A robust Bayesian bias-adjusted random effects model for consideration of uncertainty about bias terms in evidence synthesis.

Meta-analysis is a statistical method used in evidence synthesis for combining, analyzing and summarizing studies that have the same target endpoint and aims to derive a pooled quantitative estimate using fixed and random effects models or network models. Differences among included studies depend on variations in target populations (ie, heterogeneity) and variations in study quality due to study design and execution (ie, bias). The risk of bias is usually assessed qualitatively using critical appraisal, and quantitative bias analysis can be used to evaluate the influence of bias on the quantity of interest. We propose a way to consider ignorance or ambiguity in how to quantify bias terms in a bias analysis by characterizing bias with imprecision (as bounds on probability) and use robust Bayesian analysis to estimate the overall effect. Robust Bayesian analysis is here seen as Bayesian updating performed over a set of coherent probability distributions, where the set emerges from a set of bias terms. We show how the set of bias terms can be specified based on judgments on the relative magnitude of biases (ie, low, unclear, and high risk of bias) in one or several domains of the Cochrane's risk of bias table. For illustration, we apply a robust Bayesian bias-adjusted random effects model to an already published meta-analysis on the effect of Rituximab for rheumatoid arthritis from the Cochrane Database of Systematic Reviews.

Bayesian Network Applications for Sustainable Holistic Water Resources Management: Modeling Opportunities for South Africa.

Anthropogenic transformation of land globally is threatening water resources in terms of quality and availability. Managing water resources to ensure sustainable utilization is important for a semiarid country such as South Africa. Bayesian networks (BNs) are probabilistic graphical models that have been applied globally to a range of water resources management studies; however, there has been very limited application of BNs to similar studies in South Africa. This article explores the benefits and challenges of BN application in the context of water resources management, specifically in relation to South Africa. A brief overview describes BNs, followed by details of some of the possible opportunities for BNs to benefit water resources management. These include the ability to use quantitative and qualitative information, data, and expert knowledge. BN models can be integrated into geographic information systems and predict impact of ecosystem services and sustainability indicators. With additional data and information, BNs can be updated, allowing for integration into an adaptive management process. Challenges in the application of BNs include oversimplification of complex systems, constraints of BNs with categorical nodes for continuous variables, unclear use of expert knowledge, and treatment of uncertainty. BNs have tremendous potential to guide decision making by providing a holistic approach to water resources management.

A suggestion for the quantification of precise and bounded probability to quantify epistemic uncertainty in scientific assessments.

An honest communication of uncertainty about quantities of interest enhances transparency in scientific assessments. To support this communication, risk assessors should choose appropriate ways to evaluate and characterize epistemic uncertainty. A full treatment of uncertainty requires methods that distinguish aleatory from epistemic uncertainty. Quantitative expressions for epistemic uncertainty are advantageous in scientific assessments because they are nonambiguous and enable individual uncertainties to be characterized and combined in a systematic way. Since 2019, the European Food Safety Authority (EFSA) recommends assessors to express epistemic uncertainty in conclusions of scientific assessments quantitatively by subjective probability. A subjective probability can be used to represent an expert judgment, which may or may not be updated using Bayes's rule to integrate evidence available for the assessment and could be either precise or approximate. Approximate (or bounded) probabilities may be enough for decision making and allow experts to reach agreement on certainty when they struggle to specify precise subjective probabilities. The difference between the lower and upper bound on a subjective probability can also be used to reflect someone's strength of knowledge. In this article, we demonstrate how to quantify uncertainty by bounded probability, and explicitly distinguish between epistemic and aleatory uncertainty, by means of robust Bayesian analysis, including standard Bayesian analysis through precise probability as a special case. For illustration, the two analyses are applied to an intake assessment.

Robust Decision Analysis under Severe Uncertainty and Ambiguous Tradeoffs: An Invasive Species Case Study.

Bayesian decision analysis is a useful method for risk management decisions, but is limited in its ability to consider severe uncertainty in knowledge, and value ambiguity in management objectives. We study the use of robust Bayesian decision analysis to handle problems where one or both of these issues arise. The robust Bayesian approach models severe uncertainty through bounds on probability distributions, and value ambiguity through bounds on utility functions. To incorporate data, standard Bayesian updating is applied on the entire set of distributions. To elicit our expert's utility representing the value of different management objectives, we use a modified version of the swing weighting procedure that can cope with severe value ambiguity. We demonstrate these methods on an environmental management problem to eradicate an alien invasive marmorkrebs recently discovered in Sweden, which needed a rapid response despite substantial knowledge gaps if the species was still present (i.e., severe uncertainty) and the need for difficult tradeoffs and competing interests (i.e., value ambiguity). We identify that the decision alternatives to drain the system and remove individuals in combination with dredging and sieving with or without a degradable biocide, or increasing pH, are consistently bad under the entire range of probability and utility bounds. This case study shows how robust Bayesian decision analysis provides a transparent methodology for integrating information in risk management problems where little data are available and/or where the tradeoffs are ambiguous.

