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fr
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| Locale | Domain | Times used | Message ID | Message Preview |
|---|---|---|---|---|
| fr | messages | 1 | menu.presentation | Présentation |
| fr | messages | 1 | menu.analyse_bmd | Analyse BMD |
| fr | messages | 1 | menu.aide | Aide |
| fr | messages | 1 | presentation.full_text | <div class=WordSection1> <h1>1.<span> </span>Introduction</h1> <h2>1.1.<span> </span>Contexte</h2> <p class=MsoNormal>Dans le cadre de la prévention des risques liés à des émissions accidentelles dans l'atmosphère de substances chimiques dangereuses, les gestionnaires de risques souhaitent disposer de valeurs seuils de toxicité aiguë par inhalation. Ces valeurs sont utilisées pour la modélisation de phénomènes toxiques dangereux dans le cadre des études de dangers. Les résultats des études de dangers sont principalement utilisés pour la maitrise du risque sur le site, pour la maitrise de l’urbanisation et l'élaboration de plans d'urgence. </p> <p class=MsoNormal>La méthodologie de détermination des valeurs seuils de toxicité aiguë françaises en cas d’émission accidentelle de substances chimiques dans l’atmosphère encadre le développement de ces valeurs. Ainsi, elles sont généralement modélisées à partir de données expérimentales. Le logiciel Probit BMD a donc été conçu par l'Ineris pour évaluer les seuils de toxicité aiguë en situation accidentelle des substances chimiques. Il s’agit d’une application web dont le noyau de calcul permet de modéliser des données binaires (telles que la mortalité, la présence ou absence d’effets irréversibles (brûlures graves, cécité, anomalies congénitales…) ou d’effets réversibles (irritation, inconscience passagère…) obtenues pour une ou plusieurs durées d'exposition et pour une ou plusieurs concentrations, afin de calculer la BenchMark Dose (BMD) ainsi que la limite inférieure de son intervalle de confiance, la BenchMark Dose Limit (BMDL). </p> <p class=MsoNormal>Le modèle log-probit utilisé, reconnu pour sa pertinence dans la modélisation des données de mortalité, ajusté par maximisation de la vraisemblance, est décrit dans la section 2.</p> <p class=MsoNormal></p> <h1>2.<span> </span>Le modèle log-probit</h1> <p class=MsoNormal>Plusieurs études ont été réalisées afin de comparer les performances statistiques de différents modèles à partir de données réelles et de données simulées (Péry et al., 2010; Ineris, 2012). Le modèle probit standard s’est avéré être le plus performant, en termes de description des données et d’estimation de paramètres. Ce dernier a donc été retenu pour la détermination des valeurs seuils de toxicité aiguë françaises en cas d’émission accidentelle de substances chimiques dans l’atmosphère.</p> <h2>2.1.<span> </span>Conditions de modélisation </h2> <p class=MsoNormal>La modélisation log-probit de données correspondant à une seule durée d’exposition est effectuée si les conditions suivantes sont réunies : </p> <p class=MsoListParagraphCxSpFirst> -<span > </span>au moins 3 doses ou concentrations ont été testées, </p> <p class=MsoListParagraphCxSpLast> -<span > </span>au moins un taux de mortalité est compris en 0 et 100%. </p> <p class=MsoNormal>La modélisation log-probit de données correspondant à plusieurs durées d’exposition est effectuée si les conditions suivantes sont réunies : </p> <p class=MsoListParagraphCxSpFirst> -<span > </span>au moins 4 couples concentration/temps ont été testés, </p> <p class=MsoListParagraphCxSpMiddle> -<span > </span>au moins une concentration a été testée pour plusieurs durées d’exposition ou au moins une durée d’exposition a été testée à plusieurs concentrations, </p> <p class=MsoListParagraphCxSpLast> -<span > </span>au moins 2 couples concentration/temps présentent un taux de mortalité compris en 0 et 100%. </p> <p class=MsoNormal>Ces conditions sont nécessaires mais non suffisantes pour considérer que les données peuvent servir à calculer une BMD valide.</p> <h2>2.2.<span> </span>Utilisation du modèle en fonction des durées d’exposition</h2> <h3>2.2.1.<span> </span>Modèle pour une durée d’exposition unique</h3> <p class=MsoNormal><span >À une concentration donnée, le nombre d'animaux présentant une réponse toxique est supposé être distribué de manière binomiale. Le modèle mathématique utilisé est le modèle log-probit. Ce modèle a été retenu dans une étude comparative de Péry et al. (2010) pour sa capacité à décrire la distribution de la sensibilité dans une population.