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相关概念视频

Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

Physiological Pharmacokinetic Models: Assumption with Protein Binding

23
Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
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Drug Distribution: Plasma Protein Binding01:29

Drug Distribution: Plasma Protein Binding

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Drugs predominantly attach to plasma proteins, with only a small percentage remaining unbound. The unbound portion can be calculated as one minus the bound fraction. Acidic drugs form large, inactive complexes by reversibly binding to plasma albumin, which prevents them from diffusing across biological barriers. These drug-protein complexes act as reservoirs for the drugs. As the concentration of unbound drugs decreases, these complexes quickly dissociate to release the free drug, maintaining...
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Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance01:07

Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance

17
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,...
17
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

21
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
21
Factors Affecting Drug Distribution: Tissue Permeability01:30

Factors Affecting Drug Distribution: Tissue Permeability

101
The drug distribution process within the human body is a complex interplay of various physicochemical properties inherent to the drugs. These properties, including molecular size, ionization degree, partition coefficient, and stereochemical nature, significantly impact how drugs permeate biological membranes to reach their target tissues.
Small molecules with a molecular weight below 500 to 600 Daltons can easily pass through the capillary membrane, gaining access to different tissues. Larger...
101
Factors Affecting Dissolution: Drug pKa, Lipophilicity and GI pH01:21

Factors Affecting Dissolution: Drug pKa, Lipophilicity and GI pH

799
Drug absorption within the gastrointestinal (GI) tract is a complex process influenced by several critical factors, including the site pH, the drug's dissociation constant (pKa), and the drug's lipophilicity. The GI tract exhibits a pH gradient, with an acidic environment in the stomach and a more alkaline environment in the small intestine. This pH variation directly affects the ionization state of drugs.
A drug's pKa and the pH of the gastrointestinal (GI) tract play crucial roles...
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Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia
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通过公式指导网络预测化合物的大脑与等离子体不结合的分离系数.

Yurong Zou1, Haolun Yuan2, Zhongning Guo1

  • 1State Key Laboratory of Biotherapy and Collaborative Innovation Center of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China.

Journal of chemical information and modeling
|May 9, 2025
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概括

我们开发了一种深度学习模型来预测血脑屏障 (BBB) 的透性,特别是大脑与血不结合的分区系数 (Kp,uu). 这个工具通过改善药物如何穿越BBB的预测来帮助药物开发.

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科学领域:

  • 药理学 药理学是指药理学的学科.
  • 计算化学计算化学
  • 神经科学是一个神经科学.

背景情况:

  • 血脑屏障 (BBB) 透性对于大脑药物疗效至关重要.
  • 大脑与等离子体不结合的分离系数 (Kp,uu) 是BBB透性的关键指标.
  • 现有的Kp,uu数据很少,实证预测模型缺乏广泛的适用性.

研究的目的:

  • 为了解决Kp,uu数据的稀缺性和现有模型的局限性.
  • 开发一种新的,准确的,广泛适用的模型来预测Kp,uu.
  • 为 rat Kp,uu 值建立一个公共数据集.

主要方法:

  • 数据挖掘以建立一个公共的 rat Kp,uu 数据集.
  • 开发一个公式导向的深度学习模型 (CMD-FGKpuu).
  • 在多个基准测试中验证模型.

主要成果:

  • CMD-FGKpuu模型在预测Kp,uu.puu方面表现强.
  • 该模型显示了Kp,uu预测中的深度学习应用的潜力.
  • 该模型可以通过特定项目的数据进行微调,以提高效用.

结论:

  • 一个新的深度学习工具 (CMD-FGKpuu) 能够有效地预测BBB透度 (Kp,uu).
  • 该研究为药物开发和BBB研究提供了宝贵的资源.
  • 介绍了在制药研究中对少数人学习的新应用,用于预测药物特性.