Cellular Metabolism in High-Throughput In Vitro Reporter Gene Assays and Implications for the Quantitative In Vitro–In Vivo Extrapolation

Fischer, Fabian C.; Abele, Cedric; Henneberger, Luise; Klüver, Nils; König, Maria; Mühlenbrink, Marie; Schlichting, Rita; Escher, Beate I.

doi:10.1021/acs.chemrestox.0c00037

Cited by 19 publications

(32 citation statements)

References 54 publications

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“…One weakness is that metabolism is not accounted for or is merely integrated in the toxicodynamic parameter of SR baseline . Generally, in vitro reporter gene assays are considered not to be metabolically active; but, as has been recently demonstrated, one of the cell lines applied in the present study, AREc32, expresses Cyp1A1 after induction with B[a]p (Fischer et al 2020).…”

Section: Resultsmentioning

confidence: 99%

Effect‐Based Trigger Values for Mixtures of Chemicals in Surface Water Detected with In Vitro Bioassays

Escher

Neale

2021

Enviro Toxic and Chemistry

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Effect-based trigger (EBT) values for in vitro bioassays are important for surface water quality monitoring because they define the threshold between acceptable and poor water quality. EBTs have been derived for highly specific This article is protected by copyright. All rights reserved. Accepted Article bioassays, such as hormone-receptor activation in reporter gene bioassays, by reading across from existing chemical guideline values. This read-across method is not easily applicable to bioassays indicative of adaptive stress responses, which are triggered by many different chemicals, and activation of nuclear receptors for xenobiotic metabolism, to which many chemicals bind with rather low specificity. We propose an alternative approach to define the EBT from the distribution of specificity ratios of all active chemicals. Specificity ratios are the ratio between the predicted baseline toxicity of a chemical in a given bioassay and its measured specific endpoint. Unlike many previous read-across methods to derive EBTs, the proposed method accounts for mixture effects and includes all chemicals, not only high-potency chemicals. The EBTs were derived from a cytotoxicity EBT that was defined as equivalent to 1% of cytotoxicity in a native surface water sample. The cytotoxicity EBT was scaled by the median of the log-normal distribution of specificity ratios to derive the EBT for effects specific for each bioassay. We illustrate the new approach using the example of the AREc32 assay indicative of the oxidative stress response and two nuclear receptor assays targeting the peroxisome proliferator activated receptor PPAR and the arylhydrocarbon receptor AhR. The EBTs were less conservative than previously proposed but were able to differentiate untreated and insufficiently treated wastewater from wastewater treatment plant effluent with secondary or tertiary treatment and surface water.

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Section: Resultsmentioning

confidence: 99%

Effect‐Based Trigger Values for Mixtures of Chemicals in Surface Water Detected with In Vitro Bioassays

Escher

Neale

2021

Enviro Toxic and Chemistry

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“…However, there were problems measuring the concentration of the bases using SPME. Five bases were selected for this work to investigate whether these chemicals are stable for the duration of the assay, as metabolic activity was recently detected in one of the cell lines used . Some of the organic acids (e.g., diclofenac, 2,4-D, ibuprofen, naproxen, and warfarin) showed nonlinear binding to FBS at high concentrations of the chemicals in a previous study .…”

Section: Methodsmentioning

confidence: 99%

Experimental Exposure Assessment of Ionizable Organic Chemicals in In Vitro Cell-Based Bioassays

Huchthausen

Mühlenbrink

König

et al. 2020

Chem. Res. Toxicol.

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Exposure assessment in in vitro cell-based bioassays is challenging for ionizable organic chemicals (IOCs), because they are present as more than one chemical species in the bioassay medium. Furthermore, compared to neutral organic chemicals, their binding to medium proteins and lipids is driven by more complex molecular interactions. Total medium concentrations (C total,medium ) and/or freely dissolved medium concentrations (C free,medium ) were determined for one neutral chemical and 14 IOCs (acids, bases, multifunctional) at concentrations relevant for determination of cytotoxicity and effect. C free,medium was measured in two in vitro bioassays at the time of dosing and after 24 h of incubation using solid-phase microextraction. C free,medium was maximally 1.7 times lower than the nominal concentrations (C nom ) for the hydrophilic chemicals (caffeine and lamotrigine). For the organic acids (naproxen, ibuprofen, warfarin, and diclofenac), C free,medium was by a factor of 4 lower than C nom at high concentrations, but the ratio was much higher at low concentrations, indicating a nonlinear binding behavior. The experimental C free,medium was also compared with C free,medium predicted with a mass balance model accounting for binding to medium proteins and lipids. The mass balance model performed well for five of the test chemicals (within a factor of 10), but it underestimated C free,medium by up to a factor of 1200 for chemicals that showed nonlinear binding to medium components. These findings emphasize that experimental exposure assessment is required for improved understanding of in vitro toxicity data.

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“…One of the earliest known kinetic models is the one developed by Zaldivar and colleagues ( Zaldívar et al, 2010 ; Zaldivar Comenges et al, 2011 ). Another available kinetic mass balance model is by Fischer et al (2020) . While all these kinetic mass balance models predict medium and intracellular concentration over time, there is a general lack of model evaluation in relation to the growing number and diversity of chemicals that have been tested in in vitro systems.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Mass Balance Modeling for Chemical Distribution Over Time in In Vitro Systems With Repeated Dosing

et al. 2022

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As toxicologists and risk assessors move away from animal testing and more toward using in vitro models and biological modeling, it is necessary to produce tools to quantify the chemical distribution within the in vitro environment prior to extrapolating in vitro concentrations to human equivalent doses. Although models predicting chemical distribution in vitro have been developed, very little has been done for repeated dosing scenarios, which are common in prolonged experiments where the medium needs to be refreshed. Failure to account for repeated dosing may lead to inaccurate estimations of exposure and introduce bias into subsequent in vitro to in vivo extrapolations. Our objectives were to develop a dynamic mass balance model for repeated dosing in in vitro systems; to evaluate model accuracy against experimental data; and to perform illustrative simulations to assess the impact of repeated doses on predicted cellular concentrations. A novel dynamic in vitro partitioning mass balance model (IV-MBM DP v1.0) was created based on the well-established fugacity approach. We parameterized and applied the dynamic mass balance model to single dose and repeat dosing scenarios, and evaluated the predicted medium and cellular concentrations against available empirical data. We also simulated repeated dosing scenarios for organic chemicals with a range of partitioning properties and compared the in vitro distributions over time. In single dose scenarios, for which only medium concentrations were available, simulated concentrations predicted measured concentrations with coefficients of determination (R2) of 0.85–0.89, mean absolute error within a factor of two and model bias of nearly one. Repeat dose scenario simulations displayed model bias <2 within the cell lysate, and ∼1.5-3 in the medium. The concordance between simulated and available experimental data supports the predictive capacity of the IV-MBM DP v1.0 tool, but further evaluation as empirical data becomes available is warranted, especially for cellular concentrations.

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Cellular Metabolism in High-Throughput In Vitro Reporter Gene Assays and Implications for the Quantitative In Vitro–In Vivo Extrapolation

Cited by 19 publications

References 54 publications

Effect‐Based Trigger Values for Mixtures of Chemicals in Surface Water Detected with In Vitro Bioassays

Effect‐Based Trigger Values for Mixtures of Chemicals in Surface Water Detected with In Vitro Bioassays

Experimental Exposure Assessment of Ionizable Organic Chemicals in In Vitro Cell-Based Bioassays

Dynamic Mass Balance Modeling for Chemical Distribution Over Time in In Vitro Systems With Repeated Dosing

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