先进的自然语言处理在临床药理学中的应用
Joy C Hsu1, Michael Wu2, Chloe Kim2
1Clinical Pharmacology, Genentech, Inc., South San Francisco, California, USA.
Clinical pharmacology and therapeutics
|December 23, 2023
概括
自然语言处理 (NLP) 可以通过有效地从监管文件中提取关键信息来加速药物开发. 这种人工智能驱动的方法支持临床药理学,试验设计和监管提交.
科学领域:
- 人工智能的人工智能
- 计算语言学 计算语言学
- 机器学习 机器学习
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 自然语言处理 (NLP) 越来越多地用于各个行业,但在药物开发中未得到充分利用.
- NLP整合了计算语言学,机器学习和深度学习,用于人类语言处理.
- 目前的药物开发工作流程可以通过先进的NLP技术来增强.
研究的目的:
- 为了证明先进的NLP如何加速药物开发的信息提取和分析.
- 解决临床药理学问题,为临床试验设计提供信息,并支持监管过程.
- 展示NLP在瘤学剂量优化,药理动力学参数分析和PBPK建模中的应用.
主要方法:
- 开发了一个NLP工作流程,涉及数据准备,模型构建和自动提取.
- 使用监管文件,如FDA批准总结基础 (SBA),美国包装插件 (USPI) 和批准信作为源数据.
- 将NLP应用于三个特定的用例:瘤学剂量优化,瘤学中的PK共变量和PBPK分析.
主要成果:
- 先进的NLP成功地加速了从监管文件中提取和分析大型数据集的速度.
- 证明NLP能够解决临床药理学的关键问题.
- 验证了NLP在为临床试验设计提供信息和支持监管审查方面的有用性.
结论:
- 将先进的NLP集成到临床药理学工作流程中可以显著提高效率.
- NLP有助于提取有影响力的信息,这些信息对于推动药物开发至关重要.
- NLP提供了一种强大的工具,可以简化和增强药物开发过程的各个阶段.
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