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Decoding Extrahepatic Targeting of Lipid Nanoparticles with Interpretable Machine Learning — arXiv:2609.17721

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.17721
License (as archived): CC0-1.0
Archived: 2026-10-15T15:05:10.859Z
SHA-256 of the archived text: 7a15234778e8d81387ec755e2861aef7959341439a4a3594aebb082df4f79595
Stable URL of this page: https://metraipolis.com/documento/dc_96310ef377ca

How to cite

Decoding Extrahepatic Targeting of Lipid Nanoparticles with Interpretable Machine Learning — arXiv:2609.17721 — arXiv (en), https://arxiv.org/abs/2609.17721 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_96310ef377ca (Metraipolis archive dc_96310ef377ca, sha256 7a15234778e8d81387ec755e2861aef7959341439a4a3594aebb082df4f79595)

BibTeX

@misc{dc96310ef377ca,
  title = {Decoding Extrahepatic Targeting of Lipid Nanoparticles with Interpretable Machine Learning — arXiv:2609.17721},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.17721},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_96310ef377ca; sha256 7a15234778e8d81387ec755e2861aef7959341439a4a3594aebb082df4f79595},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.17721. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Lipid nanoparticles (LNPs) have transformed RNA medicine, yet their clinical utility remains constrained by predominant hepatic accumulation after systemic administration. Redirecting LNPs to extrahepatic tissues requires understanding of how lipid chemistry and formulation composition jointly govern in vivo biodistribution. Here, we develop an interpretable machine learning framework to predict hepatic versus extrahepatic LNP accumulation and identify molecular design rules for extrahepatic RNA delivery. A literature-derived dataset of 476 intravenous LNP formulations was curated from 81 studies, integrating formulation composition, lipid chemical structures, and IVIS-based biodistribution profiles. Standardized SMILES representations of ionizable lipids, helper lipids, sterols, PEGylated or polymer-conjugated lipids, additional lipids, and polymer repeat units were converted into RDKit Expert descriptors and combined with formulation-level variables to generate an 808-dimensional feature representation. Logistic regression, random forest, and XGBoost achieved ROC-AUC values of 0.839, 0.866, and 0.874, respectively. SHAP-based interpretation and consensus feature ranking revealed that ionizable-lipid descriptors dominate biodistribution prediction, while formulation composition, particularly ionizable lipid, sterol, and PEGylated/polymer-conjugated lipid fractions, contributes substantially. The top 20 consensus features retained nearly all predictive information in tree-based models. The most informative features implicated electrotopological surface properties, charge- and hydrophobicity-weighted surface areas, molecular topology, and amide/alkyl structural motifs as drivers of extrahepatic accumulation. This study establishes an interpretable, data-driven strategy for decoding LNP biodistribution and provides actionable design principles for engineering LNPs beyond the liver.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-17 in cs.LG).
Categorie arXiv: Quantitative Methods (q-bio.QM) ; Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.17721 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-15T14:08:19.201Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-17?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).