降低扣押责任风险:整合不利结果途径 (AOPs),新方法方法 (NAMs) 和In Silico方法,同时突出知识差距
Mamta Behl1, Agnes Karmaus2,3, Mohan Rao1
1Neurocrine Biosciences Inc., San Diego, CA, USA.
概括
这项研究引入了一种新方法,使用不良结果途径 (AOP) 和体外数据来预测化学诱导的发作,改善药物开发和环境安全评估.
科学领域:
- 神经毒理学 神经毒理学
- 药物开发 药物开发
- 环境风险评估环境风险评估
背景情况:
- 预测人类毒性的动物模型,特别是对中枢神经系统 (CNS) 影响 (如发作) 的预测,可靠性有限.
- 由于时间和资源的限制,现有的方法在评估环境化合物方面面临挑战,导致未经测试的化学物质.
- 药物诱导的发作是临床试验中的一个重要问题,突出了需要更好的预测模型.
研究的目的:
- 开发和演示一种新的方法来评估扣押责任,使用不良结果路径 (AOP) 框架和体外数据.
- 为了确定生物标和体外测定终点,相关的扣押机制.
- 为了突出当前针对发作相关神经毒性的化合物查数据缺口.
主要方法:
- 政府与行业的合作结合了AOP和药物发现数据,以确定与扣押机制相关的25个生物目标家族.
- 确定了100多个体外测定终点,涵盖了24个目标家族,包括关键受体,载体和离子通道.
- 为了评估现有数据覆盖范围,对196种诱发发作和34种不发作的参考化合物进行了审查.
主要成果:
- 该研究确定了关键的生物标和100多个相关的内测试终点,用于发作机制.
- 分析揭示了显著的数据差距,只有不到30%的确定的目标被测试为已知的诱发性或非诱发性化合物.
- 这种概念验证证明了使用AOP和体外数据评估机械押责任的可行性.
结论:
- AOP框架与体外数据相结合,提供了一种可靠的方法来评估机械扣押责任.
- 需要扩展查面板,包括额外的查相关目标.
- 这种综合方法增强了化合物优先级,优化了资源使用,完善了中枢神经系统的安全评估,并改善了公共卫生保护.
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