Identification of Potent siRNA Delivery Peptides Using Computer Modeling

Area of Science:

• Biotechnology
• Molecular Biology
• Drug Delivery Systems

Background:

• Peptides are promising siRNA delivery vectors due to their aggregation and electrical properties.
• Identifying peptides with optimal delivery and safety profiles is challenging due to sequence variability.

Purpose of the Study:

• To develop a computational and experimental program for identifying ideal siRNA delivery peptides.
• To evaluate the in vivo safety and efficacy of candidate peptides.

Main Methods:

• A holistic program combining computer modeling and single-cell RNA sequencing (scRNA-seq) was employed.
• Sequential screening identified peptides with ideal assembly and delivery abilities.
• Cell subtype-level analysis assessed in vivo tissue safety.

Main Results:

• A 12-amino acid peptide, after hydrophobic modification, exhibited strong lung-targeted siRNA delivery.
• Systemic administration showed minimal damage to liver and lung tissues and preserved immune cell populations.
• STAT3 siRNA loading resulted in significant anticancer effects in patient-derived xenograft models.

Conclusions:

• The developed screening procedure effectively identifies safe and effective peptide-based siRNA delivery vectors.
• This approach facilitates the advancement of peptide-based RNA interference (RNAi) therapeutics.
• The leading peptide candidate shows potential for targeted cancer therapy with reduced toxicity.