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相关概念视频

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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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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...
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Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

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Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
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Multicompartment Models: Overview01:14

Multicompartment Models: Overview

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Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Compartment Models: Single-Compartment Model01:14

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The single-compartment model serves as a simplified representation of the human body. This model assumes that the body functions as a single, well-mixed open compartment. When a drug is administered intravenously, it enters the body and quickly distributes uniformly. The drug then undergoes biotransformation and elimination, ultimately leaving the body. The volume of this compartment is referred to as the apparent volume of distribution into which the drug can uniformly distribute. In this...
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实证模型可变性:开发一种新的全球优化方法来填充压缩和压缩混合规则.

Theo Tait1, Mohammad Salehian1, Magdalini Aroniada2

  • 1Centre for Continuous Manufacturing and Advanced Crystallization (CMAC), University of Strathclyde, Glasgow G1 1RD, UK; Strathclyde Institute of Pharmacy & Biomedical Sciences, University of Strathclyde, Glasgow G4 0RE, UK.

International journal of pharmaceutics
|July 17, 2024
PubMed
概括
此摘要是机器生成的。

这项研究评估了制药粉末紧缩模型,发现性能随着更高的活性药物成分 (API) 负载而下降. 一种新的全球优化方法提高了预测准确性,并减少了对新混合物的实验需求.

关键词:
经验模型是经验模型.全球优化全球优化混合规则 混合规则一个平板电脑压缩片.一个平板电脑压缩平板电脑.

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科学领域:

  • 制药科学 制药科学
  • 材料科学 材料科学 材料科学

背景情况:

  • 经验模型对于预测制药粉末缩行为至关重要.
  • 现有的模型经常显示,随着活性药物成分 (API) 度的增加,准确性降低.
  • 模型参数的优化对于在平板电脑配方中可靠的预测至关重要.

研究的目的:

  • 系统地评估用于制药粉末紧缩的常见实证模型的预测准确性.
  • 为实证紧缩模型开发和验证一种全新的全球优化方法.
  • 在新混合物配方过程中减少实验要求和API消耗.

主要方法:

  • 将9种安慰剂和12种含有API的混合物与Gurnham,Heckel,Kawakita,Ryshkewitch-Duckworth和Leuenberger模型相匹配.
  • 执行参数可变性分析以确定可接受的模型匹配.
  • 开发一个全局优化方法,使用算术,几何和和混合规则来调整参数.
  • 进行交叉验证研究,以评估新混合物的预测能力.

主要成果:

  • 所有模型在低API负载 (<20w/w%) 的情况下都表现良好 (R2>90%,RRMSE<0.1).
  • 模型性能,特别是Heckel模型,随着API负载的增加而显著下降.
  • 新的全球优化方法超过了传统的最佳合适的线路.
  • 交叉验证证实了该方法能够预测新混合物中可接受的好适合性调整参数的能力.

结论:

  • 经验压缩模型的预测准确性对API负载敏感.
  • 开发的全球优化方法为制药混合物开发提供了改进的预测和减少的实验负担.
  • 这种方法可以有效地探索新的混合物,尽量减少API使用和实验力度.