Model-based estimation of lowest observed effect concentration from replicate experiments to identify potential biomarkers of in vitro neurotoxicity

Calderazzo, Silvia; Tavel, Denise; Zurich, Marie-Gabrielle; Kopp‐Schneider, Annette

doi:10.1007/s00204-019-02520-8

Cited by 2 publications

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“…While all analyses were conducted under the assumption of homogeneity, the application of the methods is also possible in the case of heteroscedasticity or non-normal residuals ( Calderazzo et al , 2019 ). Heteroscedasticity and skewed distributions can also be addressed using robust standard errors as in the case of robust linear regression ( Venables and Ripley, 2002 ).…”

Section: Discussionmentioning

confidence: 99%

Comparison of observation-based and model-based identification of alert concentrations from concentration–expression data

et al. 2021

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Motivation An important goal of concentration-response studies in toxicology is to determine an ’alert’ concentration where a critical level of the response variable is exceeded. In a classical observation-based approach, only measured concentrations are considered as potential alert concentrations. Alternatively, a parametric curve is fitted to the data that describes the relationship between concentration and response. For a prespecified effect level, both an absolute estimate of the alert concentration and an estimate of the lowest concentration where the effect level is exceeded significantly are of interest. Results In a simulation study for gene expression data, we compared the observation-based and the model-based approach for both absolute and significant exceedance of the prespecified effect level. Results show that, compared to the observation-based approach, the model-based approach overestimates the true alert concentration less often and more frequently leads to a valid estimate, especially for genes with large variance. Availability and Implementation The code used for the simulation studies is available via the GitHub repository: https://github.com/FKappenberg/Paper-IdentificationAlertConcentrations Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Discussionmentioning

confidence: 99%

Comparison of observation-based and model-based identification of alert concentrations from concentration–expression data

et al. 2021

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“…As the BMC method uses the information of the whole dataset, including the data uncertainties (i.e., variability produced by experimental errors and fluctuations of experimental parameters), it is less influenced by concentration spacing than a NOAEL, and it provides an estimate of statistical uncertainty of a point of departure (PoD). It is therefore a preferred method for modern toxicological approaches (Hartung and Leist, 2008;Leist et al, 2010Leist et al, , 2012Leist et al, , 2014, and the concept has been used for a number of algorithms to define non-cytotoxic concentrations (Stadnicka-Michalak et al, 2018;Hsieh et al, 2019;Calderazzo et al, 2019;Behl et al, 2015). Several software tools have been developed to calculate the BMC (Filer et al, 2017;Slob and Setzer, 2014;Ritz and Streibig, 2005) 1,2 , but applying the BMC concept still requires considerably more mathematical/ statistical and programming skills than the NOAEL/LOAEL approach and can be challenging for experimental biologists.…”

Section: Introductionmentioning

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

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Nyffeler

Karreman

et al. 2019

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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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Model-based estimation of lowest observed effect concentration from replicate experiments to identify potential biomarkers of in vitro neurotoxicity

Cited by 2 publications

References 30 publications

Comparison of observation-based and model-based identification of alert concentrations from concentration–expression data

Comparison of observation-based and model-based identification of alert concentrations from concentration–expression data

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

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