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相关概念视频

Theories of Dissolution: Diffusion Layer Model01:15

Theories of Dissolution: Diffusion Layer Model

966
Dissolution, the process by which drug particles dissolve in a solvent, is explained by the diffusion layer model, a theoretical framework that simulates the absorption of oral drugs and allows us to analyze experimental data.
This process starts with a thin layer, saturated with the drug, forming at the interface between the solid and liquid. The solute then diffuses from this layer into the main solution. The Noyes-Whitney equation suggests that the rate of dissolution relies on the diffusion...
966
Theories of Dissolution: The Danckwerts' Model and Interfacial Barrier Model01:09

Theories of Dissolution: The Danckwerts' Model and Interfacial Barrier Model

457
Various dissolution theories provide insight into the factors that influence the dissolution rate. Danckwerts' Model suggests that turbulence, rather than a stagnant layer, characterizes the dissolution medium at the solid-liquid interface. In this model, the agitated solvent contains macroscopic packets that move to the interface via eddy currents, facilitating the absorption and delivery of the drug to the bulk solution. The regular replenishment of solvent packets maintains the...
457
Factors Influencing Drug Absorption: Drug Dissolution01:27

Factors Influencing Drug Absorption: Drug Dissolution

734
The pharmacokinetic journey of drugs from solid oral dosage forms into systemic circulation is multifaceted. It begins with disintegration, a prerequisite ensuring a solid dosage form's subdivision into minute particles. Dissolution occurs next as these granulated entities solubilize in gastrointestinal fluids. This solubilization is crucial for the succeeding stage, permeation, which describes the traversal of the drug across the intestinal membrane and its subsequent entry into the blood...
734
Factors Affecting Dissolution: Drug pKa, Lipophilicity and GI pH01:21

Factors Affecting Dissolution: Drug pKa, Lipophilicity and GI pH

2.0K
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...
2.0K
Factors Affecting Dissolution: Polymorphism, Amorphism and Pseudopolymorphism01:21

Factors Affecting Dissolution: Polymorphism, Amorphism and Pseudopolymorphism

407
Polymorphism refers to the existence of a drug substance in multiple crystalline forms, known as polymorphs. Recently, this term has been expanded to include solvates (forms containing a solvent), amorphous forms (non-crystalline forms), and desolvated solvates (forms from which the solvent has been removed).
Some polymorphic crystals possess lower aqueous solubility than their amorphous counterparts, leading to incomplete absorption. For instance, the oral suspension of Chloramphenicol, which...
407
Factors Affecting Dissolution: Drug Permeability, Stability and Stereochemistry01:20

Factors Affecting Dissolution: Drug Permeability, Stability and Stereochemistry

269
Orally administered drugs primarily enter the systemic circulation via passive diffusion through the intestinal membranes. The drug's absorption is influenced by drug stability in the gastrointestinal GI tract, membrane permeability, the surface area available for absorption, luminal drug concentration, and residence time in the lumen. Drug permeability can be enhanced by adjusting the lipophilicity, polarity, or molecular size of the drug, promoting its passive transport across intestinal...
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Updated: Sep 19, 2025

An In Vitro Dissolution Determination of Multi-Index Components in Tibetan Medicine Rhodiola Granules
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基于机器学习的定量结构-溶解配置文件关系关系

Lap Au-Yeung1, Chih-Yuan Tseng2, Yun K Tam2

  • 1Department of Mechanical Engineering, University of Alberta, Edmonton T6G 2R3, Alberta, Canada.

Journal of chemical information and modeling
|June 5, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的机器学习方法,用于预测药物溶解率,这是药物生物可用性的关键因素. 这种方法显示了有效的早期药物配方的希望,尽管目前的准确性有限.

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Measuring Biomolecular DSC Profiles with Thermolabile Ligands to Rapidly Characterize Folding and Binding Interactions
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An In Vitro Dissolution Determination of Multi-Index Components in Tibetan Medicine Rhodiola Granules
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Coherent anti-Stokes Raman Scattering CARS Microscopy Visualizes Pharmaceutical Tablets During Dissolution
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科学领域:

  • 计算化学和药物发现
  • 机器学习在制药科学中的应用.

背景情况:

  • 准确的药物溶解概况对于估计口服药物的生物可用性至关重要.
  • 现有的方法可以从化学结构中预测药物溶解度,但不能预测溶解速率常数.
  • 目前还没有可靠的工具来预测溶解速率常数.

研究的目的:

  • 开发一种新的两阶段机器学习方法,用于预测药物溶解概况.
  • 为了实现这一预测,整合了基于物理学的神经网络 (PINNs) 和深度神经网络 (DNNs).
  • 预测药物溶解在含有不同度酸硫酸盐的水中.

主要方法:

  • 一个两阶段的机器学习方法:基于机器学习的定量结构-溶解配置关系.
  • 阶段1:PINNs从数据中提取溶解参数 (溶解速率常数"k"和和度"φs"的溶解质量分数),利用物理定律和诺耶斯-惠特尼方程.
  • 第二阶段:DNN使用提取的参数,药物的化学结构和溶解介质来预测溶解概况.

主要成果:

  • 通过使用FDA推的f1和f2指标,DNN实现了61.7%的平均测试准确率.
  • 这种准确性是通过80:20的火车到测试分割实现的,低于典型的70-80%的接受范围.
  • 通过过噪音,PINNs显示了提高预测性能和减少数据需求的潜力.

结论:

  • 开发的两阶段机器学习方法显示出作为早期药物制剂的低成本,时间效率高的工具的巨大潜力.
  • 随着数据质量和多样性的提高,预计会有进一步的改进.
  • 这种方法为预测药物溶解概况提供了一个有希望的途径,有助于药物发现和开发.