Estimating effects of arable land use intensity on farmland birds using joint species modeling.

Declines in European farmland birds over past decades have been attributed to the combined effects of agricultural intensification and abandonment. Consequently, aspirations to stop declines should focus attention on reversing these changes through voluntary or policy-driven interventions. The design of such interventions should ideally be informed by scientific knowledge of which aspects of the transformation of agricultural landscapes have contributed to the farmland bird declines. Declines may be associated with loss of natural habitats or the intensification and homogenization of land use management on production land, and furthermore, these changes may interact. Here, we applied an orthogonal design exploiting spatial variation in land use in a major agricultural region of Sweden to seek evidence for benefits to farmland birds of reversing some of the intensifications on and among arable fields and whether effects are modified by the availability of seminatural habitats (pastures and field borders) in the landscape. We accounted for the potentially confounding effect of interactions between species by using a joint species distribution model explicitly controlling for additional variation and covariation among species. We found that interventions aimed specifically at land in production could provide benefits to farmland birds. Landscapes with a higher proportion leys or fallows and/or with a more diverse set of crops held higher abundances of most farmland birds. However, effects were only apparent in landscapes with low availability of seminatural habitats and were sometimes even negative in landscapes with high amounts of such habitats, demonstrating context dependence. Even if we found little evidence of interactions between species, the joint modeling approach provided several benefits. It allowed information to be shared between species making analyses robust to uncertainty due to low abundances and provided direct information about the mean and variability in effects of studied predictors among species. We also found that care needs to be taken regarding prior and distributional assumptions as the importance of species interactions might otherwise be overstated. We conclude that this approach is well suited for evaluating agricultural policies by providing evidence for or against certain interventions or to be linked to policy scenarios of land use change.

Assessment of uncertainty in chemical models by Bayesian probabilities: Why, when, how?

A prediction of a chemical property or activity is subject to uncertainty. Which type of uncertainties to consider, whether to account for them in a differentiated manner and with which methods, depends on the practical context. In chemical modelling, general guidance of the assessment of uncertainty is hindered by the high variety in underlying modelling algorithms, high-dimensionality problems, the acknowledgement of both qualitative and quantitative dimensions of uncertainty, and the fact that statistics offers alternative principles for uncertainty quantification. Here, a view of the assessment of uncertainty in predictions is presented with the aim to overcome these issues. The assessment sets out to quantify uncertainty representing error in predictions and is based on probability modelling of errors where uncertainty is measured by Bayesian probabilities. Even though well motivated, the choice to use Bayesian probabilities is a challenge to statistics and chemical modelling. Fully Bayesian modelling, Bayesian meta-modelling and bootstrapping are discussed as possible approaches. Deciding how to assess uncertainty is an active choice, and should not be constrained by traditions or lack of validated and reliable ways of doing it.

Uncertainty in QSAR predictions.

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.

Arguments for considering uncertainty in QSAR predictions in hazard and risk assessments.

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.

Experimental assessment of the environmental fate and effects of triazoles and benzotriazole.

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.

Evaluation of CADASTER QSAR models for the aquatic toxicity of (benzo)triazoles and prioritisation by consensus prediction.

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.

Transcriptional Responses as Biomarkers of General Toxicity: A Systematic Review and Meta-analysis on Metal-Exposed Bivalves.

