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Particle-adapted next-generation risk assessment of micro- and nanoplastics from food contact materials: Integrating PBK/PBTK, AOPs and explainable artificial intelligence.
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This review paper proposes a new framework for assessing health risks from micro- and nanoplastics released by food packaging and containers. It combines computer modeling of how particles move through the body, maps of biological effects, and artificial intelligence tools to help organize evidence and guide future testing, rather than reporting new experimental results.
Micro- and nanoplastics (MNPs) released from food contact materials (FCMs) present a risk-assessment challenge because exposure depends not only on mass concentration but also on particle size, number, morphology, polymer identity, surface properties, aggregation, transformation, and co-migration of additives and non-intentionally added substances. Conventional approaches remain fragmented and are poorly suited to translating FCM-specific particle release into human-relevant internal dosimetry and mechanistic toxicity. This review proposes a particle-adapted next-generation risk assessment (NGRA) framework integrating FCM release characterization, gastrointestinal transformation and absorbed fraction, physiologically based toxicokinetic (PBK/PBTK) modelling, adverse outcome pathways (AOPs), and explainable artificial intelligence (XAI). The framework explicitly defines data hand-offs between modules, including particle-specific exposure metrics, internal particle burden, persistence and clearance parameters, key-event evidence, and model uncertainty. AOPs are used to organize mechanistic evidence rather than infer causality, while XAI is positioned as an interpretability and prioritization tool subject to applicability-domain assessment, external validation, reproducibility, and uncertainty analysis. Attention is given to distinguishing particle-mediated effects from those associated with co-migrating additives and non-intentionally added substances. Regulatory translation is considered in the context of FCM migration assessment and emerging NAM-based approaches. The framework provides a basis for evidence integration, testing prioritization, and future quantitative regulatory assessment of FCM-derived MNPs.
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