新时代的黎明:机器学习和大型语言模型可以重塑QSP建模吗?
Ioannis P Androulakis1,2, Lourdes Cucurull-Sanchez3,4, Anna Kondic5,4
1Biomedical Engineering, Rutgers University, Piscataway, NJ, USA. yannis@soe.rutgers.edu.
Journal of pharmacokinetics and pharmacodynamics
|June 16, 2025
概括
人工智能 (AI) 和机器学习 (ML) 正在彻底改变定量系统药理学 (QSP) 药物开发. 这些技术增强了建模,实现了个性化医疗,并为更广泛的研究应用民主化了复杂的QSP工作流.
科学领域:
- 药理学和计算生物学
- 药物开发和个性化医学
- 人工智能和机器学习应用程序
背景情况:
- 定量系统药理 (QSP) 对于在药物开发中整合临床前和临床数据至关重要.
- QSP可以进行预测,优化剂量和个性化药物策略.
- 传统的QSP工作流程在数据集成和模型复杂性方面面临挑战.
研究的目的:
- 探索人工智能 (AI) 和机器学习 (ML) 对QSP建模的变革性影响.
- 要突出包括大型语言模型 (LLM) 在内的AI/ML工具如何重塑QSP工作流程.
- 讨论AI和ML在加速药物开发和治疗创新的潜力.
主要方法:
- 在QSP建模中审查和分析AI/ML应用.
- 探索使用AI/ML的自动化文献挖掘和动态模型生成.
- 讨论混合机械-ML模型和LLMs在QSP民主化中的作用.
主要成果:
- 人工智能/ML工具促进了增强的数据提取,自动化文献挖掘和动态模型生成.
- 在QSP中,LLM正在成为积极的合作伙伴,为没有深度编码专业知识的研究人员降低障碍.
- 正在开发混合模型,将机械洞察力与数据驱动方法相结合.
结论:
- 人工智能和机器学习通过增强模型开发,可解释性和可访问性来彻底改变QSP的巨大潜力.
- 挑战包括验证,伦理考虑,监管框架和跨学科合作.
- 人工智能/ML的整合将为药物开发和个性化医学带来一个变革的时代.
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