Moving forward through the in silico modeling of multiple sclerosis: Treatment layer implementation and validation

Area of Science:

• Computational immunology
• Neuroimmunology
• Digital health

Background:

• Multiple sclerosis (MS) is a chronic autoimmune disease impacting the central nervous system.
• Relapse-reducing therapies are crucial for managing the most common relapsing-remitting MS.
• Existing models lack comprehensive predictive capabilities for novel MS treatments.

Purpose of the Study:

• To extend the Universal Immune System Simulator-Multiple Sclerosis (UISS-MS) model with new treatments.
• To validate the UISS-MS model's predictive accuracy for cladribine and ocrelizumab in MS.
• To establish UISS-MS as a digital twin for personalized MS treatment prediction.

Main Methods:

• Agent-based modeling (ABM) to simulate immune system dynamics in MS.
• Incorporation of cladribine and ocrelizumab into the UISS-MS model.
• Retrospective validation using clinical and MRI data from MS patient trials.

Main Results:

• UISS-MS accurately simulated the mechanisms of action and clinical outcomes of cladribine and ocrelizumab.
• Model predictions closely mirrored results from clinical trials for both drugs.
• The simulation demonstrated effective relapse reduction consistent with clinical observations.

Conclusions:

• The UISS-MS model serves as a validated digital twin for multiple sclerosis.
• It can predict the effects of existing and novel MS therapies.
• UISS-MS can support clinical decision-making and personalize patient treatment strategies.