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相关概念视频

Bioequivalence of Drugs: Drugs with Multiple Indications01:09

Bioequivalence of Drugs: Drugs with Multiple Indications

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The concept of therapeutic equivalence (TE) in drugs with multiple indications is complex. A generic drug may be therapeutically equivalent to a brand-name product for one specific indication, but this doesn't necessarily mean it's equivalent for all other indications. Evidence of TE in one patient group and bioequivalence shown in healthy volunteers can support—but not confirm—TE for other indications. However, definitive proof requires individual clinical studies for each...
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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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...
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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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Pharmacogenomics: Identification of New Drug Targets01:29

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Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
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Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

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Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
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Combined Effects of Drugs: Synergism01:27

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Synergism is a useful mechanism where combining two or more drugs is more effective than each constituent used alone. Such combinations are also called supra-additive interactions. The drugs collectively enhance the final therapeutic effect by acting on different targets. Another advantage is that the low dose of each constituent drug is sufficient to achieve the desired effect. This helps reduce the duration of therapy and lower the adverse effects of these drugs.
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Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
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DVMMHGNN:一个双视图多模态异构图神经网络,用于微生物信息化药物重定位的对比学习.

Huan Li1, YingZhe Bai1,2, Yang Lv3

  • 1State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, P. R. China.

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概括
此摘要是机器生成的。

这项研究介绍了DVMMHGNN,这是一种用于药物重定向的新型计算框架,它集成了微生物数据. 它准确地预测了药物与疾病的关联,增强了制药开发策略.

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科学领域:

  • 计算生物学 计算生物学
  • 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:

  • 药物重新定位 (DR) 通过为现有药物找到新的用途来加速制药发展.
  • 准确识别药物与疾病的关联是至关重要的,但由于复杂的生物相互作用,具有挑战性.
  • 当前的方法往往忽视了微生物群的调节作用,并在数据集成中与语义一致性作斗争.

研究的目的:

  • 提出DVMMHGNN,一个微生物知情的异构图对比学习框架,用于增强药物重定位.
  • 解决现有的计算方法的局限性,特别是关于微生物群的影响和语义一致性的问题.

主要方法:

  • 开发了DVMMHGNN,使用异质图对比学习框架集成结构和元路径信息.
  • 采用多式联运特征融合模块用于跨式联运实体嵌入.
  • 使用图形掩盖的自动编码器进行高阶表示学习.
  • 在结构和元路径层面应用对比学习以增强语义连贯性.

主要成果:

  • 在预测药物疾病关联方面,DVMMHGNN显著超过了九种最先进的方法.
  • 取得了卓越的性能指标,包括AUC,AUPR和F1分数.
  • 废弃性研究证实了单个模型组件的有效性.

结论:

  • DVMMHGNN提供了一种强大的方法,用于微生物信息的药物重定位,提高预测准确度.
  • 该框架有效地捕捉了复杂的生物语义,以便更好地识别药物和疾病的关联.
  • DVMMHGNN有可能发现新的药物指示并指导治疗策略.