OpenFDA Drug Intelligence, Approvals, Recalls & Safety Scraper 💊

Instant, turnkey extraction of official U.S. FDA Drug Labels, NDC package numbers, Active Ingredients, Clinical Indications, Boxed Warnings, FDA Adverse Event Reporting System (FAERS) records, and Enforcement Product Recalls via the official openFDA regulatory database.

⚡ Instant Data Scraper Guarantee (No Setup Required):

• 🟢 Zero-Config: No proxies, FDA API keys, or browser setups required.
• 🚀 1-Click Free Test: Just click the green "Save & Start" button with the pre-filled inputs to test live data extraction in < 2 seconds.
• 📊 Instant Export: Download verified pharmaceutical intelligence directly to Excel (.xlsx), CSV, or JSON.
• 🇺🇸 100% Official U.S. Federal Data: Clean, structured regulatory datasets optimized for healthcare AI agents, clinical research, pharmacovigilance, and CRM pipelines.

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⚡ Ready-to-Run Input Preview (Zero Configuration Required)

You don't need to write code or configure complex parameters. The Actor comes completely pre-configured:

Setting	What You Provide	Default Value (Ready to Run)	Quick Link
Target Drug or Pharmaceutical Firm	Brand name, generic compound, NDC, or firm	"aspirin"	👉 Run with Default Values
Search Category	Labels, Recalls, or Adverse Events	"labels"	👉 Select FDA Category
Maximum Records	Number of regulatory records to return	20 (Sub-2s quick test)	👉 Open Input Tab

💡 Tip: Click the green "Start" button in Apify Console to run your first test immediately with zero changes.

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🔍 Click to View Live Sample Output Data (JSON Preview)

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📊 Comprehensive Data Extraction Coverage

Data Domain	Extracted Attribute	Business & Clinical Value
🏷️ Product Identity	brandName, genericName, substanceName, activeIngredients	Standardized molecular and chemical drug names for formulary matching.
💊 Formulation & Delivery	route, dosageForm	Clarifies administration route (ORAL, TOPICAL, INTRAVENOUS) and form (TABLET, SOLUTION).
🏛️ Regulatory Approval	applicationNumber, marketingCategory, productNdc, packageNdc	Identifies NDA (New Drug), ANDA (Generic), or BLA (Biologic) regulatory filing numbers.
🏢 Manufacturer & Labeler	manufacturerName, recallingFirm, city, state	Corporate manufacturing entity identification for supply chain diligence.
📋 Clinical Indications	indicationsAndUsage, pharmClass	Medical purpose, symptoms treated, and pharmacological class (EPC / MoA).
⚠️ Safety & Precautions	warningsAndPrecautions, hasBoxedWarning, contraindications	Crucial safety disclosures, contraindications, and FDA Black Box warnings.
🚨 Recall & Enforcement	recallNumber, classification, recallStatus, reasonForRecall, distributionPattern	Class I (critical), Class II (reversible), or Class III enforcement recall metrics.
📉 Adverse Drug Reactions	reportId, isSerious, seriousnessCriteria, reportedReactions, patientDemographics	Post-market FAERS patient demographic data, seriousness outcomes, and adverse reactions.
🛡️ Pharma Intelligence	regulatoryRiskTier, fdaComplianceBadge, verificationUrl	Derived risk classifications and direct links to DailyMed / FDA safety portals.

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📊 Export to CSV / Excel in 1-Click

Download clean, formatted data directly without writing a single line of code:

• Microsoft Excel (.xlsx): Pivot-ready spreadsheet with verified formatting for regulatory affairs.
• CSV Format: Instantly upload into clinical databases, ERP systems, Salesforce Health Cloud, or Google Sheets.
• JSON / API: Feed real-time drug intelligence into your webhooks, n8n, Make, or custom microservices.

