In Silico Systems Modeling of Interferon Biology Using CytoSolve® Architecture for Autoimmune Disease Research at Pfizer

Partner Description

Pfizer
Pfizer is a global biopharmaceutical leader advancing innovative therapies across immunology, inflammation, oncology, and rare diseases. Through its Centers for Therapeutic Innovation (CTI), Pfizer collaborates with advanced technology partners to accelerate translational research, target discovery, and therapeutic development.

Challenge

Interferons (IFNs) play a central and highly complex role in autoimmune diseases such as lupus and dermatomyositis. IFN signaling operates across multiple cell types—including hematopoietic stem cells and fibroblasts—and involves dense regulatory networks with feedback loops, cross-talk, and context-specific behavior.

Conventional experimental and computational approaches struggle to integrate the vast and heterogeneous interferon literature into a coherent, predictive framework. Monolithic models lack scalability and adaptability, while experimental approaches alone cannot efficiently explore system-wide behavior across disease contexts. Pfizer required a purely in silico, mechanistic modeling architecture capable of preserving biological detail, experimental provenance, and flexibility while supporting translational research objectives.

How CytoSolve® Helped

CytoSolve® partnered with Pfizer’s CTI to deploy its modular, ontology-driven in silico systems architecture as a foundational modeling framework for interferon biology.

CytoSolve® enabled independent interferon signaling submodels—derived from thousands of peer-reviewed studies—to be computationally integrated without collapsing them into a single monolithic structure. Each submodel retained its original biological assumptions, mathematical formulation, and experimental lineage, while remaining interoperable within the broader interferon regulatory network.

Using this architecture, CytoSolve® established and validated an in silico Interferon Regulatory Network by benchmarking model behavior against existing in vitro and in vivo wet-lab data. The system was then extended to cell-type–specific implementations, enabling computational simulation of interferon signaling dynamics in hematopoietic stem cells and fibroblasts. The partitioned design allowed each cellular context to be simulated independently while remaining dynamically linked within a unified systems framework.

This in silico modeling environment supported mechanistic interrogation of pathway perturbations, hypothesis testing, and exploration of disease-relevant signaling behavior at a scale and resolution not achievable through experimental methods alone.

Key Benefits Realized

  • Scalable in silico systems architecture for integrating complex interferon signaling pathways
  • Mechanistic validation of computational predictions against existing experimental data
  • Cell-type–specific interferon modeling within a unified computational framework
  • Reusable and extensible modeling infrastructure for autoimmune disease research
  • In silico foundation for evaluating therapeutic strategies and biomarker hypotheses

Outcome

The CytoSolve® in silico systems architecture provided Pfizer with a robust, extensible platform for mechanistic understanding of interferon-driven autoimmune pathology. By transforming fragmented biological knowledge into a coherent, modular computational model, the collaboration enabled scalable exploration of interferon biology across cell types and disease contexts. This case study demonstrates how in silico systems architecture can serve as a predictive engine for translational research, supporting target discovery, biomarker development, and therapeutic innovation in complex autoimmune diseases.