Through a systematic review and a series of meta-analyses, we evaluated the general responsiveness of putative transcriptional biomarkers of general toxicity and chemical stress. We targeted metal exposures performed on bivalves under controlled laboratory conditions and selected six transcripts associated with general toxicity for evaluation: catalase, glutathione-S-transferase, heat shock proteins 70 and 90, metallothionein, and superoxide dismutase. Transcriptional responses (n = 396) were extracted from published scientific articles (k = 22) and converted to log response ratios (lnRRs). By estimating toxic units, we normalized different metal exposures to a common scale, as a proxy of concentration. Using Bayesian hierarchical random effect models, we then tested the effects of metal exposure on lnRR, both for metal exposure in general and in meta-regressions using toxic unit and exposure time as independent variables. Corresponding analyses were also repeated with transcript and tissue as additional moderators. Observed patterns were similar for general and for transcript- and tissue-specific responses. The expected overall response to arbitrary metal exposure was an lnRR of 0.50, corresponding to a 65% increase relative to a nonexposed control. However, when accounting for publication bias, the estimated "true" response showed no such effect. Furthermore, expected response magnitude increased slightly with exposure time, but there was little support for general monotonic concentration dependence with regard to toxic unit. Altogether, the present study reveals potential limitations that need consideration prior to applying the selected transcripts as biomarkers in environmental risk assessment. Environ Toxicol Chem 2023;42:628-641. © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Scientific opinion on the renewal of the authorisation of Smoke Concentrate 809045 (SF-003) as a smoke flavouring Primary Product.

The EFSA Panel on Food Additives and Flavourings (FAF) was requested to evaluate the safety of the smoke flavouring Primary Product Smoke Concentrate 809045 (SF-003), for which a renewal application was submitted in accordance with Article 12(1) of Regulation (EC) No 2065/2003. This opinion refers to the assessment of data submitted on chemical characterisation, dietary exposure and genotoxicity of the Primary Product. Product Smoke Concentrate 809045 is obtained by pyrolysis of beech wood. The Panel concluded that the compositional data provided on the Primary Product are adequate. At the maximum proposed use levels, dietary exposure estimates calculated with DietEx ranged from 0.1 to 1.5 mg/kg body weight (bw) per day at the mean and from 0.2 to 5.2 mg/kg bw per day at the 95th percentile. The Panel concluded that eleven components in the Primary Product raise a potential concern for genotoxicity. In addition, a potential concern for genotoxicity was identified for the unidentified part of the mixture. The Primary Product contains furan-2(5H)-one and benzene-1,2-diol, for which a concern for genotoxicity was identified in vivo upon oral administration. Considering that the exposure estimates for these two components are above the threshold of toxicological concern (TTC) of 0.0025 µg/kg bw per day for DNA-reactive mutagens and/or carcinogens, the Panel concluded that the Primary Product raises concern with respect to genotoxicity.

Scientific opinion on the renewal of the authorisation of Zesti Smoke Code 10 (SF-002) as a smoke flavouring Primary Product.

The EFSA Panel on Food Additives and Flavourings (FAF) was requested to evaluate the safety of the smoke flavouring Primary Product Zesti Smoke Code 10 (SF-002), for which a renewal application was submitted in accordance with Article 12(1) of Regulation (EC) No 2065/2003. This opinion refers to the assessment of data submitted on chemical characterisation, dietary exposure and genotoxicity of the Primary Product. Zesti Smoke Code 10 is obtained by pyrolysis of hickory and oak woods. Given the limitations of the quantification approach employed by the applicant, the Panel could not judge whether the applied methods meet the legal quality criterion that at least 80% of the volatile fraction shall be identified and quantified. At the maximum proposed use levels, dietary exposure estimates calculated with DietEx ranged from 0.02 to 4.6 mg/kg body weight (bw) per day at the mean and from no dietary exposure to 13.0 mg/kg bw per day at the 95th percentile. The Panel concluded that four components in the Primary Product raise a potential concern for genotoxicity. In addition, a potential concern for genotoxicity was identified for the unidentified part of the mixture. The Primary Product contains furan-2(5H)-one and benzene-1,2-diol, for which a concern for genotoxicity was identified in vivo upon oral administration. Considering that the exposure estimates for these two components are above the threshold of toxicological concern (TTC) of 0.0025 µg/kg bw per day for DNA-reactive mutagens and/or carcinogens, the Panel concluded that the Primary Product raises concern with respect to genotoxicity.

Scientific opinion on the renewal of the authorisation of Fumokomp (SF-009) as a smoke flavouring Primary Product.

