Alnylam and CytoSolve Build a Predictive Systems Architecture Linking RNAi Knockdown to Bradykinin-Driven Angioedema in HAE

Alnylam is a leading pharmaceutical company advancing RNAi therapeutics, including programs targeting hereditary angioedema (HAE). The organization specializes in translating siRNA-driven gene silencing into clinically meaningful therapies for serious diseases with high unmet need.

Challenge

Hereditary angioedema (HAE) is a chronic, recurring condition characterized by episodes of non-itchy swelling in subcutaneous or submucosal tissues. In HAE, functional defects involving factor XII and/or C1 inhibitor (C1INH) can drive increased bradykinin production, leading to vascular permeability and swelling attacks.

While siRNA therapeutics offer a compelling strategy to suppress genes implicated in HAE biology, efficient development is constrained by limited mechanistic correlation across biomolecules within the contact activation pathway. This fragmentation makes it difficult to reliably connect target knockdown to downstream bradykinin response, slowing optimization of targets, dosing strategies, and rational combinations.

How CytoSolve Helped

Alnylam collaborated with CytoSolve to systematically explore the relationship between gene knockdown and pathway-level response in HAE-relevant biology. CytoSolve’s platform:

  • Converted contact activation and bradykinin production pathways into mechanistic in silico models, enabling quantitative simulation of pathway dynamics rather than isolated biomarker interpretation.
  • Evaluated differential sensitivity of bradykinin production to perturbations across the modeled network, clarifying which nodes most strongly influence clinically relevant readouts.
  • Validated simulation outputs against in vivo and in vitro experimental findings from the literature, establishing credibility for model behavior across multiple evidence sources.
  • Predicted outcomes observed in Alnylam’s in vivo studies, linking computational hypotheses to real-world experimental results and strengthening confidence in forward-looking predictions.

Key Benefits Realized

  • Mechanistic Link Between Knockdown and Response
    Established a systems-level, quantitative bridge connecting siRNA target suppression to bradykinin-driven outcomes.
  • Pathway Sensitivity and Leverage-Point Identification
    Determined which pathway components most strongly modulate bradykinin production, supporting prioritization of targets.
  • Cross-Validation Using Independent Evidence
    Grounded model behavior using published in vitro and in vivo results, improving robustness beyond single-study fitting.
  • Predictive Utility for Preclinical Decision-Making
    Demonstrated the ability to forecast results aligned with Alnylam’s in vivo observations, enabling more confident experimental planning.
  • Foundation for Combination siRNA Design
    Provided a validated initiative framework to examine multi-target siRNA combinations rationally, rather than by trial-and-error.

Outcome

Through collaboration with CytoSolve, Alnylam translated complex contact activation and bradykinin biology into a validated computational systems architecture capable of connecting siRNA target knockdown to downstream pathway response. The mechanistic in silico model, supported by literature-based validation and demonstrated alignment with Alnylam’s in vivo findings, established a practical foundation for accelerating target selection and exploring combination siRNA strategies aimed at improving therapeutic control of life-threatening HAE.