</span></p> <p class=MsoNormal><span>La probabilité d'une réponse toxique peut être exprimée sous la forme </span><span lang=EN-GB >α</span><span>+</span><span lang=EN-GB>β</span><span>×log(C), où </span><span lang=EN-GB>α</span><span > et </span><span lang=EN-GB>β</span><span > sont les paramètres (</span><span lang=EN-GB >β</span><span> est appelé paramètre de pente) et C est la concentration. La probabilité d'une réponse toxique suit une distribution standard normale cumulative.</span></p> <h3>2.2.2.<span> </span>Modèle pour plusieurs durées d’exposition</h3> <p class=MsoNormal><span >Le modèle pour une durée d'exposition a été adapté pour modéliser les données recueillies lors d'essais expérimentaux réalisés pour différentes durées d'exposition.</span></p> <p class=MsoNormal><span >Comme décrit dans Péry et al. (2010) et la version bêta de BMDS 2.1.2 (Davis et al., 2011), le modèle log-probit a intégré la loi de Haber étendue (ten Berge et al., 1986), qui considère que l'effet de l'exposition à la concentration C pendant une période de temps t est fonction de C<sup>n</sup>t, appelé «niveau d'effet fixe» (Jarabek, 1995), où n est la constante de Haber.</span></p> <p class=MsoNormal><span >La probabilité d'une réponse toxique peut donc être exprimée sous la forme </span><span lang=EN-GB>α</span><span >+</span><span lang=EN-GB>β</span><span >×log(C<sup>n</sup>×t) et suit une distribution standard normale cumulative.</span></p> <h2>2.3.<span> </span>Calculs de BMD et BMDL</h2> <p class=MsoNormal>L’estimation de la BMDL associée à la BMD permet d’évaluer la fiabilité de la BMD correspondant à un ensemble de données expérimentales, il s’agit de la limite inférieure de l’intervalle de confiance déterminé à partir de la log vraisemblance. L’équation de Haber, qui permet l’extrapolation de l’effet d’une concentration à des durées différentes, a été intégrée dans le modèle afin de permettre la modélisation des données correspondant à plusieurs durées d’exposition. Lorsqu’une seule durée d’exposition a été testée, l’extrapolation des BMD et des BMDL est donc effectuée en fixant n=1 pour les durées d’exposition plus longues que la durée testée et n=3 pour les durées d’exposition plus courtes. Notre implémentation du modèle log-probit suppose que la mortalité est nulle dans les lots témoins. </p> <h3>2.3.1.<span> </span>Méthode d’estimation</h3> <p class=MsoNormal><a name="_Hlk135655217"><span >Les paramètres du modèle α et β, et éventuellement n, sont estimés par la méthode du maximum de vraisemblance.</span></a></p> <p class=MsoNormal><span >La BMD est calculée pour une durée d'exposition donnée en fonction des paramètres du modèle. C'est la concentration qui aurait une incidence de x%, la BenchMark Response (BMR).</span></p> <p class=MsoNormal><span>La limite inférieure de l’intervalle de confiance à 95 % (1-α) de la BMD (BMDL) est basée sur la statistique du rapport de log-vraisemblance et est calculée à l'aide d'une optimisation sous contrainte, en déterminant la plus petite valeur de BMD telle que le log-vraisemblance est égale au maximum de vraisemblance moins 1,35 (Χ<sub>(1-2α, 1)</sub>²) (intervalle de confiance unilatéral) comme dans le logiciel BMDS version 2.1.2 (US EPA (Environmental Protection Agency), 2008).</span></p> <h3><a name="_Hlk135655357">2.3.2.<span> </span>Extrapolation à d’autres durées d’exposition non testées expérimentalement</a></h3> <h4><span>➣<span> </span></span>Modèle pour une durée d’exposition unique</h4> <p class=MsoNormal><span>Les valeurs BMD et BMDL peuvent être calculées pour plusieurs durées d'exposition, en utilisant la loi de Haber étendue, avec n=1 pour des durées d'exposition plus longues que celle testée expérimentalement, et n=3 pour des durées d'exposition plus courtes que celle testée.</span></p> <h4><span>➣<span> </span></span>Modèle pour plusieurs durées d’exposition</h4> <p class=MsoNormal><span>La BMD et la BMDL peuvent être estimées pour des durées d'exposition qui n'ont pas été testées expérimentalement, mais doivent être utilisées avec précaution lorsqu'elles sont hors de la plage testée.</span></p> <h1>3.<span> </span>L’interface du logiciel Probit BMD</h1> <h2>3.1.