The EFSA Panel on Food Additives and Flavourings (FAF) was requested to evaluate the safety of the smoke flavouring Primary Product Fumokomp (SF-009), for which a renewal application was submitted in accordance with Article 12(1) of Regulation (EC) No 2065/2003 (in the renewal application the Primary Product is reported as 'Fumokomp Conc.'). This opinion refers to an assessment of data submitted on chemical characterisation, dietary exposure and genotoxicity of the Primary Product. Fumokomp Conc. is produced by pyrolysis of beech and hornbeam woods. Gas chromatography-mass spectrometry (GC-MS) was applied for both identification and quantification of the volatile constituents of the Primary Product. Given the limitations of the method, the Panel cannot judge with confidence whether the applied method meets the legal quality criterion that at least 80% of the volatile fraction shall be identified and quantified. Moreover, the Panel concluded that the absence of furan-2(5H)-one from the Primary Product was not convincingly demonstrated. At the maximum proposed use levels, dietary exposure estimates calculated with FAIM ranged from 0.04 to 0.9 mg/kg body weight (bw) per day at the mean and from 0.1 to 1.5 mg/kg bw per day at the 95th percentile. The information available on the 32 identified components of the Primary Product, although limited, did not indicate a concern for genotoxicity for any of these substances. However, whole mixture testing in an in vitro mouse lymphoma assay gave positive results which would require an adequate in vivo follow-up study. In addition, the potential for aneugenicity of the Primary Product has not been adequately investigated. Accordingly, the potential safety concern for genotoxicity of the Primary Product cannot be ruled out.

Scientific opinion on the renewal of the authorisation of SmokEz C-10 (SF-005) as a smoke flavouring Primary Product.

The EFSA Panel on Food Additives and Flavourings (FAF) was requested to evaluate the safety of the smoke flavouring Primary Product SmoKEz C-10 (SF-005), for which a renewal application was submitted in accordance with Article 12(1) of Regulation (EC) No 2065/2003. This opinion refers to the assessment of data submitted on chemical characterisation, dietary exposure and genotoxicity of the Primary Product. SmoKEz C-10 is obtained by pyrolysis of maple, oak, hickory, ash, birch, beech and cherry woods. Given the limitations of the quantification approach employed by the applicant, the Panel could not judge whether the applied methods meet the legal quality criterion that at least 80% of the volatile fraction shall be identified and quantified. At the maximum proposed use levels, dietary exposure estimates calculated with DietEx ranged from 0.01 to 5.1 mg/kg body weight (bw) per day at the mean and from no dietary exposure to 18.1 mg/kg bw per day at the 95th percentile. The Panel concluded that five components in the Primary Product raise a potential concern for genotoxicity. In addition, a potential concern for genotoxicity was identified for the unidentified part of the mixture. The Primary Product contains furan-2(5H)-one and benzene-1,2-diol, for which a concern for genotoxicity was identified in vivo upon oral administration. Considering that the exposure estimates for these two components are above the threshold of toxicological concern (TTC) of 0.0025 µg/kg bw per day for DNA-reactive mutagens and/or carcinogens, the Panel concluded that the Primary Product raises concern with respect to genotoxicity.

Scientific opinion on the renewal of the authorisation of SmokEz Enviro-23 (SF-006) as a smoke flavouring Primary Product.

The EFSA Panel on Food Additives and Flavourings (FAF) was requested to evaluate the safety of the smoke flavouring Primary Product SmokEz Enviro-23 (SF-006), for which a renewal application was submitted in accordance with Article 12(1) of Regulation (EC) No 2065/2003. This opinion refers to the assessment of data submitted on chemical characterisation, dietary exposure and genotoxicity of the Primary Product. SmokEz Enviro-23 is obtained by pyrolysis of oak, maple, hickory, ash, birch, beech and cherry woods. Given the limitations of the quantification approach employed by the applicant, the Panel could not judge whether the applied methods meet the legal quality criterion that at least 80% of the volatile fraction shall be identified and quantified. At the maximum proposed use levels, dietary exposure estimates calculated with DietEx ranged from 0.01 to 3.2 mg/kg body weight (bw) per day at the mean and from no dietary exposure to 9.5 mg/kg bw per day at the 95th percentile. The Panel concluded that four components in the Primary Product raise a potential concern for genotoxicity. In addition, a potential concern for genotoxicity was identified for the unidentified part of the mixture. The Primary Product contains furan-2(5H)-one and benzene-1,2-diol, for which a concern for genotoxicity was identified in vivo upon oral administration. Considering that the exposure estimates for these two components are above the threshold of toxicological concern (TTC) of 0.0025 µg/kg bw per day for DNA-reactive mutagens and/or carcinogens, the Panel concluded that the Primary Product raises concern with respect to genotoxicity.

Scientific opinion on the renewal of the authorisation of Scansmoke SEF7525 (SF-004) as a smoke flavouring Primary Product.

