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相关概念视频

Theories of Dissolution: The Danckwerts' Model and Interfacial Barrier Model01:09

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

269
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...
269
Theories of Dissolution: Diffusion Layer Model01:15

Theories of Dissolution: Diffusion Layer Model

675
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...
675
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

56
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
56
Factors Affecting Dissolution: Drug Permeability, Stability and Stereochemistry01:20

Factors Affecting Dissolution: Drug Permeability, Stability and Stereochemistry

173
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...
173
Factors Affecting Dissolution: Particle Size and Effective Surface Area01:23

Factors Affecting Dissolution: Particle Size and Effective Surface Area

719
Dissolution kinetics, an essential aspect of oral drug delivery, is significantly influenced by the drug's particle size. According to the Noyes-Whitney dissolution model, the dissolution rate correlates directly with the drug's surface area. The larger the surface area, the higher the drug's solubility in water, leading to a faster drug dissolution rate. Reducing particle size increases the effective surface area, enhancing the dissolution process. Micronization and nanosizing are...
719
Factors Influencing Drug Absorption: Pharmaceutical Parameters01:28

Factors Influencing Drug Absorption: Pharmaceutical Parameters

114
Solid dosage forms such as tablets and capsules undergo rigorous manufacturing processes to ensure stability and effectiveness. Their dissolution and absorption properties are influenced significantly by the choice of excipients (inactive ingredients that serve various roles in the formulation), and the methodology applied during production. The manufacturing parameters, such as compression force and granulation techniques, significantly affect dissolution rates. Elevated compression forces...
114

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积极学习和高斯过程用于溶解模型的开发:基于人工智能的数据高效方法.

Roshan A Patel1, Siddharth S Kesharwani1, Fady Ibrahim1

  • 1Drug Product Development, Synthetics Platform, Sanofi, 350 Water St., Cambridge, MA 02141, USA.

Journal of controlled release : official journal of the Controlled Release Society
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PubMed
概括

高斯过程回归 (GPR) 和主动学习显著减少了对制药溶解建模的数据需求. 这些方法提高了预测准确性,并比传统方法更有效地识别关键处理参数.

关键词:
积极学习是指积极学习.生物制药生物制药公司溶解模型是一种溶解模型.高斯过程回归的高斯过程回归.机器学习是机器学习.过程参数优化过程参数优化

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科学领域:

  • 制药科学 制药科学
  • 计算化学的计算化学
  • 化学工程是化学工程的重要组成部分.

背景情况:

  • 在体外溶解测试对于药品质量控制和配方优化至关重要.
  • 数据驱动的溶解模型通过预测结果,减少物理实验和识别关键影响因素来提供效率.
  • 尽量减少模型开发的数据要求,提高了这些预测工具的实际实用性.

研究的目的:

  • 调查高斯过程回归 (GPR) 和主动学习在减少预测溶解建模数据需求方面的有效性.
  • 将GPR的性能与传统的多项式模型进行比较,用于溶解预测.
  • 评估这些方法识别影响药物溶解的关键处理参数的能力.

主要方法:

  • 实验设计 (DOE) 研究在五个处理参数中进行,以生成B化合物的溶解数据.
  • 用高斯过程回归 (GPR) 和多项式模型进行训练并使用生成的数据集进行比较.
  • 沙普利添加式解释 (SHAP) 用于GPR模型解释和参数重要性评估.
  • 积极学习策略被追溯分析,以评估它们在选择最佳实验子集中的潜力.

主要成果:

  • 与多项式模型相比,在相同的数据集上训练时,GPR表现出更高保真度的溶解预测.
  • 沙普利的添加式解释有效地识别和排名了影响溶解的各种加工参数的重要性.
  • 追溯分析表明,积极学习可以识别比完全的DOE更小,更具信息性的实验集,用于模型开发和参数识别.

结论:

  • 与传统的多项式模型相比,高斯过程回归为药物溶解提供了优越的预测性能.
  • 积极学习策略可以大幅减少开发准确溶解模型和理解过程参数关系所需的实验负担.
  • 将GPR和主动学习结合使用,为制药产品开发和质量控制提供了一个强大的,数据效率高的方法.