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相关概念视频

Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance01:07

Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance

Drug transporters are critical in drug absorption, distribution, and excretion processes. They should be included in physiological-based pharmacokinetic (PBPK) models, which help predict human drug disposition. However, predicting this is challenging during drug development, especially when liver transport is involved. However, with a realistic representation of body transport processes, an accurate model may be possible.
A recent model describes pravastatin's hepatobiliary excretion, mediated...
Drug Product Performance: In Vitro–In Vivo Correlation01:20

Drug Product Performance: In Vitro–In Vivo Correlation

In pharmaceutical development, it's crucial to establish a predictive in vitro–in vivo correlation (IVIVC) for two or more formulations to gain a comprehensive understanding of release properties. IVIVC reduces the need for costly in vivo studies and facilitates the establishment of meaningful dissolution specifications with significant cost savings and decreased regulatory burden. Furthermore, a meaningful IVIVC should predict Cmax and AUC within 20%, aligning with FDA guidance while adhering...
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

Updated: Jul 8, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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使用概率机器学习预测与肝功能相关的体外测试.

Flavio M Morelli1, Marian Raschke2, Natalia Jungmann2

  • 1R&D Machine Learning Research, Bayer AG, Pharmaceuticals Division, Berlin, Germany; Department of Mathematics and Computer Science, Free University of Berlin, Berlin, Germany.

Toxicology
|May 21, 2025
PubMed
概括

这项研究将多种数据类型集成到一个概率框架中,以预测体外肝毒性,量化预测不确定性,以获得更安全的药物开发和减少动物试验.

关键词:
贝叶斯式建模 贝叶斯式建模肝毒性 肝毒性 肝毒性在体外检测中进行检测.多式联运集成 多式联运集成机器学习的概率学.不确定性定量化 不确定性定量化

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Predicting In Vivo Payloads Delivery using a Blood-brain Tumor-barrier in a Dish
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相关实验视频

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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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科学领域:

  • 毒理学 毒理学 毒理学
  • 计算生物学 计算生物学
  • 药理学 药理学是指药理学的学科.

背景情况:

  • 机器学习 (ML) 在毒理学中越来越多地使用,但有限的数据需要量化in silico预测的不确定性.
  • 在毒理学评估中可靠的决策需要强大的不确定性量化方法.

研究的目的:

  • 开发和评估一个概率框架,用于预测体外肝检测结果,使用综合数据模式.
  • 量化与这些in silico预测相关的不确定性.
  • 将预测整合到药物诱导肝损伤 (DILI) 概率的估计中.

主要方法:

  • 系统地比较各种概率方法来预测体外肝功能测试.
  • 整合多种数据模式:化学描述符,基因表达和形态资料.
  • 为生成活性氧物种和肝细胞毒性测试生成新的实验数据.

主要成果:

  • 在概率框架内证明了不同数据模式的性能.
  • 成功整合了框架和体外测试预测,以估计DILI概率.
  • 提供了新的肝细胞毒性和反应性氧物种生成的实验数据.

结论:

  • 不确定性量化对于可靠的in silico毒性预测至关重要.
  • 开发的概率框架提高了体外测试和DILI风险的预测.
  • 这种方法可以促进更安全的药物开发过程,并减少动物试验.