<span> </span>Présentation</h2> <p class=MsoNormal>Le logiciel Probit BMD est à destination des toxicologues et des gestionnaires de risques. Il est d’utilisation libre, peut être utilisé sans connaissances préalables en programmation informatique et permet le calcul des BMD pour de nouvelles données en un temps de calcul acceptable pour l’utilisateur. </p> <p class=MsoNormal>Le noyau de calcul estime les valeurs des paramètres du modèle mathématique à partir des données expérimentales saisies par l’utilisateur. Ces données associées à l’expérimentation correspondent à la concentration, la durée d’exposition, le nombre d’incidences et le nombre d’observations. L’utilisateur peut soit les saisir manuellement sur l’interface, soit charger un fichier de données. L’utilisateur doit également spécifier si les données correspondent à une seule durée d’exposition constante (commune pour toutes les modalités d’exposition) ou à plusieurs durées d’exposition : dans ce cas, la durée d’exposition est prise en compte dans les calculs. </p> <p class=MsoNormal>L’application affiche ensuite les estimations des paramètres du modèle ainsi que leurs intervalles de confiance, le graphique de la courbe effet-dose modélisée avec ces mêmes paramètres, la Benchmark dose (BMD) et la Benchmark dose Limit (BMDL).</p> <p class=MsoNormal>L’application permet d’imprimer ces résultats dans un fichier PDF avec les caractéristiques des données expérimentales pour une utilisation ultérieure (incorporation dans un rapport). </p> <h2>3.2.<span> </span>Les différents onglets du logiciel</h2> <p class=MsoNormal>Le logiciel présente 3 onglets différents:«Présentation», « Analyse BMD »et «Aide».</p> <p class=MsoNormal>L’onglet «Présentation» décrit brièvement le modèle, les calculs et les estimations, et les données requises pour réaliser les modélisations.</p> <p class=MsoNormal>L’onglet «Analyse BMD » est l’onglet sur lequel les modélisations peuvent être réalisées. Plusieurs types de modélisations peuvent être réalisées (une ou plusieurs durées d’exposition) et via différentes méthodes de renseignement des données (formulaire en ligne ou importation d’un fichier de données au format CSV ou TXT).</p> <p class=MsoNormal>Enfin, l’onglet «Aide» décrit le format à respecter pour l’import de données via des fichiers CSV ou TXT ainsi qu’une explication des messages d’erreur qui pourraient être rencontrés lors des modélisations.</p> <p class=MsoNormal>Le logiciel Probit BMD est disponible en langue française et anglaise. La langue est automatiquement sélectionnée selon la langue choisie dans les paramètres du navigateur internet.</p> <h2>3.3.<span> </span>Données d’entrée et de sortie </h2> <h3>3.3.1.<span> </span>Données d’entrée </h3> <p class=MsoNormal>L’utilisateur est appelé à renseigner les informations suivantes (cf. figure 1 et figure 2) : </p> <p class=MsoListParagraph> -<span> </span>nom de la substance analysée (facultatif), </p> <p class=MsoListParagraph> -<span> </span>espèce utilisée (facultatif),</p> <p class=MsoListParagraph> -<span> </span>référence de l’étude (facultatif), </p> <p class=MsoListParagraph> -<span> </span><span>le nombre d'animaux dans chaque groupe,</span></p> <p class=MsoListParagraph> -<span> </span><span>la concentration d'exposition de chaque groupe,</span></p> <p class=MsoListParagraph> -<span> </span><span>la durée </span><span>d’exposition de chaque groupe,</span></p> <p class=MsoListParagraph> -<span> </span><span>le nombre d'animaux présentant une réponse toxique après exposition dans chaque groupe,</span></p> <p class=MsoListParagraph> -<span > </span>si les données correspondent à une seule ou à plusieurs durée(s) d'exposition (case à cocher),</p> <p class=MsoListParagraph> <span>o<span > </span></span>dans le cas une seule durée : si la BMD doit être extrapolée aux durées d’exposition par défaut (case à cocher) et dans ce cas la durée d’exposition des données, </p> <p class=MsoListParagraph> <span>o<span > </span></span>dans le cas plusieurs durées : si la BMD doit être estimée aux durées d’exposition par défaut (case à cocher) et durées (supplémentaires si la case précédente est cochée) auxquelles la BMD doit être estimée (facultatif). </p> <p class=MsoListParagraph> -<span > </span>si les données expérimentales sont entrées par saisie de données (formulaire html) ou téléchargement d’un fichier (case à cocher), </p> <p class=MsoListParagraph> <span>o<span > </span></span>nombre de données dans le cas de la saisie de données (il s’agit du nombre de lignes du formulaire HTML), </p> <p class=MsoListParagraph> <span>o<span > </span></span>chemin du fichier dans le cas d’un téléchargement,</p> <p class=MsoListParagraph> -<span > </span>seuils d'effets auxquels la BMD sera estimée, sous forme de cases à cocher (1 %, 5 %, 10 % et<br> 50 %). </p> <p class=MsoListParagraph> -<span > </span>paramètres graphiques : </p> <p class=MsoListParagraph> <span>o<span > </span></span>si l’échelle de l’axe des abscisses doit être logarithmique (valeur par défaut), </p> <p class=MsoListParagraph> <span>o<span> </span></span>affichage des