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在体外、离体条件下以及借助机器学习优化用于胎儿基因递送的脂质纳米颗粒。

Optimizing lipid nanoparticles for fetal gene delivery in vitro, ex vivo, and aided with machine learning.

作者信息

Abostait Amr, Abdelkarim Mahmoud, Bao Zeqing, Miyake Yuichiro, Tse Wai Hei, Di Ciano-Oliveir Caterina, Buerki-Thurnherr Tina, Allen Christine, Keijzer Richard, Labouta Hagar I

机构信息

Keenan Research Centre for Biomedical Science, Unity Health Toronto, Toronto M5B 1T8, Canada; College of Pharmacy, University of Manitoba, Winnipeg R3E 0T5, Canada.

Keenan Research Centre for Biomedical Science, Unity Health Toronto, Toronto M5B 1T8, Canada; Biomedical Engineering, Faculty of Engineering, University of Toronto, Toronto M5S 3G9, Canada.

出版信息

J Control Release. 2024 Dec;376:678-700. doi: 10.1016/j.jconrel.2024.10.047. Epub 2024 Oct 28.

Abstract

There is a clinical need to develop lipid nanoparticles (LNPs) to deliver congenital therapies to the fetus during pregnancy. The aim of these therapies is to restore normal fetal development and prevent irreversible conditions after birth. As a first step, LNPs need to be optimized for transplacental transport, safety on the placental barrier and fetal organs and transfection efficiency. We developed and characterized a library of LNPs of varying compositions and used machine learning (ML) models to delineate the determinants of LNP size and zeta potential. Utilizing different in vitro placental models with the help of a Random Forest algorithm, we could identify the top features driving percentage LNP transport and kinetics at 24 h, out of a total of 18 input features represented by 41 LNP formulations and 48 different transport experiments. We further evaluated the LNPs for safety, placental cell uptake, transfection efficiency in placental trophoblasts and fetal lung fibroblasts. To ensure the integrity of the LNPs following transplacental transport, we screened LNPs for transport and transfection using a high-throughput integrated transport-transfection in vitro model. Finally, we assessed toxicity of the LNPs in a tracheal occlusion fetal lung explant model. LNPs showed little to no toxicity to fetal and placental cells. Immunoglobin G (IgG) orientation on the surface of LNPs, PEGylated lipids, and ionizable lipids had significant effects on placental transport. The Random Forest algorithm identified the top features driving LNPs placental transport percentage and kinetics. Zeta potential emerged in the top driving features. Building on the ML model results, we developed new LNP formulations to further optimize the transport leading to 622 % increase in transport at 24 h versus control LNP formulation. To induce preferential siRNA transfection of fetal lung, we further optimized cationic lipid percentage and the lipid-to-siRNA ratio. Studying LNPs in an integrated placental and fetal lung fibroblasts model showed a strong correlation between zeta potential and fetal lung transfection. Finally, we assessed the toxicity of LNPs in a tracheal occlusion lung explant model. The optimized formulations appeared to be safe on ex vivo fetal lungs as indicated by insignificant changes in apoptosis (Caspase-3) and proliferation (Ki67) markers. In conclusion, we have optimized an LNP formulation that is safe, with high transplacental transport and preferential transfection in fetal lung cells. Our research findings represent an important step toward establishing the safety and effectiveness of LNPs for gene delivery to the fetal organs.

摘要

临床上需要开发脂质纳米颗粒(LNP),以便在孕期将先天性疗法递送至胎儿体内。这些疗法的目的是恢复胎儿的正常发育,并预防出生后出现不可逆的病症。作为第一步,LNP需要在经胎盘转运、对胎盘屏障及胎儿器官的安全性以及转染效率方面进行优化。我们开发并表征了一系列不同组成的LNP库,并使用机器学习(ML)模型来确定LNP大小及zeta电位的决定因素。借助随机森林算法,利用不同的体外胎盘模型,在由41种LNP制剂和48个不同转运实验所代表的总共18个输入特征中,我们能够识别出在24小时时驱动LNP转运百分比及动力学的主要特征。我们进一步评估了LNP的安全性、胎盘细胞摄取、在胎盘滋养层细胞和胎儿肺成纤维细胞中的转染效率。为确保经胎盘转运后LNP的完整性,我们使用高通量体外整合转运-转染模型筛选LNP的转运和转染情况。最后,我们在气管闭塞胎儿肺外植体模型中评估了LNP的毒性。LNP对胎儿和胎盘细胞几乎没有毒性。LNP表面的免疫球蛋白G(IgG)取向、聚乙二醇化脂质和可电离脂质对胎盘转运有显著影响。随机森林算法识别出驱动LNP胎盘转运百分比及动力学的主要特征。zeta电位出现在主要驱动特征之中。基于ML模型的结果,我们开发了新的LNP制剂,以进一步优化转运,与对照LNP制剂相比,在24小时时转运增加了622%。为诱导胎儿肺的优先siRNA转染,我们进一步优化了阳离子脂质百分比和脂质与siRNA的比例。在整合的胎盘和胎儿肺成纤维细胞模型中研究LNP表明,zeta电位与胎儿肺转染之间存在很强的相关性。最后,我们在气管闭塞肺外植体模型中评估了LNP的毒性。如凋亡(Caspase-3)和增殖(Ki67)标志物的变化不显著所示,优化后的制剂在离体胎儿肺上似乎是安全的。总之,我们优化了一种安全的LNP制剂,其具有高经胎盘转运能力,并能在胎儿肺细胞中优先转染。我们的研究结果是朝着确立LNP用于向胎儿器官进行基因递送的安全性和有效性迈出的重要一步。

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