A Toxicokinetic–Toxicodynamic Modeling Workflow Assessing the Quality of Input Mortality Data.
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| Title: | A Toxicokinetic–Toxicodynamic Modeling Workflow Assessing the Quality of Input Mortality Data. |
|---|---|
| Authors: | Bauer, Barbara1 (AUTHOR), Singer, Alexander1 (AUTHOR) alexander.singer@rifcon.de, Gao, Zhenglei2 (AUTHOR), Jakoby, Oliver1 (AUTHOR), Witt, Johannes2 (AUTHOR), Preuss, Thomas2 (AUTHOR), Gergs, André2 (AUTHOR) |
| Source: | Environmental Toxicology & Chemistry. Jan2024, Vol. 43 Issue 1, p197-210. 14p. |
| Subjects: | Bayer AG, Toxicity testing, Ecological risk assessment, Workflow, Environmental chemistry, Plant products, Chronic toxicity testing |
| Abstract: | Toxicokinetic–toxicodynamic (TKTD) models simulate organismal uptake and elimination of a substance (TK) and its effects on the organism (TD). The Reduced General Unified Threshold model of Survival (GUTS‐RED) is a TKTD modeling framework that is well established for aquatic risk assessment to simulate effects on survival. The TKTD models are applied in three steps: parameterization based on experimental data (calibration), comparing predictions with independent data (validation), and prediction of endpoints under environmental scenarios. Despite a clear understanding of the sensitivity of GUTS‐RED predictions to the model parameters, the influence of the input data on the quality of GUTS‐RED calibration and validation has not been systematically explored. We analyzed the performance of GUTS‐RED calibration and validation based on a unique, comprehensive data set, covering different types of substances, exposure patterns, and aquatic animal species taxa that are regularly used for risk assessment of plant protection products. We developed a software code to automatically calibrate and validate GUTS‐RED against survival measurements from 59 toxicity tests and to calculate selected model evaluation metrics. To assess whether specific survival data sets were better suited for calibration or validation, we applied a design in which all possible combinations of studies for the same species–substance combination are used for calibration and validation. We found that uncertainty of calibrated parameters was lower when the full range of effects (i.e., from high survival to high mortality) was covered by input data. Increasing the number of toxicity studies used for calibration further decreased parameter uncertainty. Including data from both acute and chronic studies as well as studies under pulsed and constant exposure in model calibrations improved model predictions on different types of validation data. Using our results, we derived a workflow, including recommendations for the sequence of modeling steps from the selection of input data to a final judgment on the suitability of GUTS‐RED for the data set. Environ Toxicol Chem 2024;43:197–210. © 2023 Bayer AG and The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC. [ABSTRACT FROM AUTHOR] |
| Copyright of Environmental Toxicology & Chemistry is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 174443469 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Toxicokinetic–Toxicodynamic Modeling Workflow Assessing the Quality of Input Mortality Data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Bauer%2C+Barbara%22">Bauer, Barbara</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Singer%2C+Alexander%22">Singer, Alexander</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> alexander.singer@rifcon.de</i><br /><searchLink fieldCode="AR" term="%22Gao%2C+Zhenglei%22">Gao, Zhenglei</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jakoby%2C+Oliver%22">Jakoby, Oliver</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Witt%2C+Johannes%22">Witt, Johannes</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Preuss%2C+Thomas%22">Preuss, Thomas</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gergs%2C+André%22">Gergs, André</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Environmental+Toxicology+%26+Chemistry%22">Environmental Toxicology & Chemistry</searchLink>. Jan2024, Vol. 43 Issue 1, p197-210. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Bayer+AG%22">Bayer AG</searchLink><br /><searchLink fieldCode="DE" term="%22Toxicity+testing%22">Toxicity testing</searchLink><br /><searchLink fieldCode="DE" term="%22Ecological+risk+assessment%22">Ecological risk assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Workflow%22">Workflow</searchLink><br /><searchLink fieldCode="DE" term="%22Environmental+chemistry%22">Environmental chemistry</searchLink><br /><searchLink fieldCode="DE" term="%22Plant+products%22">Plant products</searchLink><br /><searchLink fieldCode="DE" term="%22Chronic+toxicity+testing%22">Chronic toxicity testing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Toxicokinetic–toxicodynamic (TKTD) models simulate organismal uptake and elimination of a substance (TK) and its effects on the organism (TD). The Reduced General Unified Threshold model of Survival (GUTS‐RED) is a TKTD modeling framework that is well established for aquatic risk assessment to simulate effects on survival. The TKTD models are applied in three steps: parameterization based on experimental data (calibration), comparing predictions with independent data (validation), and prediction of endpoints under environmental scenarios. Despite a clear understanding of the sensitivity of GUTS‐RED predictions to the model parameters, the influence of the input data on the quality of GUTS‐RED calibration and validation has not been systematically explored. We analyzed the performance of GUTS‐RED calibration and validation based on a unique, comprehensive data set, covering different types of substances, exposure patterns, and aquatic animal species taxa that are regularly used for risk assessment of plant protection products. We developed a software code to automatically calibrate and validate GUTS‐RED against survival measurements from 59 toxicity tests and to calculate selected model evaluation metrics. To assess whether specific survival data sets were better suited for calibration or validation, we applied a design in which all possible combinations of studies for the same species–substance combination are used for calibration and validation. We found that uncertainty of calibrated parameters was lower when the full range of effects (i.e., from high survival to high mortality) was covered by input data. Increasing the number of toxicity studies used for calibration further decreased parameter uncertainty. Including data from both acute and chronic studies as well as studies under pulsed and constant exposure in model calibrations improved model predictions on different types of validation data. Using our results, we derived a workflow, including recommendations for the sequence of modeling steps from the selection of input data to a final judgment on the suitability of GUTS‐RED for the data set. Environ Toxicol Chem 2024;43:197–210. © 2023 Bayer AG and The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Environmental Toxicology & Chemistry is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/etc.5761 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 197 Subjects: – SubjectFull: Bayer AG Type: general – SubjectFull: Toxicity testing Type: general – SubjectFull: Ecological risk assessment Type: general – SubjectFull: Workflow Type: general – SubjectFull: Environmental chemistry Type: general – SubjectFull: Plant products Type: general – SubjectFull: Chronic toxicity testing Type: general Titles: – TitleFull: A Toxicokinetic–Toxicodynamic Modeling Workflow Assessing the Quality of Input Mortality Data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Bauer, Barbara – PersonEntity: Name: NameFull: Singer, Alexander – PersonEntity: Name: NameFull: Gao, Zhenglei – PersonEntity: Name: NameFull: Jakoby, Oliver – PersonEntity: Name: NameFull: Witt, Johannes – PersonEntity: Name: NameFull: Preuss, Thomas – PersonEntity: Name: NameFull: Gergs, André IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 07307268 Numbering: – Type: volume Value: 43 – Type: issue Value: 1 Titles: – TitleFull: Environmental Toxicology & Chemistry Type: main |
| ResultId | 1 |
