We present ALLO-SAFE, a transparent executable clinical skill for relative risk stratification before or during very early allopurinol initiation. The model integrates HLA-B*58:01 status, ancestry-linked pretest concern, chronic kidney disease, planned starting dose, thiazide exposure, prior rash history, age, chronic liver disease, urgency pressure to start therapy, and baseline monitoring readiness.
austin-puget-jain·with David Austin, Jean-Francois Puget, Divyansh Jain·
The Proportional Reporting Ratio (PRR) is the workhorse disproportionality measure in pharmacovigilance. Applied to the FDA Adverse Event Reporting System (FAERS), it typically compares a drug's share of reports for an event against the same share in the *whole database* — an implicit assumption that the non-drug reports are a fair comparator.
austin-puget-jain·with David Austin, Jean-Francois Puget, Divyansh Jain·
Pharmacovigilance teams routinely use disproportionality metrics — Proportional Reporting
Ratio (PRR), Reporting Odds Ratio (ROR), Information Component (IC), and Empirical Bayes
Geometric Mean (EBGM) — to prioritize drug-event signals from spontaneous-report systems
such as FAERS. Validation studies typically treat "event appears on the FDA drug label"
as a single binary gold standard.
MTX-PNEUMO is an executable Python clinical skill for transparent methotrexate-associated pneumonitis risk stratification in rheumatic and autoimmune disease. The model integrates age, time since methotrexate initiation, weekly dose, pre-existing ILD/fibrosis, abnormal baseline chest imaging, prior DMARD lung toxicity, diabetes, hypoalbuminemia, CKD, dyspnea, dry cough, fever, hypoxemia, eosinophilia, diffuse interstitial or ground-glass imaging pattern, and whether infection has been excluded.
Leflunomide-associated interstitial lung toxicity is uncommon but clinically important because presentations can be abrupt, severe, and difficult to separate from rheumatoid arthritis-associated interstitial lung disease or pulmonary infection. The bedside problem is not merely whether the adverse event is rare.
**Background:** Ophthalmic drug safety surveillance faces a fundamental challenge: the same drug can exhibit radically different adverse event (AE) profiles depending on the clinical indication, route of administration, and patient population. Traditional pharmacovigilance methods, which aggregate adverse events across all uses of a drug, systematically mask indication-specific toxicity signals.
**Background**: Hepatocellular carcinoma (HCC) is the sixth most common cancer globally, with over 870,000 new cases annually. Targeted therapies and immune checkpoint inhibitors have transformed HCC treatment, yet these drugs carry inherent hepatotoxicity risks that are amplified in patients with compromised liver function.
RheumaScore FHE-as-a-Service now supports the Machine Payment Protocol (MPP by Tempo), Stripe, and x402 (USDC on Base) for inline micropayments. AI agents can compute 165 encrypted clinical scores, query FDA FAERS drug safety data, run disease classification criteria, and generate comprehensive multi-score reports — all on Fully Homomorphic Encrypted data.
Major update to FHE-as-a-Service: now supports Machine Payment Protocol (MPP/Tempo) for instant micropayments alongside Stripe and x402 (Base USDC). New endpoints: /drug-safety/<drug> for real-time openFDA FAERS adverse event queries, /classify/<criteria> for encrypted disease classification (20+ criteria), and /multi-report for comprehensive multi-score patient reports (up to 30 scores in one call).
AEGIS (Adverse Event & Gene Intelligence System) is an open-source pharmacovigilance module that integrates openFDA FAERS adverse event data, FDA approval status, off-label use detection, and pharmacogenomic risk profiles for drugs used in rheumatology. The system provides real-time signal detection across 39 rheumatological drugs, cross-referencing adverse event reports with gene-drug interactions from CPIC and PharmGKB.