Bio-Inspired Navigation for Intelligent Nanoscale Drug Delivery Systems Based on Chemotaxis and Entropy

IF 2.5 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Molecular, Biological, and Multi-Scale Communications Pub Date : 2026-01-01 Epub Date: 2026-07-02 DOI:10.1109/TMBMC.2026.3708193

Bahram Hedayati;Karthik Reddy Gorla;Maurizio Magarini

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引用次数: 0

Abstract

Biological intelligence provides a powerful paradigm for developing novel computational and communication frameworks inspired by living systems. Addressing complex diseases such as cancer, which remains one of the most persistent global health challenges, requires intelligent and targeted therapeutic strategies capable of distinguishing and eliminating malignant cells while preserving surrounding healthy tissue. In this work, we propose a biologically inspired navigation framework that combines chemotaxis, a natural process that governs directional cell movement in response to chemical gradients, with entropy exploration to guide nanoscale medical agents (NMA) toward cancerous regions within the tumor microenvironment (TME). The hybrid chemotaxis–entropy mechanism emulates adaptive decision making observed in biological organisms, enabling autonomous localization and tumor treatment through targeted drug delivery. Using hypoxia as a biomarker, due to the elevated oxygen consumption of cancerous cells, numerical simulations are performed for both single- and multi-tumor configurations. Comparative analyses in multiple performance metrics reveal that the hybrid navigation approach achieves higher efficiency, reliability, and tumor elimination rates compared to random and pure chemotaxis strategies. Furthermore, the feasibility of biologically grounded navigation is demonstrated to be significantly enhanced through an entropy-guided perspective, highlighting its potential for intelligent control and communication in nanoscale systems. This study establishes a computational framework linking biological intelligence, molecular communication, and nanoscale drug delivery, laying the foundation for future bio-inspired optimization of drug localization in cancer therapy. Testing

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基于趋化性和熵的智能纳米给药系统仿生导航

生物智能为开发受生命系统启发的新型计算和通信框架提供了一个强大的范例。应对复杂疾病，如癌症，这仍然是最持久的全球健康挑战之一，需要智能和有针对性的治疗策略，能够区分和消除恶性细胞，同时保留周围的健康组织。在这项工作中，我们提出了一种受生物学启发的导航框架，该框架结合了趋化性（一种根据化学梯度控制定向细胞运动的自然过程）和熵探索，以指导纳米级医学药物（NMA）在肿瘤微环境（TME）内的癌变区域。混合趋化-熵机制模拟了生物有机体中观察到的自适应决策，通过靶向给药实现自主定位和肿瘤治疗。利用缺氧作为生物标志物，由于癌细胞的氧气消耗增加，对单个和多个肿瘤配置进行了数值模拟。多种性能指标的对比分析表明，与随机和纯趋化策略相比，混合导航方法具有更高的效率、可靠性和肿瘤消除率。此外，从熵引导的角度来看，生物接地导航的可行性得到了显著增强，突出了其在纳米级系统中的智能控制和通信潜力。本研究建立了一个连接生物智能、分子通信和纳米级药物传递的计算框架，为未来癌症治疗中药物定位的仿生优化奠定了基础。测试

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Molecular, Biological, and Multi-Scale Communications Mathematics-Modeling and Simulation

CiteScore

3.90

自引率

13.60%

发文量

期刊介绍： As a result of recent advances in MEMS/NEMS and systems biology, as well as the emergence of synthetic bacteria and lab/process-on-a-chip techniques, it is now possible to design chemical “circuits”, custom organisms, micro/nanoscale swarms of devices, and a host of other new systems. This success opens up a new frontier for interdisciplinary communications techniques using chemistry, biology, and other principles that have not been considered in the communications literature. The IEEE Transactions on Molecular, Biological, and Multi-Scale Communications (T-MBMSC) is devoted to the principles, design, and analysis of communication systems that use physics beyond classical electromagnetism. This includes molecular, quantum, and other physical, chemical and biological techniques; as well as new communication techniques at small scales or across multiple scales (e.g., nano to micro to macro; note that strictly nanoscale systems, 1-100 nm, are outside the scope of this journal). Original research articles on one or more of the following topics are within scope: mathematical modeling, information/communication and network theoretic analysis, standardization and industrial applications, and analytical or experimental studies on communication processes or networks in biology. Contributions on related topics may also be considered for publication. Contributions from researchers outside the IEEE’s typical audience are encouraged.