Juhani Lahdenperä

Chief Operating Officer RNatives

Seminars

Thursday 29th January 2026
Panel Discussion: Looking into the Future of RNAi & Precision Medicine Through Synergizing AI, Combination Therapies & Multi-Target Silencing for Next-Generation Treatments
3:30 pm

The future of RNAi lies at the intersection of precision medicine, AI-driven innovation, and strategic combination approaches. This panel explores how integrating cutting-edge technologies like AI target discovery with synergistic modalities can unlock new therapeutic potential. Experts will discuss multi-target silencing strategies, new target molecule classes, and how to future-proof RNAi pipelines to lead the next wave of transformative treatments. Join us to gain actionable insights on positioning your RNAi therapeutics at the forefront of precision medicine by discussing:

  • Exploring AI-driven target discovery and combination therapies with CAR-T or radiotherapy to accelerate identification of disease-specific genes with high clinical relevance to enable faster precision RNAi therapy development
  • Predicting future combination therapy strategies to stay ahead of emerging trends in multi-target silencing, for the expansion of treatment options for complex diseases
  • Strategizing how to position pipelines at the forefront of next-gen RNAi innovation

Tuesday 27th January 2026
Maximizing Novel In Silico Strategies to Harness Data-driven Approaches for the Development & Delivery of RNAi-based Therapeutics

The rapid evolution of in silico tools is revolutionizing RNAi therapeutic development, from target discovery to regulatory approval. Attendees will learn how to leverage these tools to expand RNAi applications beyond

current frontiers, optimize drug stability, and navigate regulatory hurdles. A must for teams aiming to harness data-driven approaches for faster, smarter RNAi therapeutic design

  • Deploying AI-powered genomic and transcriptomic analysis to uncover high-confidence RNAi targets, and directed evolution-based discovery of novel protein-to-protein interactions governing tissue-selective, extrahepatic delivery of siRNA.
  • Applying machine learning to predict off-target effects and potency, streamlining lead candidate selection, and reducing late-stage attrition in preclinical development
  • Utilizing computational modelling to connect sequence and structure with functional data layers, such as predicting ligands for orphan receptors, binding affinity, internalization dynamics and stability, to accelerate discovery and translation of early leads.
Juhani lup