Preventing Chronic Disease: AI-assisted Degranulation Immunotherapy

Combinational immunotherapy represents an emerging therapeutic approach for chronic diseases, including cancer, autoimmune disorders, and neurodegenerative conditions. Controlled atopic responses are being investigated as mechanisms to direct immune cell activity to disease sites. However, combination strategies require careful balance between therapeutic efficacy and adverse event profiles. The objective is to assess whether artificial intelligence (AI) can accurately predict efficacy and safety of allergy-based degranulation immunotherapy combinations with Food and Drug Administration (FDA) approved monoclonal antibodies for chronic diseases, including cancer, multiple sclerosis, amyotrophic lateral sclerosis (ALS), and Alzheimer’s disease. A systematic evaluation was conducted using Gemini AI (version 2.5 Pro) to assess treatment combinations pairing FDA-approved monoclonal antibodies with allergy-based degranulation immunotherapy approaches. Four disease categories were examined: cancer, multiple sclerosis, ALS, and Alzheimer’s disease. The AI system evaluated complementary immunomodulation, predicted adverse events, and identified combinations with favorable benefit-risk profiles. AI identified several promising combination candidates with predicted favorable benefit–risk profiles. The system projected how treatments might augment each other’s effects and pinpointed specific side effect risks. This supports rational prioritization for laboratory testing and trial design. AI evaluation can identify promising allergy-based degranulation immunotherapy combinations and predict adverse event patterns; however, these predictions require rigorous experimental validation before clinical application. This computational framework may accelerate the development of early-stage immunomodulatory combinations by prioritizing combinations for experimental testing, potentially reducing research costs. However, AI-generated predictions should not inform clinical decision-making without rigorous laboratory and clinical validation.