ArticleNano convergence2026
Machine learning-driven exosome-mimetic lipid nanoparticles for tumor-specific targeting.
Article in Nano convergence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Cancer Vaccine Development: Toward Artificial Intelligence-Assisted Personalized Cell Membrane Nanovaccine.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Programmable nanomedicine via bioorthogonal molecular engineering.Nano convergence · 2026Review
- Engineering smart polymeric lipid nanoparticles for breast cancer: AI-guided formulation design, biological barriers, and translational constraints.Journal of nanobiotechnology · 2026Review
- Engineered Exosomes in Precision Neuro-Oncology: Mechanisms, Therapeutics, and Translational Challenges.Cancers · 2026Review
- Advances in Drug Delivery Systems for Breast Cancer: From Microenvironment Barriers and Smart Carriers to Clinical Translation Strategies.Drug design, development and therapy · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Exosome-mimetic lipid nanoparticles (ENPs) are a promising alternative to PEGylated lipid nanoparticles (LNPs) for targeted cancer therapy, offering improved biocompatibility and reduced immune clearance. However, the rational design of these biomimetic particles is challenging due to complex lipid composition requirements. We developed a hybrid algorithm to optimize exosome-mimetic formulations by predicting key nanoparticle properties (size, zeta potential, and polydispersity index). The algorithm was trained on an expanded dataset of 17,800 lipid compositions generated by augmenting experimental and publicly available data using the LipidGAN generative model, incorporating physicochemical modeling and feature extraction. It identified optimal formulations, which were validated in vitro across three cancer cell lines (HeLa, H1975, and MCF-7). Cytotoxicity assays confirmed minimal toxicity (cell viability > 90%), and uptake studies demonstrated efficient, cell-type-specific internalization (91 ~ 95%). These results highlight the potential of artificial intelligence (AI)-driven lipid design to emulate the functionality of natural exosomes and advance the development of safe, effective, and personalized cancer nanomedicines.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.