Handling deviating control values in concentration-response curves

Autor: Marcel Leist, Carola van der Wurp, Wiebke Albrecht, Franziska Kappenberg, Jörg Rahnenführer, Tim Brecklinghaus, Jan G. Hengstler, Jonathan Blum
Jazyk: angličtina
Rok vydání: 2020
Předmět:
0301 basic medicine
Normalization (statistics)
Simulation study
Health
Toxicology and Mutagenesis
Viability assay
Normal Distribution
Concentration-response curve
010501 environmental sciences
In Vitro Techniques
Toxicology
Concentration-response curve
Dose-response curve
Viability assay
Deviating controls
4pLL model
Simulation study
01 natural sciences
Models
Biological
Cell Line
03 medical and health sciences
ddc:570
Dose-response curve
Statistics
Range (statistics)
Deviating controls
4pLL model
Humans
Computer Simulation
Control (linguistics)
0105 earth and related environmental sciences
Models
Statistical
Concentration Response
Valproic Acid
General Medicine
Function (mathematics)
Replicate
Hep G2 Cells
Data structure
030104 developmental biology
Research Design
Default - option
Bioinformatics and Statistics
Algorithms
Zdroj: Archives of Toxicology
Archives of toxicology, 94:3787-3798
ISSN: 1432-0738
0340-5761
Popis: In cell biology, pharmacology and toxicology dose-response and concentration-response curves are frequently fitted to data with statistical methods. Such fits are used to derive quantitative measures (e.g. EC20 values) describing the relationship between the concentration of a compound or the strength of an intervention applied to cells and its effect on viability or function of these cells. Often, a reference, called negative control (or solvent control), is used to normalize the data. The negative control data sometimes deviate from the values measured for low (ineffective) test compound concentrations. In such cases, normalization of the data with respect to control values leads to biased estimates of the parameters of the concentration-response curve. Low quality estimates of effective concentrations can be the consequence. In a literature study, we found that this problem occurs in a large percentage of toxicological publications. We propose different strategies to tackle the problem, including complete omission of the controls. Data from a controlled simulation study indicate the best-suited problem solution for different data structure scenarios. This was further exemplified by a real concentration-response study. We provide the following recommendations how to handle deviating controls: (1) The log-logistic 4pLL model is a good default option. (2) When there are at least two concentrations in the no-effect range, low variances of the replicate measurements, and deviating controls, control values should be omitted before fitting the model. (3) When data are missing in the no-effect range, the Brain-Cousens model sometimes leads to better results than the default model.
Archives of toxicology;94
Databáze: OpenAIRE
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