The Scientific Committee (SC) reconfirms that the benchmark dose (BMD) approach is a scientifically more advanced method compared to the NOAEL approach for deriving a Reference Point (RP). Most of the modifications made to the SC guidance of 2009 concern the section providing guidance on how to apply the BMD approach. Model averaging is recommended as the preferred method for calculating the BMD confidence interval, while acknowledging that the respective tools are still under development and may not be easily accessible to all. Therefore, selecting or rejecting models is still considered as a suboptimal alternative. The set of default models to be used for BMD analysis has been reviewed, and the Akaike information criterion (AIC) has been introduced instead of the log-likelihood to characterise the goodness of fit of different mathematical models to a dose-response data set. A flowchart has also been inserted in this update to guide the reader step-by-step when performing a BMD analysis, as well as a chapter on the distributional part of dose-response models and a template for reporting a BMD analysis in a complete and transparent manner. Finally, it is recommended to always report the BMD confidence interval rather than the value of the BMD. The lower bound (BMDL) is needed as a potential RP, and the upper bound (BMDU) is needed for establishing the BMDU/BMDL per ratio reflecting the uncertainty in the BMD estimate. This updated guidance does not call for a general re-evaluation of previous assessments where the NOAEL approach or the BMD approach as described in the 2009 SC guidance was used, in particular when the exposure is clearly smaller (e.g. more than one order of magnitude) than the health-based guidance value. Finally, the SC firmly reiterates to reconsider test guidelines given the expected wide application of the BMD approach.

The Scientific Committee (SC) reconfirms that the benchmark dose (BMD) approach is a scientifically more advanced method compared to the no‐observed‐adverse‐effect‐level (NOAEL) approach for deriving a Reference Point (RP). The major change compared to the previous Guidance (EFSA SC, 2017) concerns the Section 2.5, in which a change from the frequentist to the Bayesian paradigm is recommended. In the former, uncertainty about the unknown parameters is measured by confidence and significance levels, interpreted and calibrated under hypothetical repetition, while probability distributions are attached to the unknown parameters in the Bayesian approach, and the notion of probability is extended to reflect uncertainty of knowledge. In addition, the Bayesian approach can mimic a learning process and reflects the accumulation of knowledge over time. Model averaging is again recommended as the preferred method for estimating the BMD and calculating its credible interval. The set of default models to be used for BMD analysis has been reviewed and amended so that there is now a single set of models for quantal and continuous data. The flow chart guiding the reader step‐by‐step when performing a BMD analysis has also been updated, and a chapter comparing the frequentist to the Bayesian paradigm inserted. Also, when using Bayesian BMD modelling, the lower bound (BMDL) is to be considered as potential RP, and the upper bound (BMDU) is needed for establishing the BMDU/BMDL ratio reflecting the uncertainty in the BMD estimate. This updated guidance does not call for a general re‐evaluation of previous assessments where the NOAEL approach or the BMD approach as described in the 2009 or 2017 Guidance was used, in particular when the exposure is clearly lower (e.g. more than one order of magnitude) than the health‐based guidance value. Finally, the SC firmly reiterates to reconsider test guidelines given the wide application of the BMD approach.

EFSA requested its Scientific Committee to prepare a guidance document providing generic issues and criteria to consider biological relevance, particularly when deciding on whether an observed effect is of biological relevance, i.e. is adverse (or shows a beneficial health effect) or not. The guidance document provides a general framework for establishing the biological relevance of observations at various stages of the assessment. Biological relevance is considered at three main stages related to the process of dealing with evidence: Development of the assessment strategy. In this context, specification of agents, effects, subjects and conditions in relation to the assessment question(s): Collection and extraction of data; Appraisal and integration of the relevance of the agents, subjects, effects and conditions, i.e. reviewing dimensions of biological relevance for each data set. A decision tree is developed to assist in the collection, identification and appraisal of relevant data for a given specific assessment question to be answered. This is an open access article under the terms of the Creative Commons Attribution-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited and no modifications or adaptations are made.The EFSA Journal is a publication of the European Food Safety Authority, an agency of the European Union. SummaryThe European Food Safety Authority (EFSA) requested its Scientific Committee to prepare a guidance document providing generic issues and criteria to consider biological relevance, particularly when deciding on whether an observed effect is of biological relevance, i.e. is adverse (or shows a beneficial health effect) or not.The guidance clarifies a number of definitions and concepts (such as, responses of a biological system to exposure, mode of action and adverse outcome pathways, thresholds, critical effect, modelling approaches, biomarkers), which are central to biological relevance, in order to achieve that these concepts are used in a consistent way across EFSA areas of activity.The list of generic issues (e.g. nature and size of the biological changes or differences, including the relevance of the biological systems where the effects are observed) to consider when deciding on whether an observed effect is biologically relevant should be applicable to all relevant EFSA Scientific Panels and Scientific Committee.A framework was developed in which biological relevance is considered at three main stages related to the process of dealing with evidence. Development of the assessment strategy, should specify agents, effects, subjects and conditions in relation to the assessment question(s). The collection and extraction of data should follow identification of potentially biologically relevant evidence/data as specified in the Assessment strategy. The third step should include an appraisal and an integration of the biological relevance for each data set of the agents, subjects, effects and conditions. With regard to the ag...

This article describes results obtained by testing the European Food Safety Authority-tiered guidance approach for safety assessment of botanicals and botanical preparations intended for use in food supplements. Main conclusions emerging are as follows. (i) Botanical ingredients must be identified by their scientific (binomial) name, in most cases down to the subspecies level or lower. (ii) Adequate characterization and description of the botanical parts and preparation methodology used is needed. Safety of a botanical ingredient cannot be assumed only relying on the long-term safe use of other preparations of the same botanical. (iii) Because of possible adulterations, misclassifications, replacements or falsifications, and restorations, establishment of adequate quality control is necessary. (iv) The strength of the evidence underlying concerns over a botanical ingredient should be included in the safety assessment. (v) The matrix effect should be taken into account in the safety assessment on a case-by-case basis. (vi) Adequate data and methods for appropriate exposure assessment are often missing. (vii) Safety regulations concerning toxic contaminants have to be complied with. The application of the guidance approach can result in the conclusion that safety can be presumed, that the botanical ingredient is of safety concern, or that further data are needed to assess safety.

The feasibility of using a retailer fidelity card scheme to estimate food additive intake was investigated using the Swiss retailer MIGROS's Cumulus Card and the example of the food colour Sunset Yellow (E 110). Information held within the card scheme was used to identify a sample of households purchasing foods containing Sunset Yellow over a 15 day period. A sample of 1204 households was selected for interview, of which 830 households were retained in the study following interview. Interviews were conducted to establish household structure, patterns of consumption by different individuals within the household, and the proportion of foods containing Sunset Yellow habitually purchased at the retailer and/or consumed outside the home. Information provided by the retailer on levels of Sunset Yellow in the foods was combined with the information obtained at interview to calculate the per-capita intake of Sunset Yellow by members of participating households. More than 99% of consumers (n = 1902) of foods containing Sunset Yellow were estimated to consume less than 1 mg Sunset Yellow kg(-1) body weight day(-1). The method proved to be a simple and resource-efficient approach to estimate food additive intake on the basis of actual consumer behaviour and thus reports results more closely related to the actual consumption of foods by individuals.