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相关概念视频

Effect of Hepatic Disease on Pharmacokinetics: Drug Dosing and Hepatic Blood Flow01:26

Effect of Hepatic Disease on Pharmacokinetics: Drug Dosing and Hepatic Blood Flow

Chronic liver disease significantly impacts drug metabolism due to alterations in hepatic blood flow and enzyme accessibility. This disruption affects the body's pharmacokinetics—the movement and processing of drugs within the system. Key enzymes crucial for metabolizing medications become less accessible, changing how drugs are processed and utilized. Furthermore, liver disease influences the synthesis of plasma proteins, such as albumin and globulins, which play critical roles in drug binding...
Effect of Hepatic Disease on Pharmacokinetics: Pathophysiologic Assessment and Liver Function Test01:22

Effect of Hepatic Disease on Pharmacokinetics: Pathophysiologic Assessment and Liver Function Test

In clinical practice, the direct measurement of hepatic blood flow to evaluate liver function presents significant challenges due to the intricate and specialized nature of the necessary techniques. Consequently, healthcare professionals often rely on empirical estimates derived from thorough patient examinations and liver function tests to gauge liver health. Among the tools at their disposal, the Child–Pugh and MELD scoring systems stand out for their ability to categorize and assess the...

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相关实验视频

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Using Human Induced Pluripotent Stem Cell-derived Hepatocyte-like Cells for Drug Discovery
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使用机器学习预测肝脏相关的体外终点,以支持早期检测药物诱导的肝损伤.

Marina Garcia de Lomana1, Domenico Gadaleta2, Marian Raschke3

  • 1Bayer AG, Pharmaceuticals, 42113 Wuppertal, Germany.

Chemical research in toxicology
|March 10, 2025
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概括

使用多任务机器学习模型改进了药物诱导性肝损伤 (DILI) 的预测. 这些模型分析了28个体外肝毒性终点,以预测药物开发早期潜在的DILI风险.

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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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相关实验视频

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科学领域:

  • 药理学和毒理学 药理学和毒理学
  • 计算化学的计算化学
  • 药物开发 药物开发

背景情况:

  • 药物诱导性肝损伤 (DILI) 是药物开发中的一个重大挑战,导致失败和退出.
  • DILI的复杂机制使其难以预测,需要先进的预测工具.
  • 了解肝脏的药理动力学和药理动力学对于减轻DILI风险至关重要.

研究的目的:

  • 探索多任务学习在预测28个体外肝毒性终点方面的潜力.
  • 开发机器学习模型来预测触发DILI的分子事件.
  • 创建一个"虚拟肝脏安全档案"以优先考虑化合物和阐明作用模式.

主要方法:

  • 收集了28个体外肝毒性和DILI特定终点的综合数据集.
  • 应用多任务学习和预测组合建模.
  • 包含不确定性估计来定义模型适用性领域.
  • 使用公开的化合物数据评估的模型用于胆盐出口 (BSEP) 抑制和脂症.

主要成果:

  • 证明了整体建模用于预测肝毒性终点的好处.
  • 建立了使用不确定性估计的预测模型的适用性领域.
  • 分析了各种终点和DILI之间的关系.
  • 在BSEP抑制等特定试验上验证模型性能.

结论:

  • 多任务学习模型在预测DILI和相关肝脏终点方面表现有前途.
  • "虚拟肝脏安全档案"可以帮助早期药物安全性评估.
  • 这些模型支持测试优先级和对DILI机制的理解.