FSA research and evidence., Journal Year: 2024, Volume and Issue: unknown
Published: Sept. 10, 2024
Language: Английский
Toxicology Research, Journal Year: 2024, Volume and Issue: 13(2)
Published: March 1, 2024
Abstract New approach methodologies (NAMs) can deliver improved chemical safety assessment through the provision of more protective and/or relevant models that have a reduced reliance on animals. Despite widely acknowledged benefits offered by NAMs, there continue to be barriers prevent or limit their application for decision-making in assessment. These include related real and perceived scientific, technical, legislative economic issues, as well cultural societal obstacles may relate inertia, familiarity, comfort with established methods, perceptions around regulatory expectations acceptance. This article focuses science, exposure, hazard, risk assessment, explores nature these how they overcome drive wider exploitation acceptance NAMs. Short-, mid- longer-term goals are outlined embrace opportunities provided NAMs protection human health environmental security part new paradigm incorporates exposure science culture promotes use toxicological assessments.
Language: Английский
Citations
32
Frontiers in Toxicology, Journal Year: 2025, Volume and Issue: 7
Published: Feb. 12, 2025
The paradigm shift brought about by artificial intelligence (AI) across scientific disciplines has been nothing short of revolutionary. From unraveling the mysteries protein folding to enabling autonomous systems, AI demonstrated its potential tackle previously intractable problems (Jumper et. al., 2021;Abramoff et 2023). In toxicology, this transformation arrives at a crucial moment, as we face mounting challenges in chemical safety assessment and an urgent need reduce reliance on animal testing (Hartung, 2023a,b;Kleinstreuer Hartung, 2024). This Research Topic emerged from recognition that while computational toxicology made significant strides using classical approaches such physiologically-based pharmacokinetic (PBPK) modeling quantitative structure-activity relationships (QSAR), full modern techniques remains largely untapped toxicological risk assessment. recent advances machine learning, particularly deep natural language processing, semantic interoperability, offer unprecedented opportunities integrate diverse data sources create more predictive models for human health outcomes.The five contributions showcase innovative bridge traditional methods with cutting-edge applications. Collectively, these works demonstrate enhance our understanding hazards advancing development efficient, ethical, human-relevant strategies.Instability" review highlights AI/ML OMICS technologies transform assessments predict genotoxicity mutagenicity higher accuracy. It discusses how can establish biomarkers signatures early cancer detection, assessment, monitoring impacts exposure. Additionally, it emphasizes may accelerate screening chemicals evaluation, optimizing resource use, reducing testing. A common thread throughout is emphasis open science principles reproducibility. authors have shared their code, data, methodologies through public repositories, others build upon work. commitment transparency collaboration exemplifies bringing not only problem-solving but also way conduct share research.Looking ahead, several emerge works. First, shows promise predicting endpoints, integrating predictions into regulatory frameworks hurdle (Hartung Kleinstreuer, 2025).Second, quality accessibility training continue be limiting factors developing robust models. Finally, there pressing standardized validate AI-driven toxicology.Nevertheless, presented are moving closer establishing true probabilistic framework (Maertens 2022(Maertens , 2024) incorporates AI. Such could provide accurate effects.The integration multiple streams -from structures historical vitro assays, omics measurements -through represents promising path forward.We hope will serve both inspiration practical researchers working intersection toxicology. As advance methods, move
Language: Английский
Citations
1
Environment International, Journal Year: 2025, Volume and Issue: 197, P. 109370 - 109370
Published: March 1, 2025
The vast amount of registered chemicals leads to a high diversity substances occurring in the environment and creation new outpaces chemical risk assessment as well monitoring strategies. Hence, strategies need be modified ensuring that they remain aligned with rapid development marketing substances. Here we performed longitudinal chronic mixture considering real-world case study scenario diverse anthropogenic impact types characterised by different land uses along river Central Germany. We sampled water using large-volume solid phase extraction at six selected sampling sites. Following analysis liquid chromatography-high resolution mass spectrometry, quantified 192 For 34 % them, obtained empirical effect data for freshwater organisms. Furthermore, used open-source artificial intelligence (AI) model TRIDENT predict toxicity all A multi-scenario was conducted three taxonomic groups, concentration-addition concept various hazard exposure scenarios. results showed estimates groups were considerably higher when amended from silico modelling. identified hotspots pollution our indicated fish most vulnerable group, pharmaceuticals being relevant drivers. Our exemplifies application an AI aquatic organisms combination consideration multiple scenarios may complement future
Language: Английский
Citations
1
npj Digital Medicine, Journal Year: 2025, Volume and Issue: 8(1)
Published: April 10, 2025
Abstract Trust is key in AI for regulatory science, but its definition debated. If models use different features yet perform similarly, which should be trusted? scientific theories must testable, how critical explainability? At the Global Summit on Regulatory Science (GSRS24), regulators agreed that successful adoption requires ongoing dialogue, adaptability, and AI-trained personnel to harness potential responsibilities evolving 21st-century landscape.
