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相关概念视频

Dose-Response Relationship: Overview01:03

Dose-Response Relationship: Overview

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Agonists can bind with and activate receptors, resulting in the formation of drug-receptor complexes. Once formed, these complexes catalyze many biochemical processes at the cellular level and subsequently induce a pharmacologic response. The degree of response is directly proportional to the fraction of activated receptors, which in turn, depends on the concentration of the drug at the receptor site as well as the sensitivity of the receptor. An increase in the administered dose contributes to...
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Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

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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.
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Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

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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...
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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...
14
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

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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...
503
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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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...
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相关实验视频

Updated: May 11, 2025

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
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通过使用DoseRider在途径水平上建模omics剂量反应.

Pablo Monfort-Lanzas1,2, Johanna M Gostner1, Hubert Hackl2

  • 1Institute of Medical Biochemistry, Biocenter, Medical University Innsbruck, 6020 Innsbruck, Austria.

Computational and structural biotechnology journal
|April 17, 2025
PubMed
概括

DoseRider是一款新的网络应用程序和R包,用于分析omics数据,以建模剂量反应关系并确定生物暴露极限. 它为复杂曲线引入了趋势变化剂量 (TCD),帮助毒理学和药理学研究.

关键词:
基准剂量的基准剂量剂量反应建模剂量反应建模混合型号是混合型号的多个omics的多个omics.系统生物学 系统生物学毒理学 毒理学 毒理学趋势变化剂量变化

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科学领域:

  • 药物遗传学和毒物遗传学
  • 计算生物学和生物信息学
  • 分子毒理学 分子毒理学

背景情况:

  • 对于药理学和毒理学的机制性见解,Omics数据生成至关重要.
  • 非线性剂量反应关系需要强大的建模来推断生物暴露极限.
  • 目前用于剂量反应建模的工具往往缺乏全面的途径级分析.

研究的目的:

  • 介绍DoseRider,一个用于对OMIC数据的剂量反应建模的Web应用程序和R包.
  • 为了使基准剂量 (BMD) 在生物途径或特征水平的评估.
  • 提出一种趋势变化剂量 (TCD) 的新概念,用于表征复杂的剂量反应效应.

主要方法:

  • 开发DoseRider,一个用户友好的Web应用程序和R包.
  • 利用通用混合效应模型用于线性和非线性剂量反应建模.
  • 集成的多omics数据 (RNA测序,代谢学) 与路径和基因组注释.
  • 将DoseRider应用于从双AF (BPAF) 治疗的MCF-7细胞中的RNA测序数据.

主要成果:

  • DoseRider成功地分析了定制并提供了跨物种的多omics数据.
  • 证明了与BPAF治疗数据的可用性,确定了对雌激素敏感基因的0.2μM的BMD.
  • 确定了BPAF的0.003μM的最低趋势变化剂量 (TCD1),表明了早期的生物效应.

结论:

  • DoseRider提供了一个全面的平台,用于使用OMIC数据进行剂量反应建模和BMD评估.
  • 引入TCD为理解复杂的剂量反应现象提供了一个新的指标.
  • DoseRider适用于药物基因组学,毒物基因组学和更广泛的生物研究应用.