北京大学学报自然科学版 ›› 2019, Vol. 55 ›› Issue (6): 987-994.DOI: 10.13209/j.0479-8023.2019.115

上一篇    下一篇

前列腺癌的演化动力学模型以及治疗策略的优化

高鑫1, 史书毓2, 李方廷1,3,†   

  1. 1. 北京大学物理学院, 北京 100871 2. 北京大学第三医院, 北京 100191 3. 北京大学定量生物学中心, 北京 100871
  • 收稿日期:2019-05-09 修回日期:2019-06-04 出版日期:2019-11-20 发布日期:2019-11-20
  • 通讯作者: 李方廷, E-mail: lft(at)pku.edu.cn
  • 基金资助:
    国家自然科学基金(91130005, 11174011)资助

Evolutionary Dynamics Model of Prostate Cancer and Optimization of Treatment Strategies

GAO Xin1, SHI Shuyu2, LI Fangting1,3,†   

  1. 1. School of Physics, Peking University, Beijing 100871 2. Peking University Third Hospital, Beijing 100191 3. Center for Quantitative biology, Peking University, Beijing 100871
  • Received:2019-05-09 Revised:2019-06-04 Online:2019-11-20 Published:2019-11-20
  • Contact: LI Fangting, E-mail: lft(at)pku.edu.cn

PDF

摘要：

前列腺癌是男性常见的恶性肿瘤。临床上常用的干扰雄激素的治疗药物是阿比特龙, 但长期高剂量的阿比特龙治疗常导致患者前列腺肿瘤细胞进化至药物抵抗。为了揭示前列腺癌发展的动力学机制, 本文引入给药剂量因素, 建立二变量的肿瘤演化定量模型。通过分析固定给药和适应性给药策略的动力学性质, 并绘制势能景观图, 从动力学角度展示适应性给药策略的优势, 揭示高剂量药物导致体系演化为不可逆药物抵抗状态的动力学机理。此外还构建治疗策略的评分体系, 以便优化治疗策略。

关键词: 前列腺癌, 阿比特龙, 药物抵抗, 动力学模型, 策略优化

Abstract:

Prostate cancer is one of the most common malignant tumors in men. Abiraterone is commonly used in clinical treatment, but it often causes patients to evolve to drug resistance prostate cancer. In order to reveal the dynamic mechanism of prostate cancer development, this paper established a two-variable quantitative model of tumor evolution and introduced dose factors. By analyzing the dynamic properties of fixed and adaptive dosing treatment therapy and plotting the potential energy landscape figure, we revealed the advantages of adaptive dosing method from the perspective of dynamics and the mechanism that high doses of Abiraterone could lead to an irreversible state of drug resistant. In addition, the treatment score system was designed for the strategies, compared the two drug delivery methods and optimized the treatment strategies.

Key words: prostate cancer, Abiraterone, drug resistant, dynamic method, optimization strategy

版权所有 © 《北京大学学报（自然科学版）》编辑部
技术支持：北京玛格泰克科技发展有限公司