Language: Английский
Citations
1
Metabolites, Journal Year: 2024, Volume and Issue: 14(2), P. 98 - 98
Published: Jan. 31, 2024
Metabolomics is emerging as a powerful systems biology approach for improving preclinical drug safety assessment. This review discusses current applications and future trends of metabolomics in toxicology development. can elucidate adverse outcome pathways by detecting endogenous biochemical alterations underlying toxicity mechanisms. Furthermore, enables better characterization human environmental exposures their influence on disease pathogenesis. approaches are being increasingly incorporated into studies pharmacology evaluations to gain mechanistic insights identify early biomarkers toxicity. However, realizing the full potential regulatory decision making requires robust demonstration reliability through quality assurance practices, reference materials, interlaboratory studies. Overall, shows great promise strengthening understanding toxicity, enhancing routine screening, transforming exposure risk assessment paradigms. Integration with computational, vitro, personalized medicine innovations will shape predictive toxicology.
Language: Английский
Citations
9
ALTEX, Journal Year: 2024, Volume and Issue: unknown
Published: Jan. 1, 2024
Developmental neurotoxicity (DNT) testing has seen enormous progress over the last two decades. Preceding even publication of animal-based OECD test guideline for DNT in 2007, a series non-animal technology workshops and conferences (starting 2005) shaped community that delivered comprehensive battery vitro methods (IVB). Its data interpretation is covered by very recent guidance (No. 377). Here, we aim to overview field, focusing on evolution strategies, role emerging technologies, impact guidelines testing. In particular, this an example targeted development animal-free approach one most complex hazards chemicals human health. These developments started literally from blank slate, with no proposed alternative available. Over decades, cutting-edge science enabled design spares animals enables throughput challenging hazard. While it evident field needs regulation, massive economic decreased cognitive capacity caused chemical exposure should be prioritized more highly. Beyond this, claim fame scientific brought understanding brain, its development, how can perturbed. Plain language summaryDevelopmental predicts hazard brain development. Comprehensive advanced strategies using now replace approaches assess large numbers accurately efficiently than approach. Recent formalized DNT, marking pivotal achievement field. The shift towards reflects both commitment animal welfare growing recognition public health impacts associated impaired function exposures. innovations ultimately contribute safer management better protection health, especially during vulnerable stages
Language: Английский
Citations
8
RSC Sustainability, Journal Year: 2024, Volume and Issue: 2(11), P. 3464 - 3477
Published: Jan. 1, 2024
Safe and sustainable chemicals/materials are critical for achieving European green goals. The novel SSbD framework aims to harmonize assessments during innovation. Here, we discuss the essential role of FAIR data tools in operationalizing SSbD.
Language: Английский
Citations
6
Current Opinion in Toxicology, Journal Year: 2025, Volume and Issue: unknown, P. 100517 - 100517
Published: Jan. 1, 2025
Language: Английский
Citations
0
Ecotoxicology and Environmental Safety, Journal Year: 2025, Volume and Issue: 292, P. 117973 - 117973
Published: Feb. 27, 2025
Language: Английский
Citations
0
Public Health Toxicology, Journal Year: 2025, Volume and Issue: 5(1), P. 1 - 3
Published: Feb. 28, 2025
Language: Английский
Citations
0