2020
DOI: 10.3390/w12123549
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Challenges and Opportunities for Integrating In Silico Models and Adverse Outcomes Pathways to Set and Relate New Biomarkers

Abstract: The Adverse Outcome Pathway (AOP) framework has been considered the most innovative tool to collect, organize, and evaluate relevant information on the toxicological effects of chemicals, facilitating the establishment of links between molecular events and adverse outcomes at the critical level of biological organization. Considering the combination of the high volume of toxicological and ecotoxicological data produced and the application of artificial intelligence algorithms from the last few years, not only … Show more

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Cited by 4 publications
(2 citation statements)
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“…For a BoE to be a reliable tool in HBM studies, it is essential to have strong confidence in the links between BoE and both chemical exposure and health outcomes. In the AOP framework, BoEs tend to coincide with MIEs/KEs between a given exposure and a given adverse outcome ( Matos Dos Santos et al, 2020 ). For BoE identification and/or validation, AOP networks, including feedback loops and modulating factors, are of particular interest, and shared KEs (nodes) are potentially more relevant as they often connect to more MIEs, KEs and/or AOs.…”
Section: Existing Case Studies Of Aop Application In Chemical Hazard ...mentioning
confidence: 99%
“…For a BoE to be a reliable tool in HBM studies, it is essential to have strong confidence in the links between BoE and both chemical exposure and health outcomes. In the AOP framework, BoEs tend to coincide with MIEs/KEs between a given exposure and a given adverse outcome ( Matos Dos Santos et al, 2020 ). For BoE identification and/or validation, AOP networks, including feedback loops and modulating factors, are of particular interest, and shared KEs (nodes) are potentially more relevant as they often connect to more MIEs, KEs and/or AOs.…”
Section: Existing Case Studies Of Aop Application In Chemical Hazard ...mentioning
confidence: 99%
“…This can be a barrier to improving the model performance and applying these models during drug development. There is a problem to solve the “black box” model to make it more interpretable. , Therefore, the mechanistic interpretability and predictivity of the model should be balanced . The utilization of model-agnostic techniques for interpretability, such as LIME, SHAP, and Anchors can provide an explanation for toxicity prediction.…”
Section: Challenges and Future Directions For Ai-based Drug Toxicity ...mentioning
confidence: 99%