Handling deviating control values in concentration-response curves

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dc.contributor.authorKappenberg, Franziska
dc.contributor.authorBrecklinghaus, Tim
dc.contributor.authorAlbrecht, Wiebke
dc.contributor.authorBlum, Jonathan
dc.contributor.authorvan der Wurp, Carola
dc.contributor.authorLeist, Marcel
dc.contributor.authorHengstler, Jan G.
dc.contributor.authorRahnenführer, Jörg
dc.date.accessioned2021-05-28T11:27:51Z
dc.date.available2021-05-28T11:27:51Z
dc.date.issued2020-09-23
dc.description.abstractIn 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.en
dc.identifier.urihttp://hdl.handle.net/2003/40227
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-22100
dc.language.isoende
dc.relation.ispartofseriesArchives of toxicology;94
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectConcentration-response curveen
dc.subjectDose-response curveen
dc.subjectViability assayen
dc.subjectDeviating controlsen
dc.subject4pLL modelen
dc.subjectSimulation studyen
dc.subject.ddc310
dc.titleHandling deviating control values in concentration-response curvesen
dc.typeTextde
dc.type.publicationtypearticlede
dcterms.accessRightsopen access
eldorado.secondarypublicationtruede
eldorado.secondarypublication.primarycitationKappenberg, F., Brecklinghaus, T., Albrecht, W. et al. Handling deviating control values in concentration-response curves. Arch Toxicol 94, 3787–3798 (2020).de
eldorado.secondarypublication.primaryidentifierhttps://doi.org/10.1007/s00204-020-02913-0de

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Statistische Methoden in der Genetik und Chemometrie