Antisense Oligonucleotide in LNA-Gapmer Design Targeting TGFBR2-A Key Single Gene Target for Safe and Effective Inhibition of TGFβ Signaling.
Sabrina KuespertRosmarie HeydnSebastian PetersEva WirkertAnne-Louise MeyerMareile SiebörgerSiw JohannesenLudwig AignerUlrich BogdahnTim-Henrik BruunPublished in: International journal of molecular sciences (2020)
Antisense Oligonucleotides (ASOs) are an emerging drug class in gene modification. In our study we developed a safe, stable, and effective ASO drug candidate in locked nucleic acid (LNA)-gapmer design, targeting TGFβ receptor II (TGFBR2) mRNA. Discovery was performed as a process using state-of-the-art library development and screening. We intended to identify a drug candidate optimized for clinical development, therefore human specificity and gymnotic delivery were favored by design. A staggered process was implemented spanning in-silico-design, in-vitro transfection, and in-vitro gymnotic delivery of small batch syntheses. Primary in-vitro and in-vivo toxicity studies and modification of pre-lead candidates were also part of this selection process. The resulting lead compound NVP-13 unites human specificity and highest efficacy with lowest toxicity. We particularly focused at attenuation of TGFβ signaling, addressing both safety and efficacy. Hence, developing a treatment to potentially recondition numerous pathological processes mediated by elevated TGFβ signaling, we have chosen to create our data in human lung cell lines and human neuronal stem cell lines, each representative for prospective drug developments in pulmonary fibrosis and neurodegeneration. We show that TGFBR2 mRNA as a single gene target for NVP-13 responds well, and that it bears great potential to be safe and efficient in TGFβ signaling related disorders.
Keyphrases
- nucleic acid
- transforming growth factor
- endothelial cells
- induced pluripotent stem cells
- genome wide
- copy number
- pluripotent stem cells
- oxidative stress
- emergency department
- small molecule
- pulmonary fibrosis
- binding protein
- drug induced
- gene expression
- cancer therapy
- genome wide identification
- molecular docking
- signaling pathway
- machine learning
- deep learning
- electronic health record
- transcription factor
- data analysis
- molecular dynamics simulations