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下一代实验和计算策略用于药物相互作用预言
Aarika Kanukolanu1, Lakshmi Vineela Nalla2, Siva Nageswara Rao Gajula1
1Department of Pharmaceutical Analysis, GITAM School of Pharmacy, GITAM (Deemed to be University), Visakhapatnam, Andhra Pradesh, India.
Drug metabolism and disposition: the biological fate of chemicals
|September 13, 2025
概括
先进的实验和计算模型改善了药物相互作用 (DDI) 的预测. 这些新型系统提高了准确性,支持更安全的药物开发,并帮助临床决策,以获得更好的患者护理.
科学领域:
- 药理学和毒理学 药理学和毒理学
- 生物医学工程 生物医学工程
- 计算生物学 计算生物学
背景情况:
- 药物相互作用 (DDI) 在药物治疗中存在重大挑战,影响药物的疗效和患者安全.
- 传统的体外和体内模型往往缺乏预测临床相关DDI的准确性.
- 需要先进的平台来改善DDI评估.
研究的目的:
- 审查用于DDI评估的先进的体外和体内系统.
- 讨论用于预测DDI的计算建模技术的进步.
- 突出实验和计算方法的整合,以提高DDI预测.
主要方法:
- 探索先进的体外/体内模型:人性化的肝脏仿真小鼠,CRISPR相关的动物模型,肝脏微生理系统,3D球体和有机体.
- 预测建模技术的讨论:基于生理学的药理动力学 (PBPK) 模型和机器学习 (ML) 方法.
- 将实验数据与PBPK和ML模型集成,用于动态DDI模拟和模式识别.
主要成果:
- 新的实验模型允许在生理条件下精确评估药物代谢,酶诱导/抑制和输送物相互作用.
- PBPK模型动态模拟DDI,将临床前和临床发现相结合.
- 机器学习工具从大型数据集中识别复杂的交互模式,改善早期DDI风险评估.
结论:
- 集成先进的实验和计算方法使得更准确的,定量DDI预测.
- 这些创新促进了更安全的药物设计,监管决策和减少药物不良反应.
- 持续改进和混合策略,结合机械和数据驱动模型,对于稳健,临床相关的DDI评估至关重要.
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