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Combination Therapies and Personalized Medicine02:50

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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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通过基因算法进行多目标优化框架,用于通过高热介导的药物输送.

Adabbo G1, Andreozzi A2, Iasiello M2

  • 1Dipartimento di Medicina e Scienze della Salute "Vincenzo Tiberio", Università del Molise, Via Francesco De Sanctis 1, 86100, Campobasso, Italy.

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概括

这项研究优化了使用多目标遗传算法对肝癌的高热介导药物递送. 这种方法最大限度地杀死癌细胞,同时最大限度地减少热损伤,改善治疗结果.

关键词:
转移生物热的转移生物热.药物交付是药物交付的过程.多目标优化多目标优化热敏脂质体的热敏脂质体是一种热敏脂质体.

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科学领域:

  • 生物医学工程 生物医学工程
  • 计算生物学 计算生物学
  • 在瘤学瘤学.

背景情况:

  • 肝细胞癌 (HCC) 治疗面临着热疗法和化疗联合治疗的挑战.
  • 目前用于高热介导药物递送的方法缺乏热和药物递送参数的最佳整合.
  • 热敏脂质体 (TSL) 具有在HCC中向药物递送的潜力,但需要精确的热控制.

研究的目的:

  • 开发和应用一个多目标优化框架,用于高热度介导的药物输送在HCC.
  • 确定最佳的设计变量,以最大限度地减少癌细胞的死亡,并最大限度地减少健康组织的损伤.
  • 为了提高基于TSL的肝癌化疗的疗效和安全性.

主要方法:

  • 计算流体动力学 (CFD) 与潘尼斯生物热方程和对流-扩散模型的整合.
  • 应用多目标遗传算法 (MOGA) 来优化加热功率,定时和天线配置.
  • 在模拟以评估各种优化参数下的治疗结果.

主要成果:

  • 具有特定加热时间表的两槽天线配置显示出最佳的治疗结果.
  • 实现了最大化的瘤药物度和最小化对周围健康组织的损伤.
  • 与以前的非优化方法相比,优化方法使死亡癌细胞的比例从10%增加到33%.

结论:

  • 拟议的MOGA框架显著提高了高热介导的HCC药物输送.
  • 优化治疗计划可以带来大大改善治疗结果和针对患者的具体策略.
  • 这种方法对推进具有减少副作用的向癌症疗法充满希望.