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DigiLoCS:在预测器官在芯片模拟中迈出了一大步.

Manoja Rajalakshmi Aravindakshan1, Chittaranjan Mandal1, Alex Pothen2

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概括

这项研究引入了肝芯片的数字双胞胎,以准确预测人类药物清除率. 该模型增强了体外到体外抽取 (IVIVE) 进行体外抽取,以实现更安全,更有效的药物开发.

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科学领域:

  • 药理动力学和药物新陈代谢
  • 仿生系统和数字双胞胎
  • 芯片上的器官技术

背景情况:

  • 从体外数据准确预测人类药物清除率对于成功开发药物至关重要,防止由于剂量不足或毒性导致的临床试验失败.
  • 现有的体外模型往往难以弥合实验结果和临床相关性之间的差距,需要改进的预测方法.
  • 数字双胞胎为模拟复杂的生物系统提供了强大的方法,但它们应用于用于药理动力学预测的肝芯片模型需要进一步开发.

研究的目的:

  • 开发和验证肝芯片系统的数字双胞胎,用于模拟人类肝脏清除.
  • 用开发的数字双胞胎来预测一组药物的人类清除值.
  • 建立一个框架,以加强体外到体外抽取 (IVIVE),并弥合体外结果和临床结果之间的差距.

主要方法:

  • 使用普通微分方程 (ODEs) 创建一个分区生理模型,以表示介质,间位体和细胞内分区中的药物度.
  • 整合了定量器官芯片 (OoC) 和基于细胞的药物消耗动力学测试数据.
  • 开发DigiLoCs (数字肝脏在芯片上) 数字双胞胎,结合硬件和生物信息,以ODE受约束的优化进行清除估计.

主要成果:

  • 数字双胞胎模型与传统模型相比,可以更好地预测肝脏内在清除量.
  • 该模型成功模拟了药物耗尽动力学,在芯片硬件和细胞内生物过程之间建立了联系.
  • 作为概念验证,对兰醇的应用验证了该模型预测临床意义的能力,并提供了有关被动与活性药物过程的见解.

结论:

  • 开发的肝芯片数字双胞胎 (DigiLoCs) 为准确预测人类药物清除提供了一个强大的平台,增强IVIVE.
  • 这种方法提供了基于生理参数的解释性,并区分了代谢和被动药物处置过程.
  • 这项研究代表了药物开发的重大进步,旨在通过改善临床结果预测来减少时间,成本和患者负担.