Human biomonitoring and toxicokinetics as key building blocks for next generation risk assessment

Reale, Elena; Zare Jeddi, Maryam; Paini, Alicia; Connolly, Alison; Duca, Radu; Cubadda, Francesco; Benfenati, Emilio; Bessems, Jos; S. Galea, Karen; Dirven, Hubert; Santonen, Tiina; M. Koch, Holger; Jones, Kate; Sams, Craig; Viegas, Susana; Kyriaki, Machera; Campisi, Luca; David, Arthur; Antignac, Jean-Philippe; B Hopf, Nancy

doi:10.1016/j.envint.2024.108474

Cited by 6 publications

(4 citation statements)

References 136 publications

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“…43 campaigns. For example, PBK may help in determining the most appropriate HBM matrix to monitor (e.g., whole blood, blood serum, urine) as well as the frequency and duration of the sampling (Reale et al, 2024). The following gaps were identified in the field of HBM:…”

Section: Data and Knowledge Gaps For Hbm Datamentioning

confidence: 99%

“…• In AEA, HBM development is seen in conjunction with exposure, PBK and in silico modelling, to optimise the design of the HBM campaign for chemicals where proper biomarkers are not identified (pertinent to gap D.3). This point becomes particularly evident in the case of pesticides, where the same biomarker of exposure might originate from different parent compounds, complicating the accurate estimation of external exposure levels and the total margins of exposure for the associated parent compounds (Reale et al, 2024). • Feasibility of an assessment methodology based on HBM data to be investigated, together with the need for harmonised methods for deriving HBM guidance values across different regulatory silos (gap D.4).…”

Section: Data and Knowledge Gaps For Hbm Datamentioning

confidence: 99%

“…Reverse dosimetry (purple boxes in Figure 6) is the estimation of the aggregate internal dose that could have caused an observed biomarker level (Tan et al, 2010). This dose is compared to the aggregate dose predicted by the exposure model (Reale et al, 2024). The estimates of aggregate dose from the exposure models can be estimates of the upper bound of the range of aggregate doses generated by deterministic models in the screening assessment or higher-tiered assessments.…”

mentioning

confidence: 99%

“…dosimetry to reconstruct exposure distributions (Reale et al, 2024). However, these models have several challenges and blockers.…”

mentioning

confidence: 99%

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Roadmap for action for advancing aggregate exposure to chemicals in the EU

Lamon,

Doyle,

Paini

et al. 2024

EFSA Supporting Publications

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The European Food Safety Authority (EFSA) has a goal to efficiently conduct aggregate exposure assessments (AEAs) for chemicals using both exposure models and human biomonitoring (HBM) data by 2030. To achieve EFSA's vision, a roadmap for action for advancing aggregate exposure (AE) in the EU was developed. This roadmap was created by performing a series of engagement and data collection activities to map the currently available methods, data, and tools for assessing AE of chemicals, against the needs and priorities of EFSA. This allowed for the creation of a AEA framework, identification of data and knowledge gaps in our current capabilities, and identification of the challenges and blockers that would hinder efforts to fill the gaps. The roadmap identifies interdependent working areas (WAs) where additional research and development are required to achieve EFSA's goal. It also proposes future collaboration opportunities and recommends several project proposals to meet EFSA's goals. Eight proposal projects supported by SWOT analysis are presented for EFSA's consideration. The project proposals inform high‐level recommendations for multi‐annual and multi‐partner projects. Recommendations to improve stakeholder engagement and communication of EFSA's work on AEA were gathered by surveying stakeholders on specific actions to improve EFSA's communication on AE, including webinars, virtual training, social media channels, and newsletters.

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Section: Data and Knowledge Gaps For Hbm Datamentioning

confidence: 99%

Section: Data and Knowledge Gaps For Hbm Datamentioning

confidence: 99%

mentioning

confidence: 99%

“…dosimetry to reconstruct exposure distributions (Reale et al, 2024). However, these models have several challenges and blockers.…”

mentioning

confidence: 99%

See 2 more Smart Citations