在药量计学中利用大型语言模型:评估NONMEM输出解释和模拟能力
Hwa Jun Cha1,2, Kyuyeon Choe1,2, Euibeom Shin3
1Department of Pharmacology, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-Gu, Seoul, 06591, Korea.
大型语言模型 (LLM) 在药量计学方面表现有前途,Claude 3.5 Sonnet有效生成模型图表和报告. 专家审查对于验证复杂分析中的LLM输出至关重要.
科学领域:
- 药理计量学和计算生物学
背景情况:
- 大型语言模型 (LLM) 越来越多地被探索为它们在专业科学领域的潜在应用.
- 药量计学是一门专注于生物过程定量建模的学科,可以从先进的计算工具中受益.
研究的目的:
- 评估不同大型语言模型 (LLM) 在执行各种药量测量任务中的性能.
- 评估LLM用于生成模型结构图,参数表,分析报告和执行模拟的实用性.
主要方法:
- 四个LLM (Claude 3.5 Sonnet,ChatGPT 4o,Gemini 1.5 Pro,Llama 3.2) 使用44个非MEM输出文件进行了比较.
- 快速工程被应用于Claude用于特定的药量测量任务,并使用ChatGPT进行模拟.
- 开发了一个R Shiny应用程序,用于自动生成图表,表格和报告.
主要成果:
- 对于90.9%的文件,Claude 3.5 Sonnet成功生成了准确的模型结构图,以及一致的参数表和报告.
- 聊天GPT展示了模拟能力,但面临着复杂的药理动力学/药理动力学 (PK/PD) 模型的局限性.
- 从复制提示生成的结构图中观察到可变性,突出显示需要仔细设计提示.
结论:
- 法律学士学位,特别是克劳德3.5索内特,显示了简化和增强关键药理学建模工作流程的巨大潜力.
- 整合LLM可以提高制造药量分析的基本组件的效率.
- 持续的专家监督是必不可少的,以确保LLM产生的药理学输出的准确性和可靠性.
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