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      Update: use of the benchmark dose approach in risk assessment

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          Abstract

          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.

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          Most cited references 40

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          A new method for determining allowable daily intakes.

           K S Crump (1984)
          The usual method for establishing allowable daily intake (ADI) for a chemical involves determining a no-observed-effect level (NOEL) and applying a safety factor. Even though this method has been used for many years, there appear to be no general guidelines or rules for defining a NOEL. The determination of a NOEL is particularly uncertain for lesions which occur naturally in untreated animals. NOELs also have shortcomings in that smaller experiments tend to give larger values (this should be reversed because larger experiments can provide greater evidence of safety) and that the steepness of the dose response in the dose range where effects occur plays little or no role in the determination of a NOEL. This paper proposes and illustrates the use of a "benchmark dose" (BD) as an alternative to a NOEL. A BD is a statistical lower confidence limit to a dose producing some predetermined increase in response rate such as 0.01 or 0.1. The BD is calculated using a mathematical dose-response model. This approach makes appropriate use of sample size and the shape of the dose-response curve. The BD normally will not depend strongly upon the mathematical model used because the method does not involve extrapolation far below the experimental range. Thus the method sidesteps much of the model dependency often associated with extrapolation of carcinogenicity data to low doses. The method can be applied to either "quantal" data in which only the presence or absence of an effect is recorded, or "continuous" data in which the severity of the effect is also noted.
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            An Implementation of Bayesian Adaptive Regression Splines (BARS) in C with S and R Wrappers.

            BARS (DiMatteo, Genovese, and Kass 2001) uses the powerful reversible-jump MCMC engine to perform spline-based generalized nonparametric regression. It has been shown to work well in terms of having small mean-squared error in many examples (smaller than known competitors), as well as producing visually-appealing fits that are smooth (filtering out high-frequency noise) while adapting to sudden changes (retaining high-frequency signal). However, BARS is computationally intensive. The original implementation in S was too slow to be practical in certain situations, and was found to handle some data sets incorrectly. We have implemented BARS in C for the normal and Poisson cases, the latter being important in neurophysiological and other point-process applications. The C implementation includes all needed subroutines for fitting Poisson regression, manipulating B-splines (using code created by Bates and Venables), and finding starting values for Poisson regression (using code for density estimation created by Kooperberg). The code utilizes only freely-available external libraries (LAPACK and BLAS) and is otherwise self-contained. We have also provided wrappers so that BARS can be used easily within S or R.
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              A probabilistic approach for deriving acceptable human intake limits and human health risks from toxicological studies: general framework.

              The use of uncertainty factors in the standard method for deriving acceptable intake or exposure limits for humans, such as the Reference Dose (RfD), may be viewed as a conservative method of taking various uncertainties into account. As an obvious alternative, the use of uncertainty distributions instead of uncertainty factors is gaining attention. This paper presents a comprehensive discussion of a general framework that quantifies both the uncertainties in the no-adverse-effect level in the animal (using a benchmark-like approach) and the uncertainties in the various extrapolation steps involved (using uncertainty distributions). This approach results in an uncertainty distribution for the no-adverse-effect level in the sensitive human subpopulation, reflecting the overall scientific uncertainty associated with that level. A lower percentile of this distribution may be regarded as an acceptable exposure limit (e.g., RfD) that takes account of the various uncertainties in a nonconservative fashion. The same methodology may also be used as a tool to derive a distribution for possible human health effects at a given exposure level. We argue that in a probabilistic approach the uncertainty in the estimated no-adverse-effect-level in the animal should be explicitly taken into account. Not only in this source of uncertainty too large to be ignored, it also has repercussions for the quantification of the other uncertainty distributions.
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                Author and article information

                Journal
                EFSA J
                EFSA J
                10.1002/(ISSN)1831-4732
                EFS2
                EFSA Journal
                John Wiley and Sons Inc. (Hoboken )
                1831-4732
                24 January 2017
                January 2017
                : 15
                : 1 ( doiID: 10.1002/efs2.2017.15.issue-1 )
                Author notes
                Article
                EFS24658
                10.2903/j.efsa.2017.4658
                7009819
                © 2017 European Food Safety Authority. EFSA Journal published by John Wiley and Sons Ltd on behalf of European Food Safety Authority.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited and no modifications or adaptations are made.

                Page count
                Figures: 9, Tables: 8, Pages: 41, Words: 21312
                Product
                Categories
                Guidance
                Guidance
                Custom metadata
                2.0
                January 2017
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.7.5 mode:remove_FC converted:21.01.2020

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