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✨ Enterprise v3.0 Data Enrichment (Included Free)

This actor is powered by the v3.0 Enterprise Data Enrichment Engine. Every record is automatically validated, normalized, and enriched with 20+ derived high-value fields at zero additional cost and < 3ms execution overhead:

Enrichment Capability	What It Delivers	How It Helps Your Healthcare & Pharma Workflow
🛡️ Data Quality Score (0–100%)	_dataQualityScore: 100	Instantly filter out sparse records before ingestion into clinical databases.
🔑 Deduplication Fingerprint	_deduplicationFingerprint: "sha256:..."	Cryptographic entity hash for 100% idempotent database upserts.
⚖️ Regulatory Risk Tier	regulatoryRiskTier	Automatically flags Black Box warnings, Class I recalls, or serious adverse outcomes.
🏅 FDA Compliance Badge	fdaComplianceBadge	Categorizes NDA vs ANDA vs OTC monograph vs active enforcement action.
🧹 Text Normalization	Cleaned excerpts & Unicode	Strips broken HTML/XML tags and formatting artifacts from raw label text.
🧠 AI Vector & RAG Chunking	_rag: { estimatedTokens, chunksCount, chunks }	Pre-chunked into semantic chunks ready for LangChain, LlamaIndex & Vector DBs.
⚡ 3-Tier Configurable Depth	Enriched (Default), Standard, Basic	Choose full enterprise enrichment, clean standard fields, or ultra-fast raw extract.

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📥 Input Parameters

All parameters come pre-filled with sensible defaults so you can run the Actor right away:

Parameter	Type	Required	Default / Prefill	Description
Target Drug or Firm (drugNameOrFirm)	string	No	"aspirin"	Brand name, generic molecule, substance, NDC, or recalling firm (e.g., "aspirin", "lipitor", "pfizer")
Search Category (searchType)	string	No	"labels"	FDA database: "labels" (Labels & Approvals), "recalls" (Enforcement Recalls), or "adverse_events" (FAERS)
Max Records Limit (maxResults)	integer	No	20	Maximum number of regulatory records to harvest (1 to 100)
Include Keywords (Filter) (includeKeywords)	string	No	""	Comma-separated terms to filter results (e.g., "tablets, oral, pain")
Exclude Keywords (Filter) (excludeKeywords)	string	No	""	Filter out results containing these comma-separated terms
Enable AI / RAG Token Chunking (enableRagEnrichment)	boolean	No	true	Generate semantic vector chunks and token counts for LLMs
Data Enrichment Tier (enrichmentMode)	string	No	"Enriched"	Enriched (20+ fields), Standard (core fields), or Basic (raw)
Preferred Output Format (outputFormat)	string	No	"JSON"	Output format preference: JSON, Markdown, or Flat

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🎯 Common High-Impact Use Cases

• 🏥 Pharmacovigilance & Safety Monitoring: Track post-market FAERS reports and adverse reactions for any medicinal product.
• 💊 Competitive Intelligence & Generic Entry: Monitor FDA approvals, ANDA applications, formulation routes, and NDC packaging.
• 🚨 Supply Chain & Pharmacy Recall Alerts: Instantly audit active Class I, Class II, and Class III FDA recalls by manufacturer firm or drug name.
• 🤖 Healthcare AI & Clinical Decision Support (CDSS): Ingest structured indications, contraindications, and boxed warnings into RAG knowledge bases.
• 📋 Formulary Management & PBM Audits: Verify active NDC codes, labeler names, and marketing categories against payer master lists.

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💻 Developer API & Workflow Integrations

1. cURL / Direct API Execution

curl -X POST "https://api.apify.com/v2/acts/trusted_tech_spot~openfda-drug-approvals-recalls-safety-scraper/runs?token=YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "drugNameOrFirm": "ibuprofen",
    "searchType": "labels",
    "maxResults": 10
  }'

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2. Node.js (apify-client)

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({
    token: 'YOUR_APIFY_TOKEN',
});

// Run actor with custom parameters
const run = await client.actor('trusted_tech_spot/openfda-drug-approvals-recalls-safety-scraper').call({
    drugNameOrFirm: 'atorvastatin',
    searchType: 'labels',
    maxResults: 25
});

// Fetch structured items from the dataset
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(`Extracted regulatory intelligence:`, items);

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3. Python (apify-client)

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

# Run actor to monitor active drug recalls
run = client.actor("trusted_tech_spot/openfda-drug-approvals-recalls-safety-scraper").call(run_input={
    "drugNameOrFirm": "metformin",
    "searchType": "recalls",
    "maxResults": 20
})

