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Determination of benchmark concentrations and their statistical uncertainty for cytotoxicity test data and functional in vitro assays.
Krebs, Alice; Nyffeler, Johanna; Karreman, Christiaan; Schmidt, Béla Z; Kappenberg, Franziska; Mellert, Jan; Pallocca, Giorgia; Pastor, Manuel; Rahnenführer, Jörg; Leist, Marcel.
Afiliación
  • Krebs A; In vitro Toxicology and Biomedicine, Department inaugurated by the Doerenkamp-Zbinden Foundation, University of Konstanz, Konstanz, Germany.
  • Nyffeler J; Konstanz Research School Chemical Biology (KoRS CB), University of Konstanz, Konstanz, Germany.
  • Karreman C; In vitro Toxicology and Biomedicine, Department inaugurated by the Doerenkamp-Zbinden Foundation, University of Konstanz, Konstanz, Germany.
  • Schmidt BZ; present address: Center for Computational Toxicology & Exposure, US EPA, Research Triangle Park, NC, USA.
  • Kappenberg F; In vitro Toxicology and Biomedicine, Department inaugurated by the Doerenkamp-Zbinden Foundation, University of Konstanz, Konstanz, Germany.
  • Mellert J; Switch Laboratory, VIB-KU Leuven Center for Brain & Disease Research, Department of Cellular and Molecular Medicine, Katholieke Universiteit Leuven, Leuven, Belgium.
  • Pallocca G; Department of Statistics, Technical University of Dortmund, Dortmund, Germany.
  • Pastor M; Faculty of Business and Economics, Macroeconomics Dortmund University, Technical University of Dortmund, Dortmund, Germany.
  • Rahnenführer J; CAAT-Europe, University of Konstanz, Konstanz, Germany.
  • Leist M; Research Programme on Biomedical Informatics (GRIB), Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Dept. of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain.
ALTEX ; 37(1): 155-163, 2020.
Article en En | MEDLINE | ID: mdl-31833558
Many toxicological test methods, including assays of cell viability and function, require an evaluation of concentration-response data. This often involves curve fitting, and the resulting mathematical functions are then used to determine the concentration at which a certain deviation from the control value occurs (e.g. a decrease of cell viability by 15%). Such a threshold is called the benchmark response (BMR). For a toxicological test, it is often of interest to determine the concentration of test compound at which a pre-defined BMR of e.g. 10, 25 or 50% is reached. The concentration at which the modelled curve crosses the BMR is called the benchmark concentration (BMC). We present a user-friendly, web-based tool (BMCeasy), designed for operators without programming skills and profound statistical background, to determine BMCs and their confidence intervals. BMCeasy allows simultaneous analysis of viability plus a functional test endpoint, and it yields absolute BMCs with confidence intervals for any BMR. Besides an explanation of the algorithm underlying BMCeasy, this article also gives multiple examples of data outputs. BMCeasy was used within the EU-ToxRisk project for preparing data packages that were submitted to regulatory authorities, demonstrating the real-life applicability of the tool.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Supervivencia Celular / Interpretación Estadística de Datos / Pruebas de Toxicidad / Benchmarking / Incertidumbre Tipo de estudio: Prognostic_studies Límite: Animals Idioma: En Revista: ALTEX Asunto de la revista: MEDICINA Año: 2020 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Alemania
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Supervivencia Celular / Interpretación Estadística de Datos / Pruebas de Toxicidad / Benchmarking / Incertidumbre Tipo de estudio: Prognostic_studies Límite: Animals Idioma: En Revista: ALTEX Asunto de la revista: MEDICINA Año: 2020 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Alemania