OpenPGx – Claude에게 23andMe/Genera 원시 파일(MCP)의 약물에 대해 물어보세요.

OpenPGx is an open, AI-readable standard for pharmacogenomic data. It defines how genetic variants, drug responses, disease risks, and traits should be structured so that any AI system can understand and reason about them. The standard is delivered today through an MCP Server — plug it into Claude, Cursor, or any MCP-compatible AI and start asking questions about your DNA. "Can I take Ozempic?" → Checks GLP1R variants against your genotype "How about Vyvanse?" → Cross-references CYP2D6 + COMT studies "What's my Alzheimer risk?" → 19 disease conditions analyzed with odds ratios Privacy-first: your data never leaves your computer. No cloud, no account, no tracking. Pharmacogenomic data is trapped in formats that AI can't use. FHIR is verbose and hospital-centric. PharmCAT is great but not designed for AI consumption. Research papers are unstructured text. OpenPGx is different: - AI-readable — structured JSON that any LLM can parse and reason about - Study-driven — every interpretation traces back to a published study with PMID/DOI - Open source — add a study by dropping a JSON file and opening a PR - FHIR-compatible — not a replacement, but an intelligence layer that exports to FHIR resources One JSON file = one gene-drug study. This is the atomic unit of pharmacogenomic knowledge in OpenPGx: { "gene": "ALDH2", "category": "drug_metabolism", "gene_description": "Alcohol metabolism, nitroglycerin bioactivation", "drugs": ["nitroglycerin", "ethanol"], "source": { "pmid": "16395407", "doi": "10.1172/JCI26564", "source_type": "pubmed", "title": "ALDH2 Glu504Lys polymorphism and nitroglycerin efficacy", "journal": "Journal of Clinical Investigation", "year": 2006, "cohort_size": 986, "url": "https://pubmed.ncbi.nlm.nih.gov/16395407/", "finding": "ALDH2*2 carriers show reduced nitroglycerin bioactivation." }, "snps": [ { "rsid": "rs671", "risk_allele": "A", "reference_allele": "G", "interpretations": { "AA": { "phenotype": "ALDH2 Deficient", "effect": "Nitroglycerin ineffective", "severity": "severe" }, "AG": { "phenotype": "Reduced Activity", "effect": "33-40% less efficacy", "severity": "moderate" }, "GG": { "phenotype": "Normal", "effect": "Standard response", "severity": "info" } } } ], "evidence_level": "established" } Want to contribute a new drug-gene study? Create a file like this in data/pgx/studies/ and open a PR. No code changes needed. Add to your claude_desktop_config.json : macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json Linux: ~/.config/claude/claude_desktop_config.json { "mcpServers": { "openpgx": { "command": "npx", "args": ["-y", "openpgx"] } } } Restart Claude Desktop. Done. claude mcp add openpgx -- npx -y openpgx Open Settings > MCP > Add new MCP Server: { "mcpServers": { "openpgx": { "command": "npx", "args": ["-y", "openpgx"] } } } Same configuration. OpenPGx uses stdio transport — any MCP-compatible client works: npx openpgx Don't want to install anything? Use the hosted server directly: { "mcpServers": { "openpgx": { "type": "streamable-http", "url": "https://mcp.openpgx.ai/mcp" } } } This connects to our remote MCP server via Streamable HTTP — same 9 tools, zero local setup. Your genome is parsed server-side and stored temporarily in memory (30-minute session TTL). Privacy note: For maximum privacy, prefer the npx install — everything stays on your machine. The remote server processes your data but does not store it permanently. "Upload my genome" → parse your raw DNA file "Can I take Ozempic?" → check semaglutide + GLP1R "What about Venvanse?" → brand names work (60+ brands mapped) "Is modafinil right for me?" → checks COMT + CYP2C19 interactions "Compare sertraline vs escitalopram" → head-to-head