Unknown

Dataset Information

0

Lipid nanoparticle-mediated siRNA delivery for safe targeting of human CML in vivo.


ABSTRACT: Efficient and safe delivery of siRNA in vivo is the biggest roadblock to clinical translation of RNA interference (RNAi)-based therapeutics. To date, lipid nanoparticles (LNPs) have shown efficient delivery of siRNA to the liver; however, delivery to other organs, especially hematopoietic tissues still remains a challenge. We developed DLin-MC3-DMA lipid-based LNP-siRNA formulations for systemic delivery against a driver oncogene to target human chronic myeloid leukemia (CML) cells in vivo. A microfluidic mixing technology was used to obtain reproducible ionizable cationic LNPs loaded with siRNA molecules targeting the BCR-ABL fusion oncogene found in CML. We show a highly efficient and non-toxic delivery of siRNA in vitro and in vivo with nearly 100% uptake of LNP-siRNA formulations in bone marrow of a leukemic model. By targeting the BCR-ABL fusion oncogene, we show a reduction of leukemic burden in our myeloid leukemia mouse model and demonstrate reduced disease burden in mice treated with LNP-BCR-ABL siRNA as compared with LNP-CTRL siRNA. Our study provides proof-of-principle that fusion oncogene specific RNAi therapeutics can be exploited against leukemic cells and promise novel treatment options for leukemia patients.

SUBMITTER: Jyotsana N 

PROVIDER: S-EPMC7116733 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

altmetric image

Publications


Efficient and safe delivery of siRNA in vivo is the biggest roadblock to clinical translation of RNA interference (RNAi)-based therapeutics. To date, lipid nanoparticles (LNPs) have shown efficient delivery of siRNA to the liver; however, delivery to other organs, especially hematopoietic tissues still remains a challenge. We developed DLin-MC3-DMA lipid-based LNP-siRNA formulations for systemic delivery against a driver oncogene to target human chronic myeloid leukemia (CML) cells in vivo. A mi  ...[more]

Similar Datasets

| S-EPMC5415968 | biostudies-literature
| S-EPMC3578952 | biostudies-literature
| S-EPMC5022131 | biostudies-literature
| S-EPMC5522105 | biostudies-literature
| S-EPMC5072529 | biostudies-literature
| S-EPMC2833237 | biostudies-literature
| S-EPMC7321291 | biostudies-literature
| S-EPMC3898629 | biostudies-literature
| S-EPMC5124971 | biostudies-literature
| S-EPMC5479285 | biostudies-literature