推进细胞特异性递送：机器学习洞察脂质纳米颗粒设计和细胞趋向性（Adv. Healthcare Mater. 18/2025）

IF 10 2区医学 Q1 ENGINEERING, BIOMEDICAL

Advanced Healthcare Materials Pub Date : 2025-07-16 DOI:10.1002/adhm.202570107

Belal I. Hanafy, Michael J. Munson, Ramesh Soundararajan, Sara Pereira, Audrey Gallud, Sajib Md Sanaullah, Gianluca Carlesso, Mariarosa Mazza

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引用次数: 0

摘要

在文章2500383中，Belal I. Hanafy、Mariarosa Mazza及其同事强调了一种数据驱动的策略，该策略集成了实验设计（DoE）、高通量筛选和机器学习，以系统地优化脂质纳米颗粒（LNP）配方，用于选择性细胞趋向性。通过在各种细胞模型中评估180种不同脂质化学和摩尔比的LNP，我们揭示了增强免疫细胞递送的配方见解，同时最大限度地减少肝脏摄取，从而为下一代疗法设计靶向LNP

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Advancing Cellular-Specific Delivery: Machine Learning Insights into Lipid Nanoparticles Design and Cellular Tropism (Adv. Healthcare Mater. 18/2025)

查看原文本刊更多论文

Advancing Cellular-Specific Delivery: Machine Learning Insights into Lipid Nanoparticles Design and Cellular Tropism (Adv. Healthcare Mater. 18/2025)

Lipid Nanoparticles

In article 2500383, Belal I. Hanafy, Mariarosa Mazza, and co-workers highlight a data-driven strategy integrating Design of Experiments (DoE), High-Throughput Screening, and Machine Learning to systematically optimize lipid nanoparticle (LNP) formulations for selective cellular tropism. By evaluating 180 LNPs across diverse lipid chemistries and molar ratios in various cell models, we reveal formulations insights that enhance immune cell delivery while minimizing liver uptake—enabling targeted LNP design for next-generation therapeutics

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来源期刊

Advanced Healthcare Materials 工程技术-生物材料

CiteScore

14.40

自引率

3.00%

发文量

600

审稿时长

1.8 months

期刊介绍： Advanced Healthcare Materials, a distinguished member of the esteemed Advanced portfolio, has been dedicated to disseminating cutting-edge research on materials, devices, and technologies for enhancing human well-being for over ten years. As a comprehensive journal, it encompasses a wide range of disciplines such as biomaterials, biointerfaces, nanomedicine and nanotechnology, tissue engineering, and regenerative medicine.