comparison "Weight loss medications" → search by category "antidepressivo" → Portuguese works too "ozmpic" → typos are auto-corrected "What's my cancer risk?" → check specific conditions "Full risk report" → all 19 conditions analyzed "Do I have the Alzheimer gene?" → APOE status "Am I at risk for blood clots?" → Factor V Leiden check "Trait report" → all 25+ traits "Am I lactose intolerant?" → lactose persistence check "Am I a morning person?" → chronotype analysis "Supplement protocol" → MTHFR, COMT, VDR, BCMO1, FUT2, CBS "Should I take methylfolate?" → based on your MTHFR status 67 studies · 63 genes · 127 drugs · 19 disease risks · 31 traits — all backed by published research with PMID/DOI. Every gene is backed by at least one published study with interpretations per genotype: | Category | Genes | |---|---| | Drug Metabolism | CYP2D6, CYP2C19, CYP2C9, CYP2B6, CYP3A5, CYP1A2, ALDH2, DPYD, TPMT, NUDT15, GSTP1 | | Drug Targets | VKORC1, DRD2, HTR2C, OPRM1, DPYD, NUDT15, C11ORF65, MTNR1B, GRK4, CLCNKA, F2, F5 | | Drug Transport | SLCO1B1, ABCB1, ABCG2 | | Immune / HLA | HLA-B, HLA-A | | GLP-1 / Incretin | GLP1R, GIPR | | Methylation & Vitamins | MTHFR, COMT, VDR, BCMO1, FUT2, CBS, DHCR7, SLC23A1 | | Lipid Metabolism | PNPLA3, TM6SF2, APOE, APOA5, APOB, LDLR, LIPC, LPL, SORT1, PPARG | | Energy Balance / Obesity | FTO, MC4R, GHSR, CLOCK, SIRT1, BDNF, PCSK1, FABP2, LYPLAL1 | | Glucose / Insulin | GCKR, SLC2A2, PPM1K, MTNR1B, IL6 | | Circadian Rhythm | PER2, CRY2, NR1D1 | | Nutrient Response | FADS1, FABP2, IL6, GRK4, CLCNKA | Full list of supported drugs (click to expand) | Therapeutic Area | Medications | |---|---| | Cardiology & Anticoagulation | warfarin, clopidogrel, rivaroxaban, apixaban, dabigatran, enoxaparin, heparin, nitroglycerin, isosorbide dinitrate, digoxin, amlodipine, losartan, hydrochlorothiazide, chlorthalidone, furosemide | | Statins & Lipid-Lowering | simvastatin, atorvastatin, rosuvastatin, pravastatin, ezetimibe, fenofibrate, gemfibrozil, niacin, evolocumab, alirocumab, mipomersen | | Psychiatry & Neurology | clozapine, olanzapine, risperidone, haloperidol, aripiprazole, quetiapine, fluoxetine, paroxetine, escitalopram, citalopram, venlafaxine, bupropion, carbamazepine, oxcarbazepine, eslicarbazepine, phenytoin, donepezil, memantine, lecanemab | | ADHD & Wakefulness | modafinil, armodafinil, methylphenidate, lisdexamfetamine, amphetamine, atomoxetine, caffeine | | Pain & Opioids | codeine, tramadol, fentanyl, morphine, methadone | | Oncology | capecitabine, fluorouracil, cisplatin, carboplatin, oxaliplatin, paclitaxel, topotecan, methotrexate, tamoxifen | | Immunosuppressants | tacrolimus, azathioprine, mercaptopurine, thioguanine, sulfasalazine | | Metabolic & GLP-1 | semaglutide, liraglutide, tirzepatide, dulaglutide, setmelanotide, metformin, pioglitazone, rosiglitazone, insulin, orlistat, empagliflozin, dapagliflozin | | Infectious Disease | abacavir, efavirenz | | Supplements & Vitamins | omega-3/fish oil/EPA/DHA, vitamin D (cholecalciferol, ergocalciferol, calcitriol), vitamin C (ascorbic acid), folic acid, methylfolate, methylcobalamin, beta-carotene, retinol, resveratrol, nicotinamide riboside, NMN, melatonin, pyridoxine | | Other | allopurinol, febuxostat, theophylline, tizanidine, cannabidiol, ethanol, disulfiram, celecoxib, ibuprofen, naproxen | | Category | Conditions | |---|---| | Oncology | Prostate Cancer, Breast Cancer (BRCA), Colorectal Cancer, Melanoma | | Cardiovascular | Coronary Artery Disease, Atrial Fibrillation | | Neurological | Alzheimer's Disease, Parkinson's Disease | | Metabolic | Type 2 Diabetes, Hereditary Hemochromatosis, Gout | | Autoimmune | Celiac Disease, Psoriasis, Rheumatoid Arthritis, Lupus (SLE) | | Hematological | Venous Thromboembolism (Factor V Leiden) | | Musculoskeletal | Osteoporosis | | Respiratory | Asthma | | Ophthalmological | Age-Related Macular Degeneration | Caffeine metabolism, alcohol flush, lactose tolerance, muscle composition, chronotype, eye color, and more. Beyond disease risk SNPs, the gene studies provide pharmacogenomic guidance across these clinical areas: | Area | What OpenPGx covers | |---|---| | Blood Clotting | Factor V Leiden (F5), Prothrombin mutation (F2), warfarin sensitivity (VKORC1, CYP2C9) | | Drug Hypersensitivity | HLA-B57:01 → abacavir, HLA-A31:01 → carbamazepine DRESS, HLA-B*15:02 → SJS/TEN | | Statin Myopathy | SLCO1B1 poor transport → simvastatin muscle toxicity | | Chemotherapy Toxicity | DPYD deficiency → fluorouracil/capecitabine, TPMT/NUDT15 → thiopurines, GSTP1 → platinum agents | | Opioid Response | CYP2D6 poor/ultrarapid → codeine, tramadol, fentanyl dosing | | Antipsychotic Side Effects | HTR2C → weight gain risk, DRD2 → efficacy, CYP2D6 → metabolism | | Obesity & Weight Loss | FTO, MC4R, BDNF, PCSK1, GHSR, CLOCK, SIRT1, LYPLAL1 — 9 genes affecting appetite, metabolism, fat distribution | | Cardiovascular Lipids | APOE, APOA5, APOB, LDLR, LIPC, LPL, SORT1, PNPLA3, TM6SF2 — LDL, HDL, triglycerides, fatty liver | | Diabetes & Glucose | GCKR, SLC2A2, MTNR1B, PPM1K, PPARG, IL6 — insulin secretion, glucose sensing, BCAA metabolism | | Salt Sensitivity & Hypertension | GRK4, CLCNKA — sodium handling, diuretic response | | Circadian & Sleep | PER2, CRY2, NR1D1, CLOCK — chronotype, shift work risk, melatonin response | | Vitamin Metabolism | MTHFR → folate, DHCR7 → vitamin D synthesis, BCMO1 → beta-carotene, VDR → vitamin D receptor, SLC23A1 → vitamin C, FUT2 → B12 | | Omega-3 & Fat Absorption | FADS1 → DHA/EPA conversion, FABP2 → dietary fat absorption | | GLP-1 Drug Response | GLP1R, GIPR → semaglutide/tirzepatide efficacy prediction | | Tool | Description | |---|---| upload_genome | Parse raw DNA data (23andMe, Genera) | check_medication | Smart drug lookup — brand names, generics, typos, categories | full_pgx_report | Complete pharmacogenomic report | supplement_protocol | Supplement optimization based on gene variants | compare_medications | Head-to-head drug comparison | check_risk | Check genetic risk for a specific disease | full_risk_report | Comprehensive disease risk report | trait_report | All genetic traits analysis | full_report | Everything combined: medications + risks + traits | OpenPGx is designed for open source contribution. Adding a new drug-gene study requires zero code changes — just a JSON file. - Find a pharmacogenomic study (PubMed, CPIC guidelines, PharmGKB) - Create a JSON file in data/pgx/studies/ following the naming pattern:{gene}_{year}_{slug}.json - Open a PR The system automatically: - Creates the gene if it doesn't exist in the catalog - Registers all rsIDs for DNA parsing - Builds the drug-to-gene index - Makes the interpretations available in all reports { "gene": "GENE_SYMBOL", "category": "drug_metabolism", "gene_description": "What this gene does", "drugs": ["generic_drug_name"], "source": { "pmid": "12345678", "doi": "10.xxxx/xxxxx", "source_type": "pubmed", "title": "Study title", "journal": "Journal name", "year": 2024, "cohort_size": 1000, "url": "https://pubmed.ncbi.nlm.nih.gov/12345678/", "finding": "One-line summary of the key finding" }, "snps": [ { "rsid": "rs12345", "risk_allele": "A", "reference_allele": "G", "interpretations": { "AA": { "phenotype": "Poor Metabolizer", "effect": "...", "recommendation": "...", "severity": "severe" }, "AG": { "phenotype": "Intermediate", "effect": "...", "recommendation": "...", "severity": "moderate" }, "GG": { "phenotype": "Normal", "effect": "...", "recommendation": "...", "severity": "info" } } } ], "evidence_level": "established" } Full