将人工智能进步与风险评估需求相结合:迈向有效使用和监管接受的旅程
Serhii Kolesnyk1, Joyce de Paula Souza1, Angela Bearth1
1SCAHT - Swiss Centre for Applied Human Toxicology, Basel, Switzerland; Department of Pharmaceutical Sciences, University of Basel, Switzerland.
Toxicology
|March 5, 2026
概括
人工智能 (AI) 工具对于富有数据的毒理学和下一代风险评估 (NGRA) 领域至关重要. 它们的整合有助于证据管理和化学风险评估 (CRA) 的生成.
科学领域:
- 毒理学 毒理学 毒理学
- 风险评估 风险评估
- 人工智能的人工智能
背景情况:
- 毒理学正在向新方法方法 (NAM) 转变,创建大型,复杂的数据集.
- 这就需要将化学风险评估 (CRA) 演变为下一代风险评估 (NGRA).
- 人工智能 (AI),机器学习 (ML) 和深度学习 (DL) 为数据处理和解释提供了潜在的解决方案.
研究的目的:
- 审查AI在CRA和NGRA当前的应用.
- 将人工智能工具分类为证据管理和证据生成领域.
- 确定需要进一步发展的领域,以便在CRA中接受AI的监管.
主要方法:
- 在化学测试和评估中AI应用的叙述性审查.
- 基于它们在证据管理和生成中的功能对AI工具的分类.
- 分析AI在支持毒理学机制,生物标志物识别,AOP开发和建模方面的作用.
主要成果:
- 人工智能工具可以支持毒理学机制发现和生物标志物识别.
- 人工智能有助于不利结果途径 (AOP) 开发和毒性动力学/毒性动力学过程的建模.
- 人工智能应用涵盖了CRA和NGRA的证据管理和证据生成.
结论:
- 在监管毒理学中,人工智能集成对于管理和解释NAM产生的数据至关重要.
- 在技术验证和制定CRA中AI工具的监管接受标准方面需要进一步进展.
- 人工智能的成功整合将推动下一代风险评估 (NGRA) 的发展.
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