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相关概念视频

Pharmacovigilance01:19

Pharmacovigilance

773
Post-marketing surveillance is a critical component of pharmaceutical regulation, often uncovering unanticipated adverse drug reactions (ADRs) once a drug is widely used over an extended period.
This process, termed pharmacovigilance, aims to detect, evaluate, and minimize harmful effects related to medication use. The data collection for pharmacovigilance depends on spontaneous reporting systems, where healthcare professionals or patients voluntarily report suspected ADRs.
In some cases, there...
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Protein Networks02:26

Protein Networks

3.9K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
3.9K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

56
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...
56
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...
232
Agonism and Antagonism: Quantification01:14

Agonism and Antagonism: Quantification

322
When drugs are administered, they can elicit either an agonist or antagonist effect on the body. Agonism occurs when a drug activates a specific receptor, triggering a biological response. On the other hand, antagonism happens when a drug binds to the same receptors but blocks their activation, thereby preventing a biological response.
To quantify these effects, researchers use a dose-response curve, which provides valuable information about the potency and efficacy of a drug. Potency refers to...
322
Drug-Receptor Interaction: Antagonist01:28

Drug-Receptor Interaction: Antagonist

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An antagonist is a drug that binds strongly to a receptor without activating it. An antagonist prevents other molecules, such as neurotransmitters or hormones, from binding to the receptor and triggering a cellular response. Such interaction effectively hinders the normal physiological processes mediated by the receptor, resulting in various pharmacological effects depending on the specific receptor targeted.
Antagonists can be classified as competitive or noncompetitive based on their...
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相关实验视频

Updated: Jun 5, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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精确的药物不良反应预测与异质图神经网络.

Yang Gao1,2, Xiang Zhang3, Zhongquan Sun1

  • 1Department of Hepatobiliary and Pancreatic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|December 4, 2024
PubMed
概括

精确ADR使用异质图形神经网络 (GNN) 准确预测患者水平的不良药物反应 (ADR). 这种新的框架通过超越传统方法来捕捉个体复杂性,提高了患者的安全性.

关键词:
美国食品和药物管理局的不良事件报告系统 (FAERS)药物不良反应 药物不良反应图表神经网络的神经网络精准医学是一门精准医学.

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科学领域:

  • 计算药理学是一种计算药理学.
  • 生物医学信息学是生物医学信息学.
  • 机器学习在医疗保健中的应用

背景情况:

  • 预测患者水平的不良药物反应 (ADR) 对安全性和结果至关重要.
  • 传统方法与个体患者的人口统计学和ADR变异作斗争.
  • 现有的模型经常预测药物级别的副作用,而不是患者特定的风险.

研究的目的:

  • 提出一个新的框架,准确的药物不良反应 (PreciseADR),用于患者级别的ADR预测.
  • 通过整合患者特定数据来克服传统方法的局限性.
  • 提高对个体患者潜在副作用的鉴定准确度.

主要方法:

  • 构建了一个异质图,包括患者,疾病,药物和ADR.
  • 利用异质图形神经网络 (GNN) 来学习患者的嵌入.
  • 在图形结构中纳入患者-疾病和患者-药物关系.

主要成果:

  • 精确ADR在一个大规模的现实世界医疗保健数据集 (FAERS) 上表现出卓越的预测性能.
  • 与最强的基线相比,获得了3.2%更高的AUC得分和4.9%更高的Hit@10.
  • 有效地捕捉到本地和全球依赖性,以识别微妙的ADR模式.

结论:

  • 精确ADR为准确的患者级别ADR预测提供了一种强大的方法.
  • 基于GNN的框架有效地模拟了影响ADR的复杂相互作用.
  • 这一进步为改善患者安全和个性化医疗提供了巨大的潜力.