fda-database

name fda-database description Query openFDA API for drugs, devices, adverse events, recalls, regulatory submissions (510k, PMA), substance identification (UNII), for FDA regulatory data analysis and safety research. license Unknown metadata {"skill-author":"K-Dense Inc."} FDA Database Access Overview Access comprehensive FDA regulatory data through openFDA, the FDA's initiative to provide open APIs for public datasets. Query information about drugs, medical devices, foods, animal/veterinary products, and substances using Python with standardized interfaces. Key capabilities: Query adverse events for drugs, devices, foods, and veterinary products Access product labeling, approvals, and regulatory submissions Monitor recalls and enforcement actions Look up National Drug Codes (NDC) and substance identifiers (UNII) Analyze device classifications and clearances (510k, PMA) Track drug shortages and supply issues Research chemical structures and substance relationships When to Use This Skill This skill should be used when working with: Drug research : Safety profiles, adverse events, labeling, approvals, shortages Medical device surveillance : Adverse events, recalls, 510(k) clearances, PMA approvals Food safety : Recalls, allergen tracking, adverse events, dietary supplements Veterinary medicine : Animal drug adverse events by species and breed Chemical/substance data : UNII lookup, CAS number mapping, molecular structures Regulatory analysis : Approval pathways, enforcement actions, compliance tracking Pharmacovigilance : Post-market surveillance, safety signal detection Scientific research : Drug interactions, comparative safety, epidemiological studies Quick Start 1. Basic Setup from scripts.fda_query import FDAQuery # Initialize (API key optional but recommended) fda = FDAQuery(api_key= "YOUR_API_KEY" ) # Query drug adverse events events = fda.query_drug_events( "aspirin" , limit= 100 ) # Get drug labeling label = fda.query_drug_label( "Lipitor" , brand= True ) # Search device recalls recalls = fda.query( "device" , "enforcement" , search= "classification:Class+I" , limit= 50 ) 2. API Key Setup While the API works without a key, registering provides higher rate limits: Without key : 240 requests/min, 1,000/day With key : 240 requests/min, 120,000/day Register at: https://open.fda.gov/apis/authentication/ Set as environment variable: export FDA_API_KEY= "your_key_here" 3. Running Examples # Run comprehensive examples python scripts/fda_examples.py # This demonstrates: # - Drug safety profiles # - Device surveillance # - Food recall monitoring # - Substance lookup # - Comparative drug analysis # - Veterinary drug analysis FDA Database Categories Drugs Access 6 drug-related endpoints covering the full drug lifecycle from approval to post-market surveillance. Endpoints: Adverse Events - Reports of side effects, errors, and therapeutic failures Product Labeling - Prescribing information, warnings, indications NDC Directory - National Drug Code product information Enforcement Reports - Drug recalls and safety actions Drugs@FDA - Historical approval data since 1939 Drug Shortages - Current and resolved supply issues Common use cases: # Safety signal detection fda.count_by_field( "drug" , "event" , search= "patient.drug.medicinalproduct:metformin" , field= "patient.reaction.reactionmeddrapt" ) # Get prescribing information label = fda.query_drug_label( "Keytruda" , brand= True ) # Check for recalls recalls = fda.query_drug_recalls(drug_name= "metformin" ) # Monitor shortages shortages = fda.query( "drug" , "drugshortages" , search= "status:Currently+in+Shortage" ) Reference: See references/drugs.md for detailed documentation Devices Access 9 device-related endpoints covering medical device safety, approvals, and registrations. Endpoints: Adverse Events - Device malfunctions, injuries, deaths 510(k) Clearances - Premarket notifications Classification - Device categories and risk classes Enforcement Reports - Device recalls Recalls - Detailed recall information PMA - Premarket approval data for Class III devices Registrations & Listings - Manufacturing facility data UDI - Unique Device Identification database COVID-19 Serology - Antibody test performance data Common use cases: # Monitor device safety events = fda.query_device_events( "pacemaker" , limit= 100 ) # Look up device classification classification = fda.query_device_classification( "DQY" ) # Find 510(k) clearances clearances = fda.query_device_510k(applicant= "Medtronic" ) # Search by UDI device_info = fda.query( "device" , "udi" , search= "identifiers.id:00884838003019" ) Reference: See references/devices.md for detailed documentation Foods Access 2 food-related endpoints for safety monitoring and recalls. Endpoints: Adverse Events - Food, dietary supplement, and cosmetic events Enforcement Reports - Food product recalls Common use cases: # Monitor allergen recalls recalls = fda.query_food_recalls(reason= "undeclared peanut" ) # Track dietary supplement events events = fda.query_food_events( industry= "Dietary Supplements" ) # Find contamination recalls listeria = fda.query_food_recalls( reason= "listeria" , classification= "I" ) Reference: See references/foods.md for detailed documentation Animal & Veterinary Access veterinary drug adverse event data with species-specific information. Endpoint: Adverse Events - Animal drug side effects by species, breed, and