Building a Predictive Systems Architecture Linking RNAi Knockdown to Bradykinin-Driven Angioedema in HAE

Alnylam Pharmaceuticals
Alnylam is a pioneering company in RNAi therapeutics, focusing on silencing genes to treat severe genetic diseases. Their expertise lies in translating siRNA-driven gene silencing into clinically impactful therapies. In addition to their pioneering work in hereditary angioedema (HAE), Alnylam's portfolio spans a range of genetic conditions, offering novel treatment approaches for diseases with high unmet needs.

CytoSolve®
CytoSolve® is a computational biology company that specializes in building mechanistic in silico models. These models allow for the simulation and prediction of complex biological pathways, offering insights that guide drug development. Their platform integrates multiple biological data sources to provide a system-level understanding of pathways and molecular interactions, which can be used to optimize therapeutic strategies.

Challenge

Hereditary Angioedema (HAE) is a rare and debilitating genetic disorder characterized by recurrent episodes of non-itchy swelling. The condition is typically driven by defects in either factor XII or C1 inhibitor (C1INH), leading to dysregulated bradykinin production. Elevated bradykinin levels increase vascular permeability, resulting in the swelling characteristic of HAE attacks.

While RNAi-based therapies hold promise in silencing genes responsible for HAE-related biology, the development process is hampered by the lack of a comprehensive, mechanistic understanding of the contact activation and bradykinin pathways. These fragmented insights made it difficult to predict how siRNA knockdown of certain genes would impact bradykinin production and, by extension, HAE symptoms.

To address this, Alnylam sought an approach to:

  • Better understand the relationship between RNAi knockdown and bradykinin response
  • Develop a pathway-based strategy for target selection
  • Optimize the use of siRNA combinations to improve therapeutic outcomes

How CytoSolve Helped

CytoSolve®'s advanced computational platform was instrumental in solving these challenges by developing a systems-level model of the contact activation and bradykinin production pathways. Here's how CytoSolve® contributed:

  • Pathway Modeling and Simulation: CytoSolve® built an in silico model representing complex interactions within contact activation and bradykinin production pathways, enabling quantitative simulation of pathway-wide responses to perturbations such as siRNA knockdowns rather than isolated biomarker effects.
  • Pathway Sensitivity Analysis: Differential sensitivity analysis identified the most influential molecular nodes governing bradykinin production, supporting prioritized therapeutic target selection.
  • Cross-Validation with Existing Literature: Model outputs were validated against published in vitro and in vivo data to ensure predictions were anchored in experimental evidence and biologic plausibility.
  • Prediction of Alnylam’s In Vivo Results: Computational predictions aligned with Alnylam’s in vivo findings, strengthening confidence in the model’s predictive reliability for future experimental design.
  • Foundation for Combination Strategies: The CytoSolve® platform supported rational design of multi-target siRNA combinations, enabling efficient exploration of combination strategies beyond traditional trial-and-error methods.

Key Benefits Realized

  • Mechanistic Link Between Knockdown and Response: The in silico model established a quantitative connection between siRNA target knockdown and bradykinin production, directly linking genetic suppression to therapeutic response in HAE.
  • Pathway Sensitivity and Leverage-Point Identification: Sensitivity analysis identified the most influential pathway nodes, enabling prioritization of gene targets for siRNA knockdown to maximize therapeutic impact.
  • Cross-Validation Using Independent Evidence: Validation against independent data sources improved model robustness and reduced reliance on any single experimental dataset.
  • Predictive Utility for Preclinical Decision-Making: Model forecasts aligned with in vivo experimental outcomes, supporting more confident preclinical decision-making and study design.
  • Foundation for Combination siRNA Design: The validated framework enabled efficient exploration of multi-target siRNA strategies, reducing dependence on costly and time-intensive trial-and-error approaches.

Outcome

Through its collaboration with CytoSolve®, Alnylam successfully developed a predictive computational model that connected siRNA knockdown to bradykinin-driven outcomes in HAE. This validated, systems-level model provided a foundation for informed target selection, experimental design, and combination siRNA therapy development.

The project not only accelerated the pace of therapeutic discovery for HAE but also helped Alnylam refine its approach to RNAi therapeutics in complex diseases. With the insights gained from this collaboration, Alnylam is now better positioned to design more effective siRNA therapies for HAE and potentially extend these learnings to other genetic diseases driven by similar molecular pathways.