# Fetch dataset items
items = client.dataset(run["defaultDatasetId"]).list_items().items
for item in items:
    for record in item.get("records", []):
        print(f"Recall: {record.get('recallNumber')} | Firm: {record.get('recallingFirm')} | Risk: {record.get('regulatoryRiskTier')}")

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4. n8n Automation Workflow

Connect this Actor to your n8n automation pipeline to receive automated Slack, email, or webhook alerts whenever new FDA recalls or adverse events are reported:

{
  "nodes": [
    {
      "parameters": {
        "method": "POST",
        "url": "https://api.apify.com/v2/acts/trusted_tech_spot~openfda-drug-approvals-recalls-safety-scraper/run-sync-get-dataset-items?token=YOUR_APIFY_TOKEN",
        "sendBody": true,
        "specifyBody": "json",
        "jsonBody": "{\n  \"drugNameOrFirm\": \"aspirin\",\n  \"searchType\": \"recalls\",\n  \"maxResults\": 10\n}"
      },
      "id": "1",
      "name": "Trigger OpenFDA Scraper",
      "type": "n8n-nodes-base.httpRequest",
      "typeVersion": 4.2,
      "position": [250, 300]
    }
  ]
}

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5. Make.com (Integromat) Workflow

1. Create a new scenario in Make.com.
2. Add an HTTP — Make a request module:
   • URL: https://api.apify.com/v2/acts/trusted_tech_spot~openfda-drug-approvals-recalls-safety-scraper/run-sync-get-dataset-items?token=YOUR_APIFY_TOKEN
   • Method: POST
   • Body type: Raw (application/json)
   • Request content: {"drugNameOrFirm": "acetaminophen", "searchType": "labels", "maxResults": 15}
3. Add a Google Sheets — Add a row or Slack — Send message module to auto-archive new drug safety updates!

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6. LangChain RAG & Vector Ingestion (Healthcare AI)

from apify_client import ApifyClient
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_core.documents import Document

# 1. Fetch pre-chunked dataset from Apify
client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("trusted_tech_spot/openfda-drug-approvals-recalls-safety-scraper").call(run_input={
    "drugNameOrFirm": "pembrolizumab",
    "searchType": "labels",
    "enableRagEnrichment": True
})
items = client.dataset(run["defaultDatasetId"]).list_items().items

# 2. Extract semantic chunks directly into LangChain documents
docs = []
for item in items:
    chunks = item.get("_rag", {}).get("chunks", [str(item)])
    for chunk in chunks:
        docs.append(Document(
            page_content=chunk,
            metadata={"source": "openfda-drug-intelligence", "query": item.get("query")}
        ))

# 3. Ingest into ChromaDB / Pinecone vector store
vector_store = Chroma.from_documents(docs, OpenAIEmbeddings())
print(f"Successfully indexed {len(docs)} clinical drug knowledge chunks into Vector DB!")

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❓ Frequently Asked Questions (FAQ)

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💬 Technical Support & Custom Demands

Need specialized biomedical feeds, custom NDC mapping, EHR integrations, or enterprise SLA support?

• 🐛 Open an Issue: Open a ticket on the Actor Issues Tab for 24-hour engineer response.
• ✉️ Direct Email Support: Reach out to contact@trustedtechspot.com for custom scraping jobs or dedicated endpoints.
• 💡 Feature Requests: Request new endpoints (medical devices, veterinary drugs, food recalls) anytime.

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🔒 Privacy & Regulatory Disclaimer

• OpenFDA Regulatory Notice: Data sourced from official openFDA API. OpenFDA and FDA do not validate individual records for clinical liability. This tool is designed for research, compliance, and intelligence workflows and is not a substitute for professional medical advice.
• Enterprise Grade Sanitization: Automatically scrubs sensitive headers and tracking parameters before saving data.

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🏷️ Search & Discovery Keywords

openfda scraper • fda drug recalls • drug label api • ndc code finder • faers adverse events • boxed warning database • pharmaceutical intelligence • clinical decision support • apify actor • healthcare data scraper 2026