基于DDQN的最佳向疗法,使用可逆抑制剂来对抗华堡效应
Jose M Sanz Nogales1, Juan Parras1, Santiago Zazo1
1Information Processing and Telecommunications Center, Universidad Politécnica de Madrid, ETSI Telecomunicación, Av. Complutense 30, 28040 Madrid, Spain.
Mathematical biosciences
|July 6, 2023
概括
这项研究使用三种细胞表型来模拟华堡效应,揭示了乳酸作为预后不良的指标. 它还使用强化学习开发了针对攻击性瘤的最佳向疗法.
科学领域:
- 在癌症生物学中的代谢重编程.
- 计算瘤学是一种计算瘤学.
- 瘤生态系统的进化建模.
背景情况:
- 沃伯格效应描述了癌细胞的新陈代谢变化,甚至在有氧的情况下也有利于糖解.
- 瘤异质性涉及不同的代谢表型,影响治疗耐药性.
- 一些瘤细胞产生的乳酸可以为其他瘤细胞提供燃料,有助于攻击性生长.
研究的目的:
- 开发一个Warburg效应的三组分进化模型.
- 了解代谢变化及其对侵袭性瘤的临床影响.
- 使用计算方法设计一种最佳的,个性化的向治疗方法.
主要方法:
- 一个模拟不同代谢表型的三组分进化模型 (糖溶性,乳酸利用,氧化酸化).
- 分析临床试验数据,特别是结直肠癌.
- 训练双深Q网络 (DDQN) 强化学习算法,以实现最佳的治疗设计.
- 根据汉密尔顿-雅各比-贝尔曼方程对DDQN衍生疗法的验证.
主要成果:
- 该模型复制了对侵袭性瘤的临床观察结果,并确定乳酸盐是不良的预后标记物.
- 乳酸酸促进瘤异质性,使治疗策略复杂化.
- 该DDQN算法产生了最佳的向疗法,考虑到患者的生活质量,药物剂量和禁忌.
- 在疗法得到了验证,证明了计算可行性.
结论:
- 沃伯格效应是由瘤内的不同相互作用的代谢表型驱动的.
- 乳酸是瘤进展和治疗耐药性的关键因素.
- 强化学习为开发个性化,最佳的癌症疗法提供了一种强有力的方法.
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