The EFSA Panel on Food Additives and Flavourings (FAF) was requested to evaluate the safety of the smoke flavouring Primary Product Scansmoke SEF7525 (SF-004), for which a renewal application was submitted in accordance with Article 12(1) of Regulation (EC) No 2065/2003. This opinion refers to the assessment of data submitted on chemical characterisation, dietary exposure and genotoxicity of the Primary Product. Scansmoke SEF7525 is obtained from a tar produced from a mixture of red oak, white oak, maple, beech and hickory. Based on the compositional data, the Panel noted that the identified and quantified proportion of the solvent-free fraction amounts to 32.6 weight (wt)%, thus the applied method does not meet the legal quality criterion that at least 50% of the solvent-free fraction shall be identified and quantified. At the maximum proposed use levels, dietary exposure estimates calculated with Food Additive Intake Model (FAIM) ranged from 0.6 to 3.8 mg/kg body weight (bw) per day at the mean and from 1.1 to 10.1 mg/kg bw per day at the 95th percentile. Based on the available information on genotoxicity on 44 identified components, the Panel concluded that two substances in the Primary Product, styrene and benzofuran, raise a potential concern for genotoxicity. In addition, a potential concern for genotoxicity was identified for the unidentified part of the mixture. Considering that the exposure estimates for styrene and benzofuran are above the threshold of toxicological concern (TTC) value of 0.0025 kg/kg bw per day for DNA-reactive mutagens and/or carcinogens and since further data are needed to clarify their potential genotoxicity, the Panel concluded that the potential safety concern for genotoxicity of the Primary Product cannot be ruled out.

Scientific opinion on the renewal of the authorisation of proFagus Smoke R714 (SF-001) as a smoke flavouring Primary Product.

The EFSA Panel on Food Additives and Flavourings (FAF) was requested to evaluate the safety of the smoke flavouring Primary Product proFagus Smoke R714 (SF-001), for which a renewal application was submitted in accordance with Article 12(1) of Regulation (EC) No 2065/2003. This opinion refers to the assessment of data submitted on chemical characterisation, dietary exposure and genotoxicity of the Primary Product. ProFagus Smoke R714 is obtained by pyrolysis of beech and oak woods as main source materials. Based on the compositional data, the Panel noted that the identified and quantified proportion of the solvent-free fraction amounts to 39 weight (wt)%, thus the applied method does not meet the legal quality criterion that at least 50% of the solvent-free fraction shall be identified and quantified. At the maximum proposed use levels, dietary exposure estimates calculated with DietEx ranged from 0.7 to 10.9 mg/kg body weight (bw) per day at the mean and from 2.2 to 42.5 mg/kg bw per day at the 95th percentile. The Panel concluded that three components in the Primary Product raise a potential concern for genotoxicity. In addition, a potential concern for genotoxicity was identified for the unidentified part of the mixture. The Primary Product contains furan-2(5H)-one, for which a concern for genotoxicity was identified in vivo upon oral administration. Considering that the exposure estimates for this component are above the threshold of toxicological concern (TTC) of 0.0025 µg/kg bw per day for DNA-reactive mutagens and/or carcinogens, the Panel concluded that the Primary Product raises concern with respect to genotoxicity.

Scientific opinion on the renewal of the authorisation of proFagus Smoke R709 (SF-008) as a smoke flavouring Primary Product.

The EFSA Panel on Food Additives and Flavourings (FAF) was requested to evaluate the safety of the smoke flavouring Primary Product proFagus Smoke R709 (SF-008), for which a renewal application was submitted in accordance with Article 12(1) of Regulation (EC) No 2065/2003. This opinion refers to the assessment of data submitted on chemical characterisation, dietary exposure and genotoxicity of the Primary Product. ProFagus Smoke R709 is obtained by pyrolysis of beech and oak wood as main source materials. The panel concluded that the compositional data provided on the Primary Product are adequate. At the maximum proposed use levels, dietary exposure estimates calculated with DietEx ranged from 0.8 to 12.2 mg/kg body weight (bw) per day at the mean and from 2.3 to 51.4 mg/kg bw per day at the 95th percentile. The Panel concluded that three components in the Primary Product raise a potential concern for genotoxicity. In addition, a potential concern for genotoxicity was identified for the unidentified part of the mixture. The Primary Product contains furan-2(5H)-one, for which a concern for genotoxicity was identified in vivo upon oral administration. Considering that the exposure estimates for this component are above the TTC of 0.0025 µg/kg bw per day for DNA-reactive mutagens and/or carcinogens, the panel concluded that the Primary Product raises concern with respect to genotoxicity.

PLS-optimal: a stepwise D-optimal design based on latent variables.

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.