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相关概念视频

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

58
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...
58
Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

Physiological Pharmacokinetic Models: Assumption with Protein Binding

32
Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
32
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

587
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...
587
Nonlinear Pharmacokinetics: Overview01:19

Nonlinear Pharmacokinetics: Overview

278
Nonlinear or dose-dependent pharmacokinetics is a phenomenon that occurs when the pharmacokinetic parameters of certain drugs deviate from linear pharmacokinetics at higher doses. These drugs do not follow the expected first-order kinetics, where the rate of drug elimination is directly proportional to the drug concentration. Instead, they exhibit a nonlinear relationship, which can be attributed to several factors.
Nonlinearity can arise due to the saturation of plasma protein-binding or...
278
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

77
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...
77
Nonlinear Pharmacokinetics: Bioavailability and Protein-Drug Binding01:22

Nonlinear Pharmacokinetics: Bioavailability and Protein-Drug Binding

121
When a drug follows nonlinear pharmacokinetics, its bioavailability, the amount of the drug that reaches the systemic circulation, can change with different doses. This is due to the presence of a saturable pathway. The pathway becomes saturated as the drug concentration increases, decreasing the absorption rate. Consequently, the drug's bioavailability may be lower than expected at higher doses.
To quantify the extent of bioavailability, pharmacologists often use a parameter called .
121

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相关实验视频

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Diagonal Method to Measure Synergy Among Any Number of Drugs
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快速的多药副作用预测使用张量因子化.

Oliver Lloyd1, Yi Liu1, Tom R Gaunt1

  • 1MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, United Kingdom.

Bioinformatics (Oxford, England)
|November 25, 2024
PubMed
概括
此摘要是机器生成的。

优化的张量分解模型准确地预测药物组合不良反应. 简单E模型有效地实现了最先进的结果,为多药副作用预测提供了更快的替代方案.

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科学领域:

  • 计算化学是一种计算化学.
  • 药理学 药理学是指药理学的学科.
  • 生物信息学是一种生物信息学.

背景情况:

  • 药物组合的不良反应在医学中越来越令人担忧.
  • 实验室方法不足以预测这些组合效应.
  • 计算方法,包括张量分解 (TF),已经显示出潜力,但需要优化.

研究的目的:

  • 调查优化张量因子化模型对多药副作用预测的有效性.
  • 与现有方法相比,评估TF模型的性能和效率.
  • 确定TF模型中单药药房数据的最佳整合.

主要方法:

  • 利用张量分解 (TF) 模型,特别是SimplE模型,来预测多药副作用.
  • 在基于图表的方法中,集成的单药数据作为自循环边缘.
  • 在NVIDIA GPU上使用PyTorch 1.7.1的Python 3.8.12训练模型.

主要成果:

  • 简单的TF模型实现了最先进的性能,AUC ROC为0.978,AUC PR为0.971,AP@50为1.000在963种副作用中.
  • 该模型在两个培训时代 (大约2年) 内达到其峰值性能的98.3%. 4分钟),显示出显著的速度优势.
  • 将单药药房数据集成为自循环边缘产生了比使用它嵌入初始化略有更好的结果.

结论:

  • 优化的张量分解模型,如SimplE,对于预测多药副作用非常有效.
  • 与现有方法相比,这些模型提供了一个计算效率高,准确的解决方案.
  • 该研究强调了TF在促进药物安全和个性化医疗方面的潜力.