The Medical Matchmaking Machine

What It Covers

Doctor David Fagenbaum survives five near-death relapses from rare Castleman disease by discovering rapamycin through self-experimentation, then builds AI system scoring 75 million drug-disease combinations to identify repurposing opportunities for all 18,000 known diseases.

Key Questions Answered

• •Drug Repurposing Economics: Twenty to thirty percent of all prescriptions written in the US are off-label because FDA approval locks drugs to specific diseases at fixed prices. Companies maximize profit on first approval, creating no financial incentive to study additional uses for generic medications.
• •AI-Driven Medical Discovery: Machine learning algorithms trained on thousands of known drug-disease relationships generate 75 million probability scores (0.01 to 0.99) ranking every FDA-approved drug against every disease. This identifies repurposing candidates humans cannot systematically evaluate, like sirolimus for Castleman disease or pembrolizumab for angiosarcoma.
• •Biomedical Knowledge Graphs: Mapping every gene, protein, pathway, and disease as connected nodes reveals treatment opportunities through shared biological mechanisms. Fagenbaum identified mTOR activation in his blood samples, traced connections to sirolimus (rapamycin), and achieved eleven-year remission after five near-fatal relapses.
• •COVID Drug Success Model: Dexamethasone and tocilizumab, both repurposed drugs, saved millions of lives during the pandemic. Dexamethasone was initially recommended against for COVID but reduced mortality by thirty-five percent when tested, demonstrating how systematic repurposing evaluation uncovers life-saving treatments faster than new drug development.
• •Public Algorithm Release Strategy: Every Cure will release their Matrix algorithm publicly within nine months, allowing researchers and physicians to access the same drug-disease probability scores the medical team uses. The tool generates research hypotheses requiring laboratory validation and clinical trials, not direct treatment recommendations for patients.