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相关概念视频

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

62
Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...
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Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

38
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.
38
Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance01:07

Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance

26
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,...
26
Passive Diffusion: Overview and Kinetics01:17

Passive Diffusion: Overview and Kinetics

407
Passive diffusion is a critical process that allows small lipophilic drugs to cross the cell membrane along a concentration gradient. This mechanism's efficiency depends on four primary factors: the membrane's surface area, the drug's lipid-water partition coefficient, the concentration gradient, and the membrane's thickness.
When administered orally, drugs establish a substantial concentration gradient between the gastrointestinal (GI) lumen and the bloodstream, expediting...
407
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

554
Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
554
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

28
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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相关实验视频

Updated: Jun 5, 2025

Models and Methods to Evaluate Transport of Drug Delivery Systems Across Cellular Barriers
18:57

Models and Methods to Evaluate Transport of Drug Delivery Systems Across Cellular Barriers

Published on: October 17, 2013

46.2K

基于结构的药物设计与等价扩散模型.

Arne Schneuing1, Charles Harris2, Yuanqi Du3

  • 1École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland. arne.schneuing@epfl.ch.

Nature computational science
|December 9, 2024
PubMed
概括
此摘要是机器生成的。

一种新的扩散模型,DifSBDD,通过生成蛋白标的配体,使基于结构的药物设计成为可能. 这个单一模型处理属性优化,负面设计和部分分子设计,简化药物发现.

更多相关视频

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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A Method for Determination and Simulation of Permeability and Diffusion in a 3D Tissue Model in a Membrane Insert System for Multi-well Plates
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A Method for Determination and Simulation of Permeability and Diffusion in a 3D Tissue Model in a Membrane Insert System for Multi-well Plates

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相关实验视频

Last Updated: Jun 5, 2025

Models and Methods to Evaluate Transport of Drug Delivery Systems Across Cellular Barriers
18:57

Models and Methods to Evaluate Transport of Drug Delivery Systems Across Cellular Barriers

Published on: October 17, 2013

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Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
10:29

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

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A Method for Determination and Simulation of Permeability and Diffusion in a 3D Tissue Model in a Membrane Insert System for Multi-well Plates
10:33

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科学领域:

  • 计算化学是一种计算化学.
  • 药物发现 药物发现
  • 医学中的人工智能

背景情况:

  • 基于结构的药物设计 (SBDD) 传统上侧重于创建具有对特定蛋白质标高亲和度的分子.
  • 当前的生成性SBDD方法通常需要特定任务的模型,需要为每个应用程序进行广泛的数据策划和再培训.
  • 这限制了药物开发中的计算方法的适应性和效率.

研究的目的:

  • 为广泛的SBDD任务引入一个多功能,单一预训练的扩散模型.
  • 展示该模型用于属性优化,明确负面设计和基于inpainting的分子设计的应用.
  • 介绍DiffSBDD,一种基于蛋白质口袋结构的条件连接体生成的SE(3) - 相当的扩散模型.

主要方法:

  • 制定SBDD作为一个三维条件生成问题.
  • 开发了DiffSBDD,一种具有SE(3) -等价性的扩散模型,用于生成受蛋白口袋条件的配体.
  • 结合额外的约束来改进基于计算指标生成的候选药物.

主要成果:

  • 单个预训练的扩散模型成功地解决了多个SBDD挑战,包括属性优化和负面设计.
  • DiffSBDD产生了在蛋白质口袋上受条件的新联体,证明了其在 de novo 设计中的能力.
  • 通过应用特定的约束,可以提高模型的性能,从而改善药物候选物.

结论:

  • DiffSBDD为基于结构的药物设计提供了一个统一而灵活的框架,克服了特定任务模型的局限性.
  • 这种方法通过利用单一的,可适应的AI模型,简化了优化药物候选物的生成.
  • 这些发现为生成性AI在加速药物发现方面的更有效和更广泛应用铺平了道路.