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Building a Pharma Competitive Intelligence Report in 60 Seconds

Walk through building a GLP-1 drug competitive landscape report using FDA, ClinicalTrials.gov, RxNorm, PubMed, and SEC data through Pipeworx PharmaPulse.

A pharmaceutical competitive intelligence report typically takes a consulting firm days to produce and costs tens of thousands of dollars. It requires pulling data from FDA databases, clinical trial registries, patent offices, financial filings, and academic literature — then synthesizing it into actionable insights.

With an AI agent connected to Pipeworx PharmaPulse, that same report takes about 60 seconds. Here’s exactly how it works, using GLP-1 receptor agonists (Ozempic, Mounjaro, Wegovy) as the example.

Step 1: Who has FDA approvals?

Ask: “What GLP-1 receptor agonist drugs are FDA approved?”

Your AI calls fda_drug_approvals and returns:

  • Semaglutide (Ozempic, Wegovy) — Novo Nordisk
  • Tirzepatide (Mounjaro, Zepbound) — Eli Lilly
  • Liraglutide (Victoza, Saxenda) — Novo Nordisk
  • Dulaglutide (Trulicity) — Eli Lilly
  • Exenatide (Byetta, Bydureon) — AstraZeneca

Approval dates, indications, dosage forms — straight from FDA.

Step 2: What’s the safety picture?

Ask: “What are the most common adverse events for semaglutide?”

Your AI calls fda_drug_events for semaglutide and returns FAERS data: 54,000+ reports, top reactions by frequency (nausea, vomiting, diarrhea, abdominal pain), demographics, and outcomes. Call fda_event_counts for trend analysis — are adverse event reports increasing or stabilizing?

Then rxnorm_interactions for drug-drug interactions with semaglutide.

This is the same FAERS data that pharmacovigilance teams at pharmaceutical companies monitor. It’s public, but few people know how to query it efficiently.

Step 3: What’s in the pipeline?

Ask: “Show me active Phase 2 and Phase 3 clinical trials for GLP-1 drugs.”

Your AI calls clinical_trials_search filtered by intervention (GLP-1), phase (2, 3), and status (recruiting, active). Returns:

  • Trial IDs, titles, sponsors, phases, enrollment targets
  • New indications being tested (NASH, Alzheimer’s, heart failure, sleep apnea)
  • Who’s running the most trials (Novo Nordisk, Lilly, smaller biotechs)

Then clinical_trials_sponsor_trials for Novo Nordisk and Eli Lilly to see their complete pipeline activity — not just GLP-1, but everything in development.

Step 4: What does the research say?

Ask: “Find recent PubMed publications on GLP-1 receptor agonists and cardiovascular outcomes.”

Your AI calls search_pubmed and returns recent publications from major journals — clinical trial results, meta-analyses, real-world evidence studies. This tells you where the clinical evidence is heading.

Step 5: What are the financials?

Ask: “How much revenue does Novo Nordisk generate from Ozempic and Wegovy?”

Your AI calls edgar_company_filings for Novo Nordisk’s latest 10-K or 20-F, then edgar_company_facts for structured revenue data. Ozempic alone generated over $18 billion in 2024. Cross-reference with Eli Lilly’s Mounjaro revenue to understand market share dynamics.

Step 6: Patent landscape

Ask: “When do key GLP-1 patents expire?”

Your AI calls search_patents for semaglutide and tirzepatide patents, showing filing dates, expiry dates, and patent claims. Patent cliffs drive generic competition timelines — critical for investment and competitive strategy.

Or: one call

Instead of six steps, call pharma_drug_profile with “semaglutide” — the compound tool chains FDA, ClinicalTrials.gov, RxNorm, PubMed, and SEC data into a single comprehensive profile.

What this replaces

A traditional competitive intelligence engagement for a drug class like GLP-1 involves:

  • Analyst time: 40-80 hours
  • Database subscriptions: Evaluate, Citeline, Cortellis ($50K-200K/year)
  • Consulting fees: $25,000-100,000 per report

An AI with PharmaPulse accesses the primary sources directly. It doesn’t replace expert judgment, but it eliminates the data gathering bottleneck entirely. The analyst’s time goes to interpretation and strategy, not pulling data from five different systems.

Who builds these reports

  • Pharma BD teams evaluating licensing opportunities and competitive threats
  • Healthcare investors conducting due diligence on biotech companies
  • Clinical development teams understanding the competitive trial landscape
  • Medical affairs staying current on safety profiles and clinical evidence
  • Consulting firms accelerating their own CI deliverables

Connect

{
  "mcpServers": {
    "pipeworx-pharma": {
      "url": "https://gateway.pipeworx.io/mcp?vertical=pharma"
    }
  }
}

Or call ask_pipeworx with “give me a competitive landscape for GLP-1 drugs” and the gateway orchestrates across FDA, NIH, NLM, SEC, and USPTO data automatically.