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相关概念视频

Treatment Resistant Cancers02:56

Treatment Resistant Cancers

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Cancer is the second leading cause of death in the United States. A cancer cell is genetically unstable and hence can mutate faster. They can also modify their microenvironment and escape immune surveillance. The difficulties in treating cancer are further compounded by the emergence of rapid resistance to anticancer drugs. The most common ways to attain resistance in cancer cells include alteration in drug transport and metabolism, modification of drug target, elevated DNA damage response, or...
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Protein Networks02:26

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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,...
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Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

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Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
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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.
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The targeted cancer therapies, also known as “molecular targeted therapies,” take advantage of the molecular and genetic differences between the cancer cells and the normal cells. It needs a thorough understanding of the cancer cells to develop drugs that can target specific molecular aspects that drive the growth, progression, and spread of cancer cells without affecting the growth and survival of other normal cells in the body.
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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.
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Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms
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系统级网络数据和模型攻击癌症药物耐药性.

Márk Kerestély1, Dávid Keresztes1, Levente Szarka1

  • 1Department of Molecular Biology, Semmelweis University, Budapest, Hungary.

British journal of pharmacology
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概括

耐药性是癌症死亡的主要原因,是一种复杂的细胞网络现象. 数据集成方面的进步现在使得能够创建全面的,全蛋白质组范围的模型来对抗癌症药物耐药性.

关键词:
人工智能的人工智能是人工智能.表皮细胞-介质细胞过渡.网络药理学 网络药理学个性化医疗是个性化的医疗.一些抗体对抗.

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科学领域:

  • 系统生物学 系统生物学
  • 在瘤学瘤学.
  • 生物信息学是一种生物信息学.

背景情况:

  • 耐药性占癌症相关死亡人数的90%以上,这在瘤学中是一个关键的挑战.
  • 癌症药物耐药性被理解为涉及整个细胞的系统级网络现象.
  • 之前的研究依赖于小规模的互动体,限制了全面的理解.

研究的目的:

  • 为了利用最近在蛋白质组范围内的相互作用和信号网络数据方面的进展.
  • 整合药物向相互作用,耐药性突变和多omics数据集.
  • 为了为构建全蛋白体抗药性模型铺平道路.

主要方法:

  • 利用全蛋白质组的人类互动组和信号网络数据.
  • 整合药物向相互作用和诱导耐药性的突变.
  • 纳入与癌症和药物耐药性相关的多omics数据集.

主要成果:

  • 开发系统级信号网络模型,用于治疗耐药性.
  • 能够在 silico 进行临床试验和药物组合选.
  • 建立了耐药性网络数据和模型的互操作性和可靠性.

结论:

  • 最近的数据整合进步使得构建全蛋白体耐药性模型成为可能.
  • 这些模型对于理解和克服癌症抗药性至关重要.
  • 这些发现支持有针对性的药物开发和个性化癌症疗法.