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相关概念视频

Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

150
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.
150
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

127
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...
127
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

387
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...
387
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

126
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
126
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

87
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...
87
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

1.1K
Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
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相关实验视频

Updated: Sep 13, 2025

An R-Based Landscape Validation of a Competing Risk Model
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在PBPK模型中选择参数子集的特性变化和不确定性.

Celia M Schacht1, Dustin F Kapraun1, Annabel E Meade2

  • 1Center for Public Health and Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, 27711, United States.

Toxicological sciences : an official journal of the Society of Toxicology
|July 30, 2025
PubMed
概括
此摘要是机器生成的。

基于生理学上的药理动力学 (PBPK) 模型估计人类等效剂量 (HED). 全球敏感性分析 (GSA) 有助于确定影响极端HED百分位数的关键参数,以改善风险评估.

关键词:
基于生理学的药理动力学.人类风险评估人类风险评估数学建模的数学建模灵敏度分析是一种灵敏度分析.不确定性量化不确定性量化

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科学领域:

  • 药理动力学和毒理学建模.
  • 计算毒理学和风险评估.
  • 环境健康科学 环境健康科学

背景情况:

  • 基于生理学的药理动力学 (PBPK) 模型模拟化学物质的吸收,分布,新陈代谢和分泌.
  • 概率PBPK模型使用蒙特卡洛采样生成人类等效剂量 (HED) 分布.
  • 极端HED百分值对于评估敏感人群中的风险至关重要.

研究的目的:

  • 开发方法来识别PBPK模型中影响极端HED百分位数的有影响力的参数.
  • 评估参数分布不确定性对HED估计的影响.
  • 使用PBPK建模来提高风险评估的可靠性.

主要方法:

  • 全球敏感性分析 (GSA) 应用于已发布的二甲和的PBPK模型.
  • 分析包括不同内部目标水平的吸入和口服暴露场景.
  • 使用一种新的方法来评估极端HED百分位数对参数分布的灵敏度.

主要成果:

  • GSA确定了对第1个和第99个HED百分位数显著影响的参数子集.
  • 具体的影响参数在不同的模型和暴露条件上有所不同.
  • 在有影响力的参数中表征不确定性可以增加对极端HED百分位数估计的信心.

结论:

  • 全球灵敏度分析有效地确定了极端HED百分位数的关键PBPK模型参数.
  • 对于GSA识别的参数,准确的分布数据可以提高风险评估的准确性.
  • 通过对敏感性进行参数化来改进PBPK模型,提高了对评估化学物质暴露风险的信心.