Redefining Tumor Vascular Permeability through Deep Learning-Guided Microneedle Delivery.

Area of Science:

• Biomedical Engineering
• Nanotechnology
• Vascular Biology

Background:

• Low-permeability (LP) tumor vasculature hinders nanomedicine efficacy.
• Quantitative understanding of tumor vascular permeability is crucial for drug delivery.
• Developing strategies to overcome these vascular barriers is essential.

Purpose of the Study:

• To introduce a deep learning-guided microneedle (MN) platform for precise tumor vasculature modulation.
• To enhance nanoparticle extravasation into tumors.
• To quantitatively map vascular remodeling and nanoparticle transport.

Main Methods:

• Utilized a microneedle (MN) delivery system for localized histamine administration.
• Integrated the MN system with the nano-ISML 1.1 single-vessel analysis framework.
• Quantitatively analyzed vascular remodeling and nanoparticle transport in diverse tumor models.

Main Results:

• Localized histamine delivery via MNs selectively expanded endothelial junctions, increasing interendothelial gaps.
• Reprogrammed LP tumors to a high-permeability phenotype, enhancing nanoparticle extravasation.
• Established a size-dependent permeability window, allowing nanoparticles ≤200 nm while excluding >500 nm particles.

Conclusions:

• The developed MN platform enables controlled modulation of tumor vasculature.
• This approach overcomes vascular barriers for improved nanomedicine delivery.
• The interdisciplinary framework offers a mechanistic and predictive paradigm for tumor-targeted nanomedicines.