed and compared: range, distance, geometrical, and probability density distribution.

Limitations of current risk characterization methods in probabilistic environmental risk assessment.

In probabilistic environmental risk assessment, the likelihood and the extent of adverse effects occurring in ecological systems because of exposure(s) to substances are estimated. It is based on the comparison of an exposure/environmental concentration distribution, with a species sensitivity distribution derived from toxicity data. The calculation of a probabilistic risk can be performed in many ways (e.g., area under the curve in joint probability curves). However, several (hypothetical) examples and some theoretical considerations illustrate that the current risk characterisation methods have an integrative character and they focus on the statistical comparison of two distributions without properly considering the environmental interpretation of these underlying distributions. Several scenarios with varying exposure/environmental concentration distribution and species sensitivity distribution standard deviations are discussed.

A freshwater food web model for the combined effects of nutrients and insecticide stress and subsequent recovery.

A microcosm experiment that addressed the interaction between eutrophication processes and contaminants was analyzed using a food web model. Both direct and indirect effects of nutrient additions and a single insecticide application (chlorpyrifos) on biomass dynamics and recovery of functional groups were modeled. Direct toxicant effects on sensitive arthropods could be predicted reasonably well using concentration-response relationships from the laboratory with representative species. Model predictions showed that nutrient additions alone caused only small effects on toxicant fate and effects probably due to the relatively high dissipation rate of chlorpyrifos. Enhancement of eutrophication effects by the insecticide was relatively small and seemed to be additive. The recovery of some affected functional groups was hampered in the indoor microcosms due to their isolation from outdoor seed populations. Introducing recolonization scenarios in the model simulated dose-dependent recovery. Recolonization increased the recovering rate after exposure to the pesticide. Modeling can extend the use of microcosms as a link between laboratory and field as this allows the prediction of effects and recovery of ecosystems for concentrations that have not been experimentally tested.