P18-63 Development and Implementation of a PBK Modeling Strategy for Next-Generation Risk Assessment in Chemical Food Safety
Next-generation risk assessment (NGRA) is an exposure-driven approach that relies on non-animal methods to evaluate chemical safety. Understanding internal exposure is critical to inform the translation of in vitro toxicity data into human risk assessment. This study presents the development of a ph...
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| Vydané v: | Toxicology letters Ročník 411; s. S214 |
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| Hlavní autori: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier B.V
01.09.2025
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| ISSN: | 0378-4274 |
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| Abstract | Next-generation risk assessment (NGRA) is an exposure-driven approach that relies on non-animal methods to evaluate chemical safety. Understanding internal exposure is critical to inform the translation of in vitro toxicity data into human risk assessment. This study presents the development of a physiologically based kinetic (PBK) modeling strategy specifically designed for food-related chemicals, aiming to predict internal dose metrics for systemic exposure assessment.
A structured approach was developed to parameterize physiologically based kinetic (PBK) models for food-related chemicals, considering oral exposure under different conditions (e.g. liquid vs. solid food matrix) and repeated exposure scenarios. A set of eight representative chemicals – atropine, aldicarb, caffeine, daidzein, genistein, erythritol, epigallocatechin gallate, resveratrol – was selected to test the framework. Model input parameters for physicochemical and absorption, distribution, metabolism, and excretion (ADME) properties were obtained through a tiered approach, integrating in silico predictions and in vitro experimental data. Sensitivity analysis was applied to identify key parameters requiring experimental refinement. The framework also incorporated population variability and uncertainty analysis to generate a range of internal dose predictions.
The performance of the PBK modeling strategy was assessed by comparing predicted systemic concentrations with available human in vivo toxicokinetic data. In vivo data were used exclusively for evaluation, not model calibration, reflecting realistic NGRA scenarios where such data are often unavailable for food-related chemicals. The results suggest that the modeling framework provides adequate estimates of internal exposure, with notable improvements when dissolution kinetics in the gastrointestinal system were explicitly considered.
The developed PBK modeling framework offers a structured approach for NGRA, enabling the integration of in vitro and in silico data to estimate systemic exposure. This strategy supports exposure-based risk assessment of food-related chemicals while reducing reliance on animal studies. Future work will expand the application to a broader set of chemicals and further investigate the role of in vitro digestion data in early-tier assessments. |
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| AbstractList | Next-generation risk assessment (NGRA) is an exposure-driven approach that relies on non-animal methods to evaluate chemical safety. Understanding internal exposure is critical to inform the translation of in vitro toxicity data into human risk assessment. This study presents the development of a physiologically based kinetic (PBK) modeling strategy specifically designed for food-related chemicals, aiming to predict internal dose metrics for systemic exposure assessment.
A structured approach was developed to parameterize physiologically based kinetic (PBK) models for food-related chemicals, considering oral exposure under different conditions (e.g. liquid vs. solid food matrix) and repeated exposure scenarios. A set of eight representative chemicals – atropine, aldicarb, caffeine, daidzein, genistein, erythritol, epigallocatechin gallate, resveratrol – was selected to test the framework. Model input parameters for physicochemical and absorption, distribution, metabolism, and excretion (ADME) properties were obtained through a tiered approach, integrating in silico predictions and in vitro experimental data. Sensitivity analysis was applied to identify key parameters requiring experimental refinement. The framework also incorporated population variability and uncertainty analysis to generate a range of internal dose predictions.
The performance of the PBK modeling strategy was assessed by comparing predicted systemic concentrations with available human in vivo toxicokinetic data. In vivo data were used exclusively for evaluation, not model calibration, reflecting realistic NGRA scenarios where such data are often unavailable for food-related chemicals. The results suggest that the modeling framework provides adequate estimates of internal exposure, with notable improvements when dissolution kinetics in the gastrointestinal system were explicitly considered.
The developed PBK modeling framework offers a structured approach for NGRA, enabling the integration of in vitro and in silico data to estimate systemic exposure. This strategy supports exposure-based risk assessment of food-related chemicals while reducing reliance on animal studies. Future work will expand the application to a broader set of chemicals and further investigate the role of in vitro digestion data in early-tier assessments. |
| Author | Montoya, G. Stroheker, T. Coulet, M. Reale, E. Basili, D. Panchaud, A. Uldry, M. Guitard |
| Author_xml | – sequence: 1 givenname: E. surname: Reale fullname: Reale, E. – sequence: 2 givenname: M. surname: Coulet fullname: Coulet, M. – sequence: 3 givenname: D. surname: Basili fullname: Basili, D. – sequence: 4 givenname: T. surname: Stroheker fullname: Stroheker, T. – sequence: 5 givenname: M. Guitard surname: Uldry fullname: Uldry, M. Guitard – sequence: 6 givenname: A. surname: Panchaud fullname: Panchaud, A. – sequence: 7 givenname: G. surname: Montoya fullname: Montoya, G. |
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| Title | P18-63 Development and Implementation of a PBK Modeling Strategy for Next-Generation Risk Assessment in Chemical Food Safety |
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