New approach methods in chemicals safety decision-making – Are we on the brink of transformative policy-making and regulatory change?

IF 3.1 Q2 TOXICOLOGY
Camilla Alexander-White
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Abstract

Decision-making on the use and management of chemicals in society is on the brink of a scientific and technological revolution. At the same time world politics is focusing more on chemicals, waste and pollution prevention, alongside climate change and biodiversity loss. To enable effective decision-making, policy makers and regulators will need to draw upon the best scientific evidence available on the real-life causation and consequences of adverse effects of chemical and waste exposures affecting humans, wildlife and the environment. New Approach Method (NAM) data from modern day multidisciplinary science and technology is becoming more available using cheminformatics, computational prediction algorithms using AI, transcriptomics, genomics, proteomics, mathematical modelling, epidemiology, biological monitoring, and clinical science. Current chemical regulation has been shaped by the animal models of the 20th century. NAMs and Next Generation Risk Assessment (NGRA) have the potential to better support innovations in chemicals and materials through science-informed decision making that is more species-relevant and protective of adverse outcomes; this will require future-proofed regulatory transformation. Capacity building and skills development in computational and in vitro NAMs will be key to this transformation.

化学品安全决策中的新方法--我们是否正处于变革性决策和监管变化的边缘?
社会中有关化学品使用和管理的决策正处于科技革命的边缘。与此同时,世界政治也更加关注化学品、废物和污染预防,以及气候变化和生物多样性的丧失。为了做出有效的决策,政策制定者和监管者需要借鉴现有的最佳科学证据,以了解化学品和废物暴露对人类、野生动植物和环境造成不良影响的现实因果关系。利用化学信息学、使用人工智能的计算预测算法、转录组学、基因组学、蛋白质组学、数学建模、流行病学、生物监测和临床科学,现代多学科科学和技术的新方法(NAM)数据正变得越来越可用。目前的化学品监管是由 20 世纪的动物模型形成的。通过科学决策,NAMs 和下一代风险评估 (NGRA) 有可能更好地支持化学品和材料的创新。计算和体外 NAM 方面的能力建设和技能发展将是这一转变的关键。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Computational Toxicology
Computational Toxicology Computer Science-Computer Science Applications
CiteScore
5.50
自引率
0.00%
发文量
53
审稿时长
56 days
期刊介绍: Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs
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