Individual Adaptive Regulation Strategy Inspired by Artificial Fish Swarm Algorithm for Tumor Targeting
Yue Sun, Shanchao Wen, Shaolong Shi, Yifan Chen
Abstract
The use of nanoparticles for tumor-targeted therapy has become an emergent topic in molecular communications due to the similarity in information propagation and drug delivery. This paper introduces a novel approach called individual adaptive regulation strategy (IARS) to enhance tumor targeting, drawing inspiration from the collective behavior of fish swarms. This approach does not require any prior knowledge of tumor location. The goal is to leverage the intelligence and adaptability of fish swarms to improve drug delivery efficiency and effectiveness and enhance the early-stage tumor detection rate. The approach integrates the perceptual information of nanoswimmers (NSs) with the biological gradient fields (BGFs) induced by tumors, which departs from the existing approaches that rely solely on the information perception of a single nanoparticle to the BGFs. IARS can dynamically adjust the motion direction of NSs in response to the characteristics of the tumor microenvironment. Extensive simulations and experiments demonstrate the efficacy and resilience of the proposed strategy, indicating promising outcomes in cancer treatment through targeted drug delivery.