一个人工智能辅助的生理学基础的药物动力学模型来预测纳米粒子向小鼠瘤的输送
Wei-Chun Chou1, Qiran Chen1, Long Yuan1
1Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32608, USA; Center for Environmental and Human Toxicology, University of Florida, Gainesville, FL 32610, USA.
概括
一个人工智能辅助的模型改善了纳米粒子向瘤的传递. 该工具使用物理化学性质预测输送效率,绕过动物试验以更快地开发癌症纳米药物.
科学领域:
- 纳米医学是一种纳米医学.
- 计算生物学 计算生物学
- 药理动力学 药理动力学
背景情况:
- 低效的纳米粒子 (NP) 传递到固体瘤是癌症纳米医学临床转化的一个主要障碍.
- 人工智能 (AI) 和机器学习方面的进步为优化NP交付提供了新的解决方案.
- 基于生理学的药理动力学 (PBPK) 模型对于模拟体内的药物行为是有价值的.
研究的目的:
- 开发一个人工智能辅助的PBPK模型,整合基于人工智能的定量结构-活动关系 (QSAR) 和PBPK建模.
- 预测瘤向传递效率 (DE) 和各种NP的生物分布.
- 建立基于物理化学性质的NP交付有效选工具.
主要方法:
- 使用机器学习和在"纳米瘤数据库"上训练的深度神经网络开发了一个基于AI的QSAR模型.
- 将AI-QSAR模型与PBPK模型集成在一起,以预测NP交付效率 (DEmax,DE24,DE168).
- 验证了AI-PBPK模型与实验测量的药理动力学数据相对应.
主要成果:
- AI-PBPK模型对DE24 (R2=0.83) 和DEmax (R2=0.82) 显示出强大的预测准确度.
- 模型预测与实验数据的相关性很好 (对133/288个数据集的R2 ≥0.70).
- 该模型有效地预测了使用物理化学性质而没有动物数据的NP交付效率.
结论:
- 人工智能辅助的PBPK模型是预测纳米粒子在瘤中的传递效率的强大工具.
- 这种方法加快了针对癌症纳米医学应用的NP选.
- 该模型减少了对动物研究的依赖,促进了更快的临床翻译.
相关概念视频
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01:20Pharmacokinetic Models: Overview
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01:26Pharmacokinetic Models: Comparison and Selection Criterion
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