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

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dc.contributor.authorAbostait, Amr
dc.contributor.examiningcommitteeLin, Francis (Physics and Astronomy)
dc.contributor.examiningcommitteeLakowski, Ted (College of Pharmacy)
dc.contributor.supervisorLabouta, Hagar
dc.contributor.supervisorKeijzer, Richard
dc.date.accessioned2024-08-29T20:16:40Z
dc.date.available2024-08-29T20:16:40Z
dc.date.issued2024-08-26
dc.date.submitted2024-08-25T23:22:51Zen_US
dc.date.submitted2024-08-29T19:40:22Zen_US
dc.degree.disciplinePharmacy
dc.degree.levelMaster of Science (M.Sc.)
dc.description.abstractThere is a clinical need to develop lipid nanoparticles (LNPs) to deliver prenatal therapies to the developing 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, and transfection efficiency in fetal cells. We developed and characterized a library of LNPs of varying compositions and used machine learning (ML) models to delineate the determinants of LNPs size and zeta potential. Utilizing different in vitro placental models with the help of the Random Forest algorithm, we studied the features driving percentage LNPs transport and kinetics, using a dataset of 18 input features encompassing 48 different transport experiments. We further evaluated the LNPs for safety and transfection efficiency in placental trophoblasts and fetal lung fibroblasts. Finally, we assessed toxicity of the LNPs in a tracheal occlusion fetal lung explant ex-vivo 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 as one of the top driving features. Building on the ML model results, we developed new LNPs formulations to further optimize the transport leading to a 622% increase in transport. To induce preferential siRNA transfection of fetal lungs, 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 and ensured the integrity of the LNPs following transplacental transport. Finally, the optimized formulations appeared to be safe in ex vivo fetal lungs as indicated by insignificant changes in apoptosis (Caspase-3) and proliferation (Ki67) markers. We have optimized an LNPs 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.
dc.description.noteOctober 2024
dc.identifier.urihttp://hdl.handle.net/1993/38459
dc.language.isoeng
dc.rightsopen accessen_US
dc.subjectLipid nanoparticles
dc.subjectFetal Gene Delivery
dc.subjectsiRNA
dc.subjectPlacenta
dc.subjectTransplacental Transport
dc.subjectmachine learning
dc.titleOptimizing lipid nanoparticles for fetal gene delivery in vitro, ex vivo, and aided by machine learning
dc.typemaster thesisen_US
local.subject.manitobano
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