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関連する概念動画

Pharmacovigilance01:19

Pharmacovigilance

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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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Effects of Chemicals: Overview01:27

Effects of Chemicals: Overview

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Drugs, encompassing various chemical compounds from natural sources, lab synthesis, or genetic engineering, elicit different biological responses in living organisms. Some of these responses are desirable or therapeutic, while others are undesirable. The primary goal of administering a drug is to achieve a therapeutic effect, that is, to address a specific disease or health condition. Any concurrent effects outside of this therapeutic outcome are considered undesirable. These undesirable...
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Agonism and Antagonism: Quantification01:14

Agonism and Antagonism: Quantification

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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...
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Pharmacokinetics: Drug–Drug Interactions01:25

Pharmacokinetics: Drug–Drug Interactions

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Drug interactions occur when the pharmacological effect of one drug is altered by another substance, either enhancing or diminishing its activity. The drug whose activity is altered is known as the object drug, and the substance causing the alteration is called the agent drug or the precipitant. The net effects of these interactions are mostly undesirable, leading to decreased effectiveness or increased adverse effects. In rare cases, interactions can be beneficial, such as the enhanced...
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Therapeutic Drug Monitoring: Affecting Factors01:29

Therapeutic Drug Monitoring: Affecting Factors

175
Therapeutic Drug Monitoring (TDM) is the clinical practice of measuring specific drug levels in a patient's blood or body tissues to manage and optimize therapy. TDM is crucial for drugs with narrow therapeutic windows, like warfarin and phenytoin, where incorrect doses can lead to treatment failure or severe side effects. This monitoring ensures the dosage administered is within a safe and effective range. The factors affecting therapeutic drug monitoring include:Patient-Specific Factors:a.
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Combined Effects of Drugs: Antagonism01:30

Combined Effects of Drugs: Antagonism

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The combined effects of drugs can result in various interactions, of which an important type is antagonism. Antagonism is a mechanism where one drug inhibits or counteracts the effects of another drug. Antagonism can occur through various means, including receptor binding, allosteric modulation, functional interaction, chemical reactions, and pharmacokinetic processes.
The most common type is receptor antagonism, where one drug acts as an antagonist to block the effects of another drug by...
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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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因果知識グラフ分析による有害事象の特定

Sumyyah Toonsi1, Paul N Schofield2, Robert Hoehndorf1,3,4

  • 1Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia.

Bioinformatics (Oxford, England)
|December 12, 2025
PubMed
まとめ
この要約は機械生成です。

因果知識グラフ（CKG）を導入し、生物医学的知識グラフと因果推論を組み合わせました。このアプローチは、既知および新規の有害事象を特定し、薬剤適応症の予測を改善することに成功しました。

キーワード:
因果知識グラフ有害事象知識グラフ因果推論薬剤疾患予測モデリング

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科学分野:

  • 生物医学情報学
  • 因果推論
  • 知識表現

背景:

  • 生物医学的知識グラフと因果モデルは価値があるが、断絶している。
  • 知識グラフは確率的意味論を欠き、因果モデルは背景知識の統合を欠いている。

研究 の 目的:

  • 知識グラフと因果モデルの間のギャップを埋めること。
  • 原理的な因果推論と仮説生成のための因果知識グラフ（CKG）を開発すること。

主な方法:

  • 知識グラフに形式的な因果意味論を拡張し、因果知識グラフ（CKG）を作成しました。
  • 疾患経路、薬剤適応症、副作用を統合した疾患-薬剤CKG（DD-CKG）を構築しました。
  • DD-CKGをUKバイオバンクおよびMIMIC-IVに適用し、薬剤効果の大規模な媒介分析を行いました。

主要な成果:

  • CKGは、背景知識を用いた脱混絡と仮説生成を可能にしました。
  • 既知の有害事象を再現し、新規の候補事象を特定しました。
  • 予測された事象と既存のデータベースを組み合わせることで、共有された薬剤適応症の予測を改善しました。

結論:

  • CKGは、スケーラブルな因果推論のための一般化可能で知識駆動型のフレームワークを提供します。
  • この方法論は臨床的関連性を示し、自動化された大規模な媒介分析をサポートします。
  • このアプローチは、薬剤効果と疾患進行の理解を深めます。