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相关概念视频

Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

216
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...
216
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

55
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...
55
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

552
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...
552
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

72
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...
72
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
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...
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Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
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精密药物再定位 (PDR):患者级建模和预测,将基础知识图与生物库数据图相结合.

Çerağ Oğuztüzün1, Zhenxiang Gao2, Hui Li2

  • 1Center for Artificial Intelligence in Drug Discovery, Case Western Reserve University, 10900 Euclid Ave, Cleveland, 44106, OH, USA; Department of Computer Science, Case Western Reserve University, 10900 Euclid Ave, Cleveland, 44106, OH, USA.

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概括

精密药物重定向集成个体患者数据与知识图表,以发现个性化疗法. 多基因风险评分显著改善了阿尔茨海默病等疾病的药物优先级.

关键词:
阿尔茨海默氏症是阿尔茨海默氏症的一种疾病.药物重新定位是药物重新定位.图表 卷积网络 卷积网络知识图是知识图.多基因风险得分的多基因风险得分.精准医学是一门精准的医学.

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科学领域:

  • 生物医学信息学 生物医学信息学
  • 药物基因组学 药物基因组学
  • 计算生物学 计算生物学

背景情况:

  • 药物重用加速治疗的发展,但与个体患者的变异性作斗争.
  • 个性化医疗需要整合患者特定的数据,以进行量身定制的药物发现.

研究的目的:

  • 引入一个精确药物重定位 (PDR) 框架,用于单一患者的解决方案.
  • 通过将个人数据与生物医学知识图集在一起,实现个性化药物发现.

主要方法:

  • 开发了一个框架,将英国生物银行数据 (多基因风险评分,生物标志物,病史) 与生物医学知识图集在一起.
  • 用阿尔茨海默病作为案例研究,将患者特定的模型与使用链接预测的基础模型进行比较.
  • 通过患者药物历史和文献审查评估候选药物.

主要成果:

  • 该PDR框架保持了强大的预测能力,多基因风险评分显著影响了药物优先级 (科恩的d=1.05).
  • 废除研究证实了多基因风险评分 (PRS) 的关键作用.
  • 患者特异型模型确定了基础模型遗漏的新药候选者,通过药物历史和与遗传特征一致的文献进行验证.

结论:

  • 通过将患者特异性数据与知识图集集成,证明了精确药物重定向的有希望的方法.
  • 突出了多基因风险评分在个性化复杂疾病药物发现方面的潜力.