barres d’erreurs (intervalles de confiance de la loi binomiale selon la méthode de Clopper-Pearson) sur le graphique des points expérimentaux. </p> <p class=MsoNormal><img width=635 height=483 id="Image 1" src=" data:image/png;base64,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"></p> <p class=MsoCaption align=center><a name="_Ref134543761">Figure </a>1: renseignement des généralités concernant l’étude expérimentale, des conditions d’exposition, du mode de remplissage des données et du type de modélisation souhaité</p> <p class=MsoNormal> <img width=635 height=287 id="Image 2" src=" data:image/png;base64,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"></p> <p class=MsoCaption align=center><a name="_Ref134543764">Figure </a>2: renseignement des données expérimentales</p> <p class=MsoNormal></p> <h3>3.3.2.<span> </span>Données de sortie </h3> <p class=MsoNormal><a name="_Hlk135655980">Les données de sortie sont les suivantes : </a></p> <p class=MsoListParagraph> -<span > </span>nom de la substance, </p> <p class=MsoListParagraph> -<span > </span>log vraisemblance maximale, </p> <p class=MsoListParagraph> -<span > </span>indicateur de convergence de la modélisation de la relation dose-réponse, </p> <p class=MsoListParagraph> -<span > </span>résidu de chaque point expérimental, </p> <p class=MsoListParagraph> -<span > </span>statistique de test de Pearson, </p> <p class=MsoListParagraph> -<span > </span>goodness-of-fit associée, </p> <p class=MsoListParagraph> -<span > </span>tableau des estimations des paramètres du modèle et intervalles de confiance associés,</p> <p class=MsoListParagraph> -<span > </span>graphique de l’ensemble des points expérimentaux,</p> <p class=MsoListParagraph> -<span > </span>dans le cas de la modélisation à plusieurs durées : </p> <p class=MsoListParagraph> <span>o<span > </span></span>graphiques de la dose-réponse pour les points expérimentaux correspondant à chaque durée d’exposition à laquelle une estimation de la BMD est effectuée, </p> <p class=MsoListParagraph> <span>o<span > </span></span>valeurs de BMD et BMDL avec écart de log vraisemblance pour les durées demandées spécifiquement par l’utilisateur et pour chaque BMR, </p> <p class=MsoListParagraph> <span>o<span > </span></span>valeurs de BMD et BMDL pour chaque BMR pour les durées calculées par défaut. </p> <p class=MsoListParagraph> -<span > </span>dans le cas de la modélisation à une seule durée : </p> <p class=MsoListParagraph> <span>o<span > </span></span>valeur de BMD et BMDL pour chaque BMR,</p> <p class=MsoListParagraph> <span>o<span > </span></span>valeurs de BMD et BMDL extrapolées aux autres durées d’exposition, si l’utilisateur les a demandées. </p> <p class=MsoListParagraph> -<span > </span>avertissements générés par le logiciel le cas échéant.</p> <p class=MsoNormal></p> <h1>4.<span> </span>Références</h1> <p><strong><span lang=EN-US >Davis JA, Gift JS, Zhao QJ.</span></strong><span lang=EN-US>Introduction to benchmark dose methods and U.S. EPA's benchmark dose software (BMDS) version 2.1.1. </span><span >Toxicology and Applied Pharmacology 2011; 254: 181-191.</span></p> <p><b><span>Ineris.</span></b><span > Calcul de BenchMark Doses (BMD) en modélisation à une seule durée et à plusieurs durées ; étude de l’incertitude autour de la BMD par analyse statistique de données de survie. Rapport d'étude DRC-12-126177-08038A, 2012.</span></p> <p><b><span>Ineris.</span></b><span > Développement d’une application informatique pour le calcul de BenchMark Doses ; application à des données de survie. </span><span lang=EN-US >Rapport d'étude DRC-12-126177-00404A, 2012.</span></p> <p><strong><span lang=EN-US >Jarabek A.</span></strong><span lang=EN-US>Consideration of temporal toxicity challenges current default assumptions. Inhalation toxicology 1995; 7: 927-946.</span></p> <p><strong><span lang=EN-US >Péry ARR, Troise A, Tissot S, Vincent JM.</span></strong><span lang=EN-US >Comparison of models to analyze mortality data and derive concentration–time response relationship of inhaled chemicals. Regulatory Toxicology and Pharmacology 2010; 57: 124–128.</span></p> <p><strong><span lang=EN-US >ten Berge WF, Zwart A, Appelman LM.</span></strong><span lang=EN-US>Concentration—time mortality response relationship of irritant and systemically acting vapours and gases. Journal of Hazardous Materials 1986; 13: 301-309.</span></p> <p><strong><span lang=EN-US >US EPA (Environmental Protection Agency).</span></strong><span lang=EN-US >Benchmark Dose Software (BMDS), 2008.</span></p> </div> |
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