# Structural–Functional Customization of Nanoscale Liposome-in-Liposome Systems: Precision Engineering Methodology and Artificial-Intelligence-Driven Design Prospects

**Authors:** Liutao Hu, Jianfeng Cai, Chao Lu

PMC · DOI: 10.34133/research.1168 · Research · 2026-02-19

## TL;DR

Researchers developed a new method to create complex liposome structures, which could improve drug delivery and advance artificial cell research.

## Contribution

A precision engineering methodology for fabricating sub-200-nm dual-layered liposomes with customizable bilayers and adjustable interbilayer spaces.

## Key findings

- A stepwise assembly approach enables controllable fabrication of liposome-in-liposome systems.
- The methodology allows integration of AI insights from unilamellar liposome research into complex liposome systems.
- This advancement may accelerate clinical translation of liposomal drugs and research in artificial organelles.

## Abstract

Liposome-in-liposome systems with multicompartment structures offer advantages in structural complexity and functional programmability, but their application is limited by poor controllability in conventional fabrication. A recent Nature Chemistry study by Elani et al. reports a methodology to prepare sub-200-nm dual-layered liposomes, enabling customizable bilayers and adjustable interbilayer spaces. Their stepwise assembly approach promises to facilitate the integration of artificial intelligence expertise from unilamellar liposome research into liposome-in-liposome systems, thereby further optimizing formulations and enabling biological predictions. Consequently, this advancement may accelerate the clinical translation of liposomal drugs and advance frontier research in areas such as artificial organelles and lipid nanoparticles.

## Full-text entities

- **Genes:** GLB1 (galactosidase beta 1) [NCBI Gene 2720] {aka EBP, ELNR1, MPS4B}
- **Diseases:** MVLs (MESH:D015223), infections (MESH:D007239), cytotoxicity (MESH:D064420), respiratory depression (MESH:D012131), inflammation (MESH:D007249), pain (MESH:D010146), cancer (MESH:D009369)
- **Chemicals:** calcein (MESH:C007740), lipid (MESH:D008055), alkyne (MESH:D000480), MVL (-), dexmedetomidine (MESH:D020927), fluorescein di-beta-d-galactopyranoside (MESH:C038848), methylene blue (MESH:D008751), phospholipid (MESH:D010743), ethanol (MESH:D000431), PEG (MESH:D011092), azide (MESH:D001386)

## Full text

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## Figures

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## References

20 references — full list in the complete paper: https://tomesphere.com/paper/PMC12917109/full.md

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Source: https://tomesphere.com/paper/PMC12917109