product Common use cases: # Species-specific events dog_events = fda.query_animal_events( species= "Dog" , drug_name= "flea collar" ) # Breed predisposition analysis breed_query = fda.query( "animalandveterinary" , "event" , search= "reaction.veddra_term_name:*seizure*+AND+" "animal.breed.breed_component:*Labrador*" ) Reference: See references/animal_veterinary.md for detailed documentation Substances & Other Access molecular-level substance data with UNII codes, chemical structures, and relationships. Endpoints: Substance Data - UNII, CAS, chemical structures, relationships NSDE - Historical substance data (legacy) Common use cases: # UNII to CAS mapping substance = fda.query_substance_by_unii( "R16CO5Y76E" ) # Search by name results = fda.query_substance_by_name( "acetaminophen" ) # Get chemical structure structure = fda.query( "other" , "substance" , search= "names.name:ibuprofen+AND+substanceClass:chemical" ) Reference: See references/other.md for detailed documentation Common Query Patterns Pattern 1: Safety Profile Analysis Create comprehensive safety profiles combining multiple data sources: def drug_safety_profile ( fda, drug_name ): """Generate complete safety profile.""" # 1. Total adverse events events = fda.query_drug_events(drug_name, limit= 1 ) total = events[ "meta" ][ "results" ][ "total" ] # 2. Most common reactions reactions = fda.count_by_field( "drug" , "event" , search= f"patient.drug.medicinalproduct:* {drug_name} *" , field= "patient.reaction.reactionmeddrapt" , exact= True ) # 3. Serious events serious = fda.query( "drug" , "event" , search= f"patient.drug.medicinalproduct:* {drug_name} *+AND+serious:1" , limit= 1 ) # 4. Recent recalls recalls = fda.query_drug_recalls(drug_name=drug_name) return { "total_events" : total, "top_reactions" : reactions[ "results" ][: 10 ], "serious_events" : serious[ "meta" ][ "results" ][ "total" ], "recalls" : recalls[ "results" ] } Pattern 2: Temporal Trend Analysis Analyze trends over time using date ranges: from datetime import datetime, timedelta def get_monthly_trends ( fda, drug_name, months= 12 ): """Get monthly adverse event trends.""" trends = [] for i in range (months): end = datetime.now() - timedelta(days= 30 *i) start = end - timedelta(days= 30 ) date_range = f"[ {start.strftime( '%Y%m%d' )} +TO+ {end.strftime( '%Y%m%d' )} ]" search = f"patient.drug.medicinalproduct:* {drug_name} *+AND+receivedate: {date_range} " result = fda.query( "drug" , "event" , search=search, limit= 1 ) count = result[ "meta" ][ "results" ][ "total" ] if "meta" in result else 0 trends.append({ "month" : start.strftime( "%Y-%m" ), "events" : count }) return trends Pattern 3: Comparative Analysis Compare multiple products side-by-side: def compare_drugs ( fda, drug_list ): """Compare safety profiles of multiple drugs.""" comparison = {} for drug in drug_list: # Total events events = fda.query_drug_events(drug, limit= 1 ) total = events[ "meta" ][ "results" ][ "total" ] if "meta" in events else 0 # Serious events serious = fda.query( "drug" , "event" , search= f"patient.drug.medicinalproduct:* {drug} *+AND+serious:1" , limit= 1 ) serious_count = serious[ "meta" ][ "results" ][ "total" ] if "meta" in serious else 0 comparison[drug] = { "total_events" : total, "serious_events" : serious_count, "serious_rate" : (serious_count/total* 100 ) if total > 0 else 0 } return comparison Pattern 4: Cross-Database Lookup Link data across multiple endpoints: def comprehensive_device_lookup ( fda, device_name ): """Look up device across all relevant databases.""" return { "adverse_events" : fda.query_device_events(device_name, limit= 10 ), "510k_clearances" : fda.query_device_510k(device_name=device_name), "recalls" : fda.query( "device" , "enforcement" , search= f"product_description:* {device_name} *" ), "udi_info" : fda.query( "device" , "udi" , search= f"brand_name:* {device_name} *" ) } Working with Results Response Structure All API responses follow this structure: { "meta" : { "disclaimer" : "..." , "results" : { "skip" : 0 , "limit" : 100 , "total" : 15234 } }, "results" : [ # Array of result objects ] } Error Handling Always handle potential errors: result = fda.query_drug_events( "aspirin" , limit= 10 ) if "error" in result: print ( f"Error: {result[ 'error' ]} " ) elif "results" not in result or len (result[ "results" ]) == 0 : print ( "No results found" ) else : # Process results for event in result[ "results" ]: # Handle event data pass Pagination For large result sets, use pagination: # Automatic pagination all_results = fda.query_all( "drug" , "event" , search= "patient.drug.medicinalproduct:aspirin" , max_results= 5000 ) # Manual pagination for skip in range ( 0 , 1000 , 100 ): batch = fda.query( "drug" , "event" , search= "..." , limit= 100 , skip=skip) # Process batch Best Practices 1. Use Specific Searches DO: # Specific field search search= "patient.drug.medicinalproduct:aspirin" DON'T: # Overly broad wildcard search= "*aspirin*" 2. Implement Rate Limiting The FDAQuery class handles rate limiting automatically, but be aware of limits: 240 requests per minute 120,000 requests per day (with API key) 3. Cache Frequently Accessed Data The FDAQuery class includes built-in caching (enabled by default): # Caching is automatic fda = FDAQuery(api_key=api_key, use_cache= True , cache_ttl= 3600 ) 4. Use Exact Matching for Counting When counting/aggregating, use .exact suffix: