AI-Driven Prediction of Viral Payload Efficacy for Personalized Cancer Immunotherapy

Purpose and goal

Develop AI-powered predictive models that could eventually match cancer patients to optimal viral immunotherapy treatments. Working with preclinical models, we analyze how tumor immune environments respond differently to viral vector therapies. Current approaches rely on trial-and-error because we lack understanding of how treatments work on patients. By identifying patterns that predict therapeutic response, we establish scientific foundations for future personalized treatment selection.

Expected effects and result

Comprehensive preclinical data revealing how viral vector immunotherapies activate tumor immune environments. Validated AI models identifying how pre-existing immune landscape characteristics influence therapeutic response. New understanding of inter-patient variability mechanisms affecting treatment outcomes.

Planned approach and implementation

First, develop predictive AI models using existing experimental data from VLP therapeutic´s (USA) viral immunotherapy studies with advanced computational analysis. Second, generate new experimental data across different therapeutic payloads and cancer models to validate and refine predictions. Third, develop strategic plan for clinical translation based on preclinical findings.