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相关概念视频

Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

575
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...
575
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

38
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.
38
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

62
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...
62
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

75
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
75
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

222
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...
222
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

28
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
28

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量化系统药理学的未来方向

Birgit Schoeberl1, Cynthia J Musante2, Saroja Ramanujan3

  • 1Biomedical Research, Novartis, Cambridge, MA, USA.

Handbook of experimental pharmacology
|January 15, 2025
PubMed
概括

量化系统药理学 (QSP) 将集成先进的分析,机器学习 (ML) 和人工智能 (AI) 来用于药物发现. 这种演变将在整个开发过程中增强模型构建,数据集成和精准医学应用.

科学领域:

  • 药理学 药理学是指药理学的学科.
  • 计算生物学 计算生物学
  • 药物开发 药物开发

背景情况:

  • 量化系统药理学 (QSP) 随着新数据和新技术的发展而发展.
  • 先进的分析,机器学习 (ML) 和人工智能 (AI) 是这种进化的关键驱动力.
  • 整合多样化和大型数据集对于QSP进步至关重要.

研究的目的:

  • 设想未来的QSP与新兴技术和数据的整合.
  • 概述QSP在药物发现和开发的所有阶段的作用.
  • 确定QSP演变的关键策略及其对精准医学的影响.

主要方法:

  • 在QSP中集成ML/AI用于数据处理和模型模拟.
  • 在药物发现中应用QSP,预测in silico化合物的性能.
  • 使用非动物方法和患者数据的QSP用于临床前和临床开发.
  • 开发用于临床试验模拟的多维数字双胞胎和虚拟人群.

主要成果:

  • QSP模型将预测人类对新型化合物的早期反应.
  • QSP将增强从临床前患者到人类患者的理解和翻译.
  • 多维数字双胞胎和虚拟人群将指导临床试验和精准医学.
关键词:
临床试验模拟的临床试验模拟数字双胞胎是一个数字双胞胎.机器学习和人工智能机器学习和人工智能微生理系统是微生理系统.建模和模拟的模型和模拟.量化系统药理学 药理学虚拟患者是虚拟患者.

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  • 质量服务专家的角色将扩展到战略,数据评估,分析执行和新技术的伦理应用.
  • 结论:

    • QSP的未来在于集成先进的数据,分析和AI/ML等技术.
    • 这种整合将使整个药物发现和开发管道中的QSP应用成为可能.
    • 通过高影响力的应用,分析整合和效率提升的战略发展对于QSP在精密医学中的未来作用至关重要.