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The regular workshops held by the Center for Alternatives to Animal Testing (CAAT) on biology-inspired microphysiological systems (MPS) taking place every four years, have become a reliable measure to assess fundamental scientific, industrial and regulatory trends for translational science in the MPS-field from a bird's eye view. The 2023 workshop participants at that time concluded that the technology as used within academia has matured significantly, underlined by the broad use of MPS and the steadily increasing number of high quality research publications - yet, broad industry adoption of MPS has been slow, despite strong interest. Academic research using MPS primarily aims to accurately recapitulate human biology in MPS-based organ models in areas where traditional models have been lacking key elements of human physiology, thereby enabling breakthrough discoveries for life sciences.

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On occasion of the DNT5 meeting in Konstanz, Germany (April-2024), participants brainstormed on future challenges concerning a regulatory implementation of the developmental neurotoxicity (DNT) in vitro test battery (DNT-IVB). The five discussion topics below outline some of the key issues, opportunities and research directions for the next several years: (1) How to contextualize DNT hazard with information on potential maternal toxicity or other toxicity domains (non-DNT)? Several approaches on how to use cytotoxicity data from NAMs were discussed. (2) What opportunities exist for an immediate or near-future application of the DNT-IVB, e.

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Toxicological test methods generate raw data and provide instructions on how to use these to determine a final outcome such as a classification of test compounds as hits or non-hits. The data processing pipeline provided in the test method description is often highly complex. Usually, multiple layers of data, ranging from a machine-generated output to the final hit definition, are considered.

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The integration of artificial intelligence (AI) into new approach methods (NAMs) for toxicology rep-resents a paradigm shift in chemical safety assessment. Harnessing AI appropriately has enormous potential to streamline validation efforts. This review explores the challenges, opportunities, and future directions for validating AI-based NAMs, highlighting their transformative potential while acknowledging the complexities involved in their implementation and acceptance.

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