Intelligent Nanoantenna‑Based Drug Delivery System for Targeted Cancer Therapy

Introduction

Traditional cancer therapies—surgery, radiation and chemotherapy—often damage healthy tissues and can leave residual malignant cells. Nanotechnology offers a targeted alternative by exploiting the unique physicochemical properties of particles 1–100 nm in size. These nanoparticles can be functionalised with ligands, antibodies or nucleic acid chains that recognise tumour‑specific markers, enabling precise drug delivery and theranostics. Moreover, nanoparticle‑mediated approaches can enhance the efficacy of conventional treatments, reducing side effects and improving survival rates.

The design of a nanoparticle system that actively senses the tumour micro‑environment and self‑optimises its delivery strategy is the focus of this work. We combine a nano‑controller, nano‑antenna‑decorated carriers and a compressive binary search algorithm to guide drug‑laden particles through the lymphatic network directly to malignant cells. This approach promises a high‑throughput, low‑resistance delivery route that could transform cancer treatment.

Compound nanoparticle drug delivery system and its interaction with lymph nodes

Regulatory bodies such as the FDA classify nanomaterials as particles 1–100 nm that exhibit distinct bulk properties. Solid lipid nanoparticles (SLNs) and their successors, nanostructured lipid carriers (NLCs), provide a robust core for drug encapsulation. Polymeric nanoparticles (PN) can be engineered from natural or inorganic polymers, forming either nanocapsules or nanospheres. These carriers improve drug stability and allow precise control over size and shape, which is critical for navigating the lymphatic system.

Given the central role of lymph fluid in cancer biology, we engineered a composite nanoparticle system that targets lymph nodes. The subsequent sections describe the system’s architecture, the underlying physics, and its performance in vitro.

Searching for the Target Lymphatic Nodes Using Compressive Binary Search

To accelerate particle delivery, we employed a compressive binary search algorithm. The algorithm interprets exploratory nanoparticle feedback to iteratively partition the search space until the target lymph node (Tf) is located. Each iteration refines the particle trajectory, ensuring a rapid, energy‑efficient arrival at the malignant site.

Results and Discussion

We evaluated the proposed system using five low‑density materials: silicone, lithium, lung, helium, and hydrogen. Parameters were set to g = 9.80665 m/s², particle diameter d = 10 Å, lymph fluid density ρf = 998.28 kg/m³ and dynamic viscosity 0.0010 kg m⁻¹ s⁻¹. Figure 4 displays the particle density for each material; Figure 5 shows their settling velocities; Figure 6 illustrates how these velocities influence lymphocyte density.

Silicone nanoparticles exhibited a settling velocity of –2.87 × 10⁻¹⁵ m/s, reducing lymph fluid density to 987.72 kg/m³. Hydrogen particles produced the most pronounced effect, collapsing lymphatic resistance to –856.28 kg/m³, effectively eliminating flow resistance. Helium mirrored this trend, confirming that low‑density particles are most effective at lowering viscosity.

Density of nanoparticles for the five selected materials

The settling velocity data for each particle

The effect of the settling velocity of nanoparticles on changing the lymphocyte density of cancer cells

Figure 7 and 8 demonstrate the inverse relationship between particle diameter and group size for lung and lithium nanoparticles, respectively. The smallest particles (≈ 10 Å) require the largest group sizes to achieve sufficient drug payload, whereas larger particles need fewer copies. Figures 9–13 further detail material compositions, average masses, group widths, and statistical variations, underscoring the superior performance of hydrogen and helium in reducing lymphatic viscosity.

Group of nanoparticles in the lung cells and their number in one of the proposed groups

Group of nanoparticles in the lithium cells and their number in one of the proposed groups

Different sets of materials proposed to have the mean highest density of both hydrogen and helium materials

Average mass of a nanoparticle in a group

Diameters of the nanoparticles related to the group width

The standard deviation of lung and lithium nanoparticles coefficients

The standard deviation of the mass for particles of silicones, lithium, lungs, helium, and hydrogen in one group

Methods

Our experimental workflow consists of the following stages:

1. Low‑density nanoparticle synthesis: We engineered carriers with densities below that of lymph fluid, enabling rapid descent and minimal resistance.
2. Preparation of anaerobic nanoparticles: Each particle was functionalised with a nano‑antenna to establish a two‑way link with the controller.
3. Nano‑controller fabrication: A metal‑based, piezoelectric micro‑controller stores a small memory buffer and manages particle release via nano‑gates.
4. Compressive binary search deployment: The controller interrogates exploratory particles and refines delivery paths until the target lymph node is reached.

All procedures were conducted in compliance with current EU and US nanomaterial regulations, ensuring safety and reproducibility.

Conclusion

Our intelligent nano‑antenna‑based drug delivery system demonstrates that smaller, low‑density particles—particularly hydrogen—significantly lower lymphatic fluid viscosity, enhancing drug transport to cancer cells. The anaerobic nano‑controller accurately modulates particle deployment, delivering therapeutics with higher precision and speed than conventional methods. These findings support the clinical potential of nano‑engineered drug delivery platforms for targeted cancer therapy.

Availability of Data and Materials

The datasets supporting the results of this article are included within the article.

Abbreviations

LN:

Lipid nanoparticles

NLC:

Nanostructured lipid carriers

PN:

Polymeric nanoparticles

SLNs:

Solid lipid nanoparticles