fda-database

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:

1. Adverse Events - Reports of side effects, errors, and therapeutic failures
2. Product Labeling - Prescribing information, warnings, indications
3. NDC Directory - National Drug Code product information
4. Enforcement Reports - Drug recalls and safety actions
5. Drugs@FDA - Historical approval data since 1939
6. 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:

1. Adverse Events - Device malfunctions, injuries, deaths
2. 510(k) Clearances - Premarket notifications
3. Classification - Device categories and risk classes
4. Enforcement Reports - Device recalls
5. Recalls - Detailed recall information
6. PMA - Premarket approval data for Class III devices
7. Registrations & Listings - Manufacturing facility data
8. UDI - Unique Device Identification database
9. 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:

1. Adverse Events - Food, dietary supplement, and cosmetic events
2. 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:

1. 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:

1. Substance Data - UNII, CAS, chemical structures, relationships
2. 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"br

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