CytoSolve® In Silico Combination Modeling for Pancreatic Adenocarcinoma Drug Development

CytoSolve® develops and deploys computational systems biology technologies that enable quantitative modeling of complex diseases and therapeutic interventions. Its platform is designed to meet government and regulatory expectations by providing transparent, literature-grounded, and reproducible mechanistic evidence. In oncology, CytoSolve® supports early-stage decision-making and regulatory submissions by accelerating discovery of efficacious drug combinations while minimizing experimental burden.

Challenge

Pancreatic adenocarcinoma is an aggressive malignancy characterized by late diagnosis, rapid progression, and poor prognosis. Key challenges for government and regulatory stakeholders include:

  • Limited efficacy of monotherapy: Single FDA-approved drugs (e.g., gemcitabine) require high doses to achieve modest benefit, often with significant toxici
  • Complex disease biology: Tumor growth and resistance are driven by intertwined signaling pathways regulating proliferation and apoptosis.
  • Protracted development timelines: Traditional identification of effective drug combinations can require 10–15 years of experimental research.
  • Regulatory demands: Government agencies require clear mechanistic rationale to justify advancing combination therapies into clinical trials.
A rigorous, predictive, and mechanistic approach was needed to identify promising combinations efficiently.

How CytoSolve Helped

CytoSolve® applied its government- and regulatory-grade modeling workflow to pancreatic cancer combination discovery:

Disease Systems Modeling

  • Constructed mathematical models representing EGFR-induced cell cycle progression and apoptotic signaling, capturing the core molecular framework of pancreatic adenocarcinoma.
  • Integrated these models within the CytoSolve® engine to simulate cancer cell proliferation and programmed cell death under therapeutic perturbations.
In Silico Combination Screening

  • Evaluated combinations of FDA-approved chemotherapeutic agents within the integrated disease model.
  • Quantified outcomes based on apoptotic cell counts and suppression of proliferative signaling, enabling objective comparison across combinations.
In Silico Combination Screening

  • Discovered Cyto-001, a novel two-drug combination predicted to:
    • Maximize apoptosis of pancreatic cancer cells
    • Minimize cancer cell proliferation
    • Achieve efficacy at lower effective doses, reducing toxicity risk
  • Generated reproducible, pathway-level mechanistic evidence suitable for regulatory review.

Key Benefits Realized

  • Accelerated Combination Discovery – Reduced years of empirical screening through predictive in silico modeling
  • Mechanistic Enablement – Clear explanation of how drug combinations modulate EGFR, cell cycle, and apoptosis pathways
  • Regulatory Readiness – Quantitative, reproducible evidence aligned with government and FDA expectations
  • Toxicity Reduction Strategy – Identification of lower-dose, higher-efficacy combinations
  • Clinical Translation Support – Enabled advancement of Cyto-001 into FDA-approved clinical trials

Outcome

The CytoSolve® modeling effort resulted in the identification of Cyto-001, a novel combination therapy for pancreatic adenocarcinoma supported by robust in silico mechanistic evidence. The FDA’s approval of Cyto-001 for further clinical trials underscores the value of computational systems biology in government-regulated oncology research. This case study demonstrates how CytoSolve® can dramatically shorten development timelines, strengthen regulatory submissions, and enable rational advancement of combination cancer therapies grounded in molecular systems understanding.