A Bio-Nano Systems Interconnection Hierarchical Network Model for Targeted Drug Delivery
Molecular communication (MC), an innovative paradigm leveraging molecules as information carriers, is gaining traction in the biomedical field, particularly in the context of the Internet of Bio-Nano Things (IoBNT) for targeted drug delivery (TDD) systems. Specifically, the transportation of drug molecules in blood vessels is described as the propagation of information molecules, offering an MC perspective to designing and optimizing TDD processes. However, the existing MC-inspired TDD is predominantly physical-layer-centric, grappling with the pharmacokinetics (PK) and pharmacodynamics (PD) of drug molecules in complex vessel networks and the diversity of drug carrier designs. These complexities result in high-computational demands, posing a significant challenge to implementing personalized TDD strategies. This article introduces a three-layer bio-nano systems interconnection (BNSI) hierarchical model to address these challenges by reducing the computational load and enabling a more precise TDD strategy. Our model extracts parameters from the physical layer, which handles the PK and PD processes, and maps drug dynamics to data transmission at the network layer. This approach draws inspiration from traditional communication systems, where the physical layer manages the propagation of signals, and the network layer oversees packet routing and topology as the path planning in the blood vessel network. The application layer in our model incorporates a feedback mechanism based on drug concentration at the diseased site, allowing for flexible adjustments to achieve the desired therapeutic outcomes. Simulation results demonstrate that the BNSI model enhances computational efficiency without compromising the accuracy of TDD, thus demonstrating its feasibility. This work improves the scalability and flexibility of TDD systems and lays the groundwork for future TDD’s optimization and digital transformation.
Authors
- Yue Sun (ORCID: https://orcid.org/0000-0002-3789-4453)
- Haowen Tan (ORCID: https://orcid.org/0000-0001-9807-7185)
- Yifan Chen (ORCID: https://orcid.org/0000-0001-7645-623X)
Institutions
- University of Electronic Science and Technology of China (CN)
Publication Details
- Journal
- IEEE Internet of Things Journal
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1109/jiot.2025.3598014
- Citations
- 1
- Primary Topic
- Molecular Communication and Nanonetworks
- Type
- article
- Field-Weighted Citation Impact
- 0.67
Funders
- National Natural Science Foundation of China