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相关概念视频

Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

1.9K
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...
1.9K
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

673
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...
673
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

240
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...
240
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

490
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
490
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

332
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.
332
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

358
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...
358

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在瘤学中构建混合药量测量-机器学习模型 药物开发:现状和建议

Anna Fochesato1, Logan Brooks2, Omid Bazgir2

  • 1Translational PKPD and Clinical Pharmacology, Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland.

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概括

标准化工作流程对于瘤药物开发中的混合药量测量机器学习模型 (hPMxML) 至关重要. 拟议的检查清单提高了这些先进模型的透明度,严格性和可重复性.

关键词:
临床药物开发 临床药物开发机器学习是机器学习.药学指标 药学指标 药学指标 药学指标

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科学领域:

  • 药量测量和机器学习
  • 计算机化药物开发 计算机化药物开发
  • 瘤学 治疗学 治疗学

背景情况:

  • 混合药量计机器学习模型 (hPMxML) 越来越多地用于瘤学药物开发和精密医学.
  • 缺乏标准化的工作流程,阻碍了透明度,严格性和有效的沟通,以实现更广泛的采用.

研究的目的:

  • 审查现有的药量计 (PMx) 和机器学习 (ML) 报告标准.
  • 将这些标准与瘤学中的hPMxML应用进行评估,以确定缺陷.
  • 提出缓解策略和hPMxML开发和报告的标准化检查清单.

主要方法:

  • 审查PMx和ML报告标准.
  • 对hPMxML瘤学研究的评估.
  • 识别当前实践中的差距.
  • 关于为hPMxML开发和报告提供综合检查清单的建议.

主要成果:

  • 确定的缺陷包括不充分的基准测试,缺少错误传播和功能稳定性评估,有限的外部验证和不适当的性能指标.
  • 建议制定一个检查清单,涵盖估计和定义,数据策划,共变量选择,超参数调整,趋同,可解释性,诊断,不确定性量化和验证.

结论:

  • 拟议的检查清单旨在提高hPMxML输出的可靠性和可重复性.
  • 标准化报告将促进信任,并使hPMxML在瘤学临床药物开发中的可靠应用成为可能.