Determination of benchmark concentrations and their statistical uncertainty for cytotoxicity test data and functional in vitro assays
| Autor: | Marcel Leist, Manuel Pastor, Giorgia Pallocca, Franziska Kappenberg, Jan Mellert, Alice Krebs, Johanna Nyffeler, Christiaan Karreman, Jörg Rahnenführer, Béla Z. Schmidt |
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| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Test battery Animal Use Alternatives Cytotoxicity test Cell Survival Concentration-response curve Value (computer science) Benchmark calculation Research & Experimental Medicine VALIDATION 03 medical and health sciences 0302 clinical medicine DESIGN FUTURE FOOD ddc:570 Statistics Toxicity Tests Animals Mathematics Pharmacology Science & Technology In vitro toxicology Uncertainty General Medicine Function (mathematics) IDENTIFY THOUGHT Confidence interval Medical Laboratory Technology Benchmarking 030104 developmental biology Medicine Research & Experimental TOXICANTS 030220 oncology & carcinogenesis Data Interpretation Statistical Screening Benchmark (computing) Curve fitting Test methods Life Sciences & Biomedicine |
| Zdroj: | ALTEX |
| ISSN: | 1868-8551 |
| Popis: | 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. This work was supported by the BMBF, EFSA, the DK-EPA, and the DFG (Konstanz Research School of Chemical Biology; KoRS-CB). It has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements No. 681002 (EU-ToxRisk) and No. 825759 (ENDpoiNTs). We are grateful to collaborators R. von Hellfeld and T. Braunbeck (University of Heidelberg), and H. Vrieling and J. Boei (Leiden University Medical Center) of the EU-ToxRisk con-sortium for providing the experimental data. We are indebted to S. Förster for triggering this work and to other colleagues for in-sightful discussions. |
| Databáze: | OpenAIRE |
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