发表机构
University of Southern California; University of Wisconsin-Madison; East Tennessee State University（南加利福尼亚大学; 威斯康星大学麦迪逊分校; 东田纳西州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究用可解释机器学习预测脂质纳米颗粒的肝外积聚，识别关键脂质设计规则，为肝外RNA递送提供指导。
AI 中文摘要
脂质纳米颗粒（LNPs）已彻底改变了RNA医学，但其临床应用仍受限于全身给药后主要积聚于肝脏。将LNPs重定向至肝外组织需要理解脂质化学与制剂组成如何共同调控体内生物分布。在此，我们开发了一个可解释的机器学习框架，用于预测肝脏与肝外LNP积聚，并识别肝外RNA递送的分子设计规则。我们从81项研究中整理了一个包含476种静脉注射LNP制剂的文献来源数据集，整合了制剂组成、脂质化学结构以及基于IVIS的生物分布谱。将可电离脂质、辅助脂质、甾醇、PEG化或聚合物偶联脂质、额外脂质及聚合物重复单元的标准SMILES表示转换为RDKit Expert描述符，并与制剂水平变量结合，生成808维特征表示。逻辑回归、随机森林和XGBoost分别实现了0.839、0.866和0.874的ROC-AUC值。基于SHAP的解释和共识特征排序显示，可电离脂质描述符主导生物分布预测，而制剂组成，特别是可电离脂质、甾醇和PEG化/聚合物偶联脂质比例，贡献显著。前20个共识特征在基于树的模型中保留了几乎所有预测信息。信息量最大的特征涉及电拓扑表面性质、电荷和疏水性加权表面积、分子拓扑以及酰胺/烷基结构基序，作为肝外积聚的驱动因素。本研究建立了一种可解释、数据驱动的策略来解码LNP生物分布，并为工程化超越肝脏的LNP提供了可操作的设计原则。
英文摘要
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.
