利用和人工智能模型,在整个生命周期中提升药物处置和反应预测
Kyunghee Yang1, Daniel Gonzalez2,3, Jeffrey L Woodhead1
1Quantitative Systems Pharmacology Solutions, Simulations Plus Inc, North Carolina, USA.
Clinical and translational science
|June 20, 2025
概括
先进的in silico和AI模型通过创建虚拟人群来增强药物开发. 这有助于更好地预测各种患者群体的药物暴露和反应,包括儿童和老年人.
科学领域:
- 药理学和药物开发领域
- 计算生物学 计算生物学
- 系统毒理学 系统毒理学
背景情况:
- 确保安全有效的药物使用需要了解药物处置和反应的个体间差异.
- 临床试验往往不足以代表各种人群 (儿童,老年人,孕妇等). ),需要先进的评估工具.
- 基于生理学的药理动力学 (PBPK) 和定量系统药理学/毒理学 (QSP/QST) 模型为虚拟人口分析提供了解决方案.
研究的目的:
- 审查in silico和AI模型在预测药物暴露和整个生命周期的反应中的应用.
- 突出PBPK和QSP/QST框架内虚拟群体的使用.
- 讨论人工智能在模拟不同年龄组药物剂量的机会和挑战.
主要方法:
- 在模拟技术 (PBPK,QSP/QST) 的文献综述.
- 机器学习 (ML) 和人工智能 (AI) 的整合用于数据分析.
- 在绝经后的妇女中使用QST建模用于药物诱导肝损伤 (DILI) 的案例研究.
主要成果:
- 在 silico 和 AI 模型中,可以创建反映不同生理状态的虚拟群体.
- 这些模型整合了临床试验和现实世界数据 (RWD) 以改善药物疗效和安全的预测.
- 人工智能工具可以识别影响不同年龄组药物反应的关键生理因素.
结论:
- 在和人工智能建模对于推进药物开发至关重要,因为它考虑了个体间的变化.
- 虚拟人群和人工智能驱动的分析增强了对整个生命周期的药物处置和反应的预测.
- 需要进一步开发和应用这些模型,以应对所有患者群体药物剂量方面的挑战.
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