Targeted Drug Delivery: Algorithmic Methods for Collecting a Swarm of Particles with Uniform External Forces

TL;DR

This paper explores algorithmic strategies for controlling a swarm of micro-particles using uniform external forces to reach a target in complex environments, addressing NP-completeness and proposing improved algorithms validated by simulations.

Contribution

It introduces new algorithmic approaches with better worst-case guarantees for targeted drug delivery using uniform external forces, supported by simulation results.

Findings

NP-complete nature of the problem explains previous limitations

Proposed algorithms significantly reduce actuation steps

Simulations show practical effectiveness of the methods

Abstract

We investigate algorithmic approaches for targeted drug delivery in a complex, maze-like environment, such as a vascular system. The basic scenario is given by a large swarm of micro-scale particles (''agents'') and a particular target region (''tumor'') within a system of passageways. Agents are too small to contain on-board power or computation and are instead controlled by a global external force that acts uniformly on all particles, such as an applied fluidic flow or electromagnetic field. The challenge is to deliver all agents to the target region with a minimum number of actuation steps. We provide a number of results for this challenge. We show that the underlying problem is NP-complete, which explains why previous work did not provide provably efficient algorithms. We also develop several algorithmic approaches that greatly improve the worst-case guarantees for the number of required actuation steps. We evaluate our algorithmic approaches by numerous simulations, both for deterministic algorithms and searches supported by deep learning, which show that the performance is practically promising.