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Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

252
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
252
One-Compartment Open Model for IV Bolus Administration: Estimation of Elimination Rate Constant, Half-Life and Volume of Distribution01:09

One-Compartment Open Model for IV Bolus Administration: Estimation of Elimination Rate Constant, Half-Life and Volume of Distribution

234
The one-compartment open model is a simplified approach used in pharmacokinetics to understand the distribution and elimination of a drug administered through an intravenous bolus. This model assumes rapid drug dispersal throughout the body and elimination using a first-order process. Key pharmacokinetic parameters, such as the elimination rate constant (k), half-life (t1/2), and the apparent volume of distribution (Vd), can be estimated from this model. The elimination rate is calculated...
234
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

697
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...
697
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

48
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
48
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

64
Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
64
Factors Influencing Drug Absorption: Pharmaceutical Parameters01:28

Factors Influencing Drug Absorption: Pharmaceutical Parameters

127
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...
127

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使用人工神经网络优化和评估修改释放固体剂型.

Tulsi Sagar Sheth1,2, Falguni Acharya3

  • 1Department of Applied Sciences and Humanities, Parul Institute of Engineering and Technology, Parul University, Vadodara, Gujarat, 391760, India.

Scientific reports
|July 16, 2024
PubMed
概括

人工神经网络 (ANN) 优化 昆氨酸 修饰释放片. 这种方法准确地预测了药物释放概况,证明了ANN.

关键词:
人工神经网络的人工神经网络药物释放特征 药物释放特征在 MATLAB 中,我们可以使用 MATLAB.相似性因子 (f2) 是一个相似性因子.模拟模拟是为了模拟.固体剂量形式 固体剂量形式

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科学领域:

  • 制药科学 制药科学
  • 计算化学计算化学
  • 药物运输 药物运输 药物运输

背景情况:

  • 修改释放剂量形式对于优化药物疗效和患者遵守是至关重要的.
  • 昆酸是一种抗精神病药物,需要精确的剂量控制.
  • 预测建模可以加速复杂药物配方的开发.

研究的目的:

  • 为了优化和评估奎酸修饰释放片的药物释放动力学.
  • 利用人工神经网络 (ANN) 来预测和优化药物释放概况.
  • 为制药配方开发建立一个强大的in silico方法.

主要方法:

  • 人工神经网络 (ANN) 用于模拟药物释放动力学.
  • 辅助成分组合物 (酸,Eudragit® L100 55,Eudragit® L30 D55,乳糖单,二酸,糖乙烯酸) 被用作可变输入.
  • 十个时间点的体外溶解数据作为网络培训的目标输出.

主要成果:

  • 训练有素的ANN成功模拟并预测了奎胺酸MR片的体外溶解概况.
  • 相似性因子 (f2) 证实了预测和制造的配方释放配置文件之间的强烈一致.
  • 在优化制药配方方面,ANN显示出显著的潜力.

结论:

  • 人工神经网络提供了一个强大的工具,用于优化药物释放动力学在修改释放配方.
  • 这项研究验证了使用ANN来有效和准确地预测制药配方性能.
  • 开发的ANN模型可以加快奎胺酸MR片和类似配方的开发周期.