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相关概念视频

Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

109
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.
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Dose-Response Relationship: Selectivity and Specificity01:25

Dose-Response Relationship: Selectivity and Specificity

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Drugs exert their therapeutic effects by interacting with receptors, enzymes, or ion channels that are present throughout the human body. The strength and duration of the interaction between a drug and its target receptor are characterized by the selectivity and specificity of the drug. Selectivity refers to a drug's strong preference for its intended target over other targets. For instance, isoprenaline, a non-selective β-adrenergic agonist, interacts with both β1- and...
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Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
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Combined Effects of Drugs: Synergism01:27

Combined Effects of Drugs: Synergism

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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.
Such synergistic combinations...
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Dose-Response Relationship: Overview01:03

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Agonists can bind with and activate receptors, resulting in the formation of drug-receptor complexes. Once formed, these complexes catalyze many biochemical processes at the cellular level and subsequently induce a pharmacologic response. The degree of response is directly proportional to the fraction of activated receptors, which in turn, depends on the concentration of the drug at the receptor site as well as the sensitivity of the receptor. An increase in the administered dose contributes to...
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Dose-Response Relationship: Potency and Efficacy01:22

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The potency of a drug is the measure of its ability to produce a biological response and can be compared by looking at the half-maximum effective concentration or EC50 values of different drugs. A lower EC50 value indicates higher potency of the drug. In the dose–response curve of two antihypertensive drugs, candesartan and irbesartan, a significant difference is observed in their EC50 values. A lower EC50 value for candesartan indicates that it is more potent than irbesartan, as it...
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High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method
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使用多目标协同效应优化指导模型驱动的组合剂量选择.

Jana L Gevertz1, Irina Kareva2

  • 1Department of Mathematics and Statistics, The College of New Jersey, Ewing, New Jersey, USA.

CPT: pharmacometrics & systems pharmacology
|July 7, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新方法,MOOCS-DS,通过分析药物协同作用来优化组合癌症治疗剂量. 它有助于选择最佳的药物组合和剂量,以改善癌症治疗结果.

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科学领域:

  • 在瘤学瘤学.
  • 药理学 药理学是指药理学的学科.
  • 计算生物学 计算生物学

背景情况:

  • 结合癌症疗法对于未来的治疗方法至关重要.
  • 选择最佳的药物组合和剂量仍然具有挑战性.

研究的目的:

  • 引入组合协同效应 - 剂量选择 (MOOCS-DS) 方法的多目标优化.
  • 利用药物协同作用来指导组合疗法的剂量选择.
  • 解和分析强度协同作用 (SoP) 和功效协同作用 (SoE).

主要方法:

  • 开发了MOOCS-DS算法用于多目标协同分析.
  • 在多目标协同空间中确定了帕雷托最佳解决方案.
  • 将该方法应用于玩具模型和临床前数据 (肺癌中的 pembrolizumab + bevacizumab).

主要成果:

  • MOOCS-DS将SoP和SoE脱,以进行全面的协同效应评估.
  • 证明了剂量选择如何受到协同效应指标的影响.
  • 在临床前模型中展示了指导剂量和时间表选择的潜力.

结论:

  • MOOCS-DS方法提供了一种系统的方法来优化组合治疗剂量.
  • 这种方法可以为组合疗法的临床前实验设计提供信息.
  • 改进的剂量选择有可能提高组合癌症治疗的成功率.