siRNA Off-Target Prediction
Poster Authors:
Jialin Zhang, Wanyu Tao, Kaiqiang Hu, Shijiao Ma, Donghua Chai, Yahui Kou, Xin Li, Xiaomin Du, Zhaozhao Wu, Fang He
Pharmaron Beijing Co., Ltd. No.6, Taihe Road, BDA Beijing, China
Computational Toxicology Predictions to Support the Discovery of Safer siRNA Therapeutics
SiRNA off-target effects remain a major challenge in developing oligonucleotide therapeutics. Seed-mediated gene silencing and unintended immune activation must be considered for siRNA discovery and candidate selection. Our computational toxicology team helps our clients to design siRNA, guide siRNA sequence optimization and provide mechanistic insight into potential siRNA safety liabilities.
In this poster, we present a case study of computational toxicology for INHBE-siRNA design, siRNA immunogenicity prediction and in silico siRNA off-target screening, with validation in primary human hepatocytes and immune cells.
Pharmaron Services Demonstrated in this Poster
- Predictive in silico RNA design, using empirical design rules, immune‑risk filtering, seed‑region Tm scoring, and genome‑wide off‑target assessment.
- Functional validation in hepatocytes and immune cells.
- Validation of siRNA off-target predictions with RNA‑seq differential‑expression analysis.
Capabilities
Key Findings
- Validation of siRNA Potency Prediction: Strong correlation between predicted thermal stability scores and measured target knockdown.
- siRNA Off-Target Prediction: Strong correlation between siRNA off‑target predictions and validated gene expression changes.
- Efficacy: The lead siRNA showed potent knockdown in hepatocytes.
- siRNA Safety Assessment: The immunogenicity signature helped identify sequences requiring further chemical refinement, contributing to a refined siRNA safety profile.
This poster was presented at the SOT Society of Toxicology 2026 conference.
Download this poster to read the full story and see how we apply our tools to enable efficient siRNA therapeutics discovery.