Objective: The performance of targeted immuno-chemotherapy of tumor is highly exposed to drug absorption in systemic circulation, which reduces its efficiency and increases side-effects. Direct drug targeting (DDT) combined with immuno-chemotherapy has the potential to mitigate the undesired systemic exposure, by using drug-loaded nanorobots to target cancer cells with the shortest possible physiological routes. This process can be modeled by the “touchable” (i.e., externally controllable and trackable) molecular communication system. However, such a complex process still suffers from various pharmacokinetic uncertainties caused by diffusion, degeneration, and branching of nanorobots (DDT pharmacokinetic uncertainties), as well as tumor/immune system modeling errors. The current work aims at identifying optimal drug administration plans by overcoming such challenges. Methods: A revisited tumor-immune interaction model is proposed to incorporate randomness of the drug concentration in the tumor site. Then, a robust multiple model predictive control (MMPC) scheme for the proposed tumor-immune interaction model is designed that uses multiple system models and an adaptive switch to identify the optimal plans for mixed drug administration via drug-loaded nanorobots. Furthermore, a wide range of prediction horizons under different loss scenarios of drug-loaded nanorobots and system model mismatches have been investigated in order to identify safe operating regions. From the molecular communications paradigm, this can be considered as a more robust information transmission system with feedback of channel state information to the transmitter implemented in the control unit. Results: The efficacy of the proposed MMPC is illustrated through identification of globally optimized drug administration schedules subject to various controller operation imperfections, which lead to successful cancer treatment as demonstrated through computational experiments. Conclusion: By combining DDT with conventional mixed immunotherapy and chemotherapy, the proposed robust MMPC offers promising performance in controlling tumor growth while keeping the immune cell density higher than a specific level in the presence of both DDT pharmacokinetic uncertainties and system model mismatches. Significance: We believe that the proposed design framework represents an important first step towards clinically relevant DDT in the combined immunotherapy and chemotherapy of tumor given its robust performance.

中文翻译：

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克服直接药物靶向辅助免疫化学疗法的可触摸分子通讯中的通道不确定性。

目的：肿瘤的靶向免疫化学疗法的性能高度暴露于全身循环中的药物吸收，这降低了其效率并增加了副作用。直接药物靶向（DDT）结合免疫化学疗法具有潜力，可以通过使用载有药物的纳米机器人以尽可能短的生理途径靶向癌细胞来减轻不良的全身性暴露。该过程可以通过“可触摸”（即，外部可控制和可跟踪）分子通信系统建模。然而，这种复杂的过程仍然遭受由纳米机器人的扩散，变性和分支引起的各种药代动力学不确定性（DDT药代动力学不确定性）以及肿瘤/免疫系统建模错误。方法：提出了重新审视的肿瘤-免疫相互作用模型，以纳入肿瘤部位药物浓度的随机性。然后，针对所提出的肿瘤-免疫相互作用模型设计了一种鲁棒的多模型预测控制（MMPC）方案，该方案使用多个系统模型和一个自适应开关来确定混合药物给药的最佳方案通过载药纳米机器人。此外，为了确定安全的操作区域，已经研究了载药纳米机器人在不同损失情况下的广泛预测范围以及系统模型不匹配。从分子通信范式来看，这可以被认为是一种更健壮的信息传输系统，具有将信道状态信息反馈到控制单元中实现的发射器的功能。结果： 拟议的MMPC的功效通过确定受各种控制器操作缺陷影响的全局优化药物给药方案来说明，这通过计算实验证明可以成功治疗癌症。 结论： 通过将DDT与传统的混合免疫疗法和化学疗法相结合，在存在DDT药代动力学不确定性和系统模型不匹配的情况下，所提出的强大的MMPC在控制肿瘤生长的同时提供了有希望的性能，同时保持免疫细胞密度高于特定水平。 意义： 我们认为，鉴于其强大的性能，拟议的设计框架代表了在临床上相关的滴滴涕在肿瘤的免疫治疗和化学疗法结合方面迈出的重要的第一步。