de Los Reyes Aurelio A, Kim Yangjin
Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon 34126, Republic of Korea.
Institute of Mathematics, University of the Philippines Diliman, Quezon City 1101, Philippines.
R Soc Open Sci. 2022 Feb 2;9(2):210705. doi: 10.1098/rsos.210705. eCollection 2022 Feb.
In a tumour microenvironment, tumour-associated neutrophils could display two opposing differential phenotypes: anti-tumour (N1) and pro-tumour (N2) effector cells. Converting N2 to N1 neutrophils provides innovative therapies for cancer treatment. In this study, a mathematical model for N1-N2 dynamics describing the cancer survival and immune inhibition in response to TGF- and IFN- is considered. The effects of exogenous intervention of TGF- inhibitor and IFN- are examined in order to enhance N1 recruitment to combat tumour progression. Our approach employs optimal control theory to determine drug infusion protocols that could minimize tumour volume with least administration cost possible. Four optimal control scenarios corresponding to different therapeutic strategies are explored, namely, TGF- inhibitor control only, IFN- control only, concomitant TGF- inhibitor and IFN- controls, and alternating TGF- inhibitor and IFN- controls. For each scheme, different initial conditions are varied to depict different pathophysiological condition of a cancer patient, leading to adaptive treatment schedule. TGF- inhibitor and IFN- drug dosages, total drug amount, infusion times and relative cost of drug administrations are obtained under various circumstances. The control strategies achieved could guide in designing individualized therapeutic protocols.
在肿瘤微环境中,肿瘤相关中性粒细胞可表现出两种相反的分化表型:抗肿瘤(N1)和促肿瘤(N2)效应细胞。将N2中性粒细胞转化为N1中性粒细胞为癌症治疗提供了创新疗法。在本研究中,考虑了一个描述对转化生长因子(TGF)和干扰素(IFN)反应的N1 - N2动力学的数学模型,该模型用于描述癌症存活和免疫抑制情况。研究了TGF抑制剂和IFN的外源性干预效果,以增强N1募集来对抗肿瘤进展。我们的方法采用最优控制理论来确定药物输注方案,该方案能以尽可能低的给药成本使肿瘤体积最小化。探索了对应不同治疗策略的四种最优控制方案,即仅TGF抑制剂控制、仅IFN控制、TGF抑制剂和IFN联合控制以及交替使用TGF抑制剂和IFN控制。对于每种方案,改变不同的初始条件以描绘癌症患者的不同病理生理状况,从而得出适应性治疗方案。在各种情况下获得了TGF抑制剂和IFN的药物剂量、总药量、输注次数以及药物给药的相对成本。所实现的控制策略可为设计个体化治疗方案提供指导。