schema: openpgx.schema.json (see $defs/study_contribution ) Patient-facing OpenPGx files are a single JSON object. The only required top-level field is openpgx_version (must be "0.4.0" ). Everything else follows openpgx.schema.json . Top-level structure: | Field | Required | Role | |---|---|---| openpgx_version | yes | Specification version; const 0.4.0 | metadata | no | generated_at , generator , sources ; optional last_updated | provenance | no | Audit trail: version , previous_version_hash , changelog[] (date , reason , optional description , affected_sections ) | patient | no | Profile only (no genotypes): id , raw_data_source , raw_data_format , extraction_date , ancestry | observations | no | Raw measurements: each item has gene , rsid , genotype ; optional chromosome , position , diplotype , activity_score | medications | no | Per-drug blocks: drug (name , class , optional brand_names , atc_code , drugbank_id ), pgx_associations[] , optional interactions[] , optional dosing , plus parsed_at , parse_source , confidence (score , evidence_level , …) | risks | no | Disease risk: condition , category (enum incl. oncology, cardiovascular, …), overall_risk , risk_snps[] , evidence , actionable , recommendation , studies ; optional icd10 , polygenic_score , lifetime_risk | traits | no | trait , category , snps[] , your_phenotype , description , evidence , practical_advice , studies | fhir_mapping | no | Hints for FHIR: patient_reference , service_request_id , resource_mappings , terminology_systems | Minimal valid skeleton (only the required field): { "openpgx_version": "0.4.0" } Example with the main optional sections (names shortened; see schema for full $defs ): { "openpgx_version": "0.4.0", "metadata": { "generated_at": "2026-04-12T12:00:00Z", "generator": "openpgx-mcp/1.x", "sources": ["CPIC", "PharmGKB"] }, "patient": { "raw_data_source": "23andMe", "ancestry": "european" }, "observations": [ { "gene": "CYP2C19", "rsid": "rs4244285", "genotype": "AG" } ], "medications": [ { "drug": { "name": "clopidogrel", "class": "antiplatelet" }, "pgx_associations": [ { "gene": "CYP2C19", "rsid": "rs4244285", "effect": "Reduced active metabolite formation", "evidence": { "level": "established", "sources": [] }, "clinical_recommendation": "Consider alternative antiplatelet per guideline" } ], "parsed_at": "2026-04-12T12:00:00Z", "parse_source": "cpic_guideline", "confidence": { "score": 0.9, "evidence_level": "established" } } ], "fhir_mapping": { "resource_mappings": { "observations": "Observation (code: LOINC 81247-9 'Master HL7 genetic variant reporting panel')", "medications": "DiagnosticReport (code: LOINC 51969-4 'Genetic analysis report') + MolecularSequence", "risks": "RiskAssessment (method: LOINC 75321-0 'Clinical genomics report')", "traits": "Observation (category: genomics)" } } } Full schema: openpgx.schema.json Raw DNA file (23andMe .txt / Genera .csv) | v +------------------+ | Parser | --> Extracts relevant SNPs from 600K+ raw data | (local, private) | +------------------+ | v +------------------+ | Study Catalog | --> 67 studies x 63 genes x 127 drugs | (data/pgx/ | Auto-creates gene definitions | studies/*.json) | Builds drug-gene index at runtime +------------------+ | v +------------------+ | MCP Tools | --> 9 tools for AI interaction | (server-core.ts) | Falls back to AI web search when needed +------------------+ | v AI Assistant (Claude, Cursor, ChatGPT, etc.) All processing happens locally. No data is sent to any server. - 23andMe (.txt) — fully supported - Genera (.csv) — fully supported - AncestryDNA — coming soon - VCF — coming soon OpenPGx resolves drug names through 6 layers: - Brand > G