Query openFDA API for drugs, devices, adverse events, recalls, regulatory submissions (510k, PMA), substance identification (UNII), for FDA regulatory data analysis and safety research.
npxskills add davila7/claude-code-templates--skill fda-databaseLoading…
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:
This skill should be used when working with:
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)
While the API works without a key, registering provides higher rate limits:
Register at: https://open.fda.gov/apis/authentication/
Set as environment variable:
export FDA_API_KEY="your_key_here"
# 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
Access 6 drug-related endpoints covering the full drug lifecycle from approval to post-market surveillance.
Endpoints:
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
Access 9 device-related endpoints covering medical device safety, approvals, and registrations.
Endpoints:
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
Access 2 food-related endpoints for safety monitoring and recalls.
Endpoints:
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
Access veterinary drug adverse event data with species-specific information.
Endpoint:
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
Access molecular-level substance data with UNII codes, chemical structures, and relationships.
Endpoints:
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
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"]
}
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
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
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}*")
}
All API responses follow this structure:
{
"meta": {
"disclaimer": "...",
"results": {
"skip": 0,
"limit": 100,
"total": 15234
}
},
"results": [
# Array of result objects
]
}
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
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
DO:
# Specific field search
search="patient.drug.medicinalproduct:aspirin"
DON'T:
# Overly broad wildcard
search="*aspirin*"
The FDAQuery class handles rate limiting automatically, but be aware of limits:
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)
When counting/aggregating, use .exact suffix:
# Count exact phrases
fda.count_by_field("drug", "event",
search="...",
field="patient.reaction.reactionmeddrapt",
exact=True) # Adds .exact automatically
Clean and validate search terms:
def clean_drug_name(name):
"""Clean drug name for query."""
return name.strip().replace('"', '\\"')
drug_name = clean_drug_name(user_input)
For detailed information about:
references/api_basics.mdreferences/drugs.mdreferences/devices.mdreferences/foods.mdreferences/animal_veterinary.mdreferences/other.mdscripts/fda_query.pyMain query module with FDAQuery class providing:
scripts/fda_examples.pyComprehensive examples demonstrating:
Run examples:
python scripts/fda_examples.py
Issue: Rate limit exceeded
Issue: No results found
Issue: Invalid query syntax
references/api_basics.mdIssue: Missing fields in results
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