Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 13

The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.

Determination of benchmark concentrations and their statistical uncertainty for cytotoxicity test data and functional in vitro assays

Krebs, Alice·2019·ALTEX
DOI 10.14573/altex.1912021
Open this paper in CoChat — free account

Abstract

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.14573/altex.1912021

Cites

33 of 33 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig. 1 The benchmark concentration concept for in vitro toxicology data
Fig. 1 The benchmark concentration concept for in vitro toxicology dataCC-BY · From the PDF
Fig. 2 Graphical representation of the absolute versus the relative BMC50
Fig. 2 Graphical representation of the absolute versus the relative BMC50CC-BY · From the PDF
Image 3
CC-BY · From the PDF
Fig. 3 Overview of the different models to fit concentrationresponse data
Fig. 3 Overview of the different models to fit concentrationresponse dataCC-BY · From the PDF
Image 5
CC-BY · From the PDF
Image 6
CC-BY · From the PDF

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 33 references

Cited by

Metadata

Authors
Krebs, A.
Venue
ALTEX
Publisher
ALTEX Edition
Published
2019
ISSN
1868-596X
DOI
10.14573/altex.1912021
Cited by
13
References
33

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.