非临床实验室中的人工智能:增强良好的实验室和文档实践
Khadijah Zai1, Anggoro Praja Mukti2, Gabriella Sabeth Notty2
1Department of Pharmaceutics, Faculty of Pharmacy, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia.
International journal of pharmaceutics
|October 12, 2025
概括
人工智能 (AI) 可以通过提高数据完整性和可追溯性来提高非临床实验室的监管合规性. 这次审查评估了AI.
科学领域:
- 监管科学是一种监管科学.
- 实验室信息学 实验室信息学
- 人工智能应用程序的人工智能应用
背景情况:
- 非临床实验室面临着来自FDA,EMA和MHRA等机构对数据完整性和合规性的越来越多的监管审查.
- 人工智能 (AI) 在科学研究中的现有应用主要集中在临床诊断和药物发现上,在验证和监管调整方面,在非临床实验室环境中进行的探索有限.
- 人工智能工具的技术准备水平 (TRL) 对于非临床实验室合规性尚未明确,需要进行结构化评估.
研究的目的:
- 在非临床实验室中批判性地审查人工智能的应用,以加强良好的实验室实践 (GLP) 和良好的文档实践 (GDP) 合规性.
- 突出人工智能实施的监管层面,包括验证,与ICH指导方针保持一致以及遵守ALCOA+原则.
- 使用TRL映射评估人工智能工具的成熟度,并提出逐步采用的策略.
主要方法:
- 对用于GLP/GDP的异常检测,预测建模,计算机视觉和自然语言处理中的AI应用进行审查.
- 在AI的背景下分析监管框架 (FDA,EMA,ICH Q8-Q14) 和ALCOA+原则.
- 使用TRL映射来区分实验室合规的既定和新兴AI工具.
- 识别人工智能采用障碍,并提出分阶段实施战略.
主要成果:
- 可以部署人工智能应用程序,如用于质量控制的随机森林模型和用于审计轨道的人工智能增强的实验室信息管理系统 (LIMS).
- 早期的人工智能概念,例如用于审计文档的NLP,需要进一步开发.
- 人工智能采用的主要障碍包括系统互操作性问题,劳动力培训差距和高基础设施成本,特别是在低收入和中等收入国家.
- 建议采用包括开源工具,云平台和人工循环监督在内的分阶段采用策略,以提高透明度和监管接受度.
结论:
- 人工智能为提高非临床实验室数据完整性,可追溯性和整体合规性提供了巨大的潜力.
- 将人工智能采用与制药4.0,过程分析技术 (PAT) 和设计质量 (QbD) 联系起来的结构化方法对于成功实施至关重要.
- 通过分阶段采用来解决障碍,并专注于可扩展性和公平访问,将确保人工智能补充现有的实验室质量实践.
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