⚕ ClinicalEdge — Pharma Competitive Intelligence Agent

"What is the competitive landscape for KRAS G12C inhibitors in NSCLC as of 2024?"
One question. Three specialist AI agents. One structured intelligence report. Under 90 seconds.

🎯 Problem Statement

Pharmaceutical competitive intelligence (CI) reports take 3–5 days of manual analyst work:

Hours searching PubMed for relevant literature
Manually cross-referencing drug pipeline databases for phase/target data
Parsing FDA approval databases for regulatory history
Writing up a coherent multi-section report

ClinicalEdge automates this end-to-end using a coordinated multi-agent AI system powered by the Google Agent Development Kit (ADK), MCP servers, and Gemini.

🏗 Architecture

ANALYST QUERY (natural language)
         │
         ▼
 ┌───────────────────────────────────┐
 │   ROOT ORCHESTRATOR (ADK LlmAgent)│
 │   • Guardrails: PHI + scope       │
 │   • Entity extraction             │
 │   • asyncio.gather fan-out        │
 │   • Gemini synthesis              │
 └──────────┬────────────┬───────────┘
            │            │           │
            ▼            ▼           ▼
  ┌──────────────┐ ┌──────────┐ ┌──────────────┐
  │ LITERATURE   │ │ PIPELINE │ │  REGULATORY  │
  │ SCOUT        │ │ SCOUT    │ │  WATCH       │
  │ (PubMed MCP) │ │ (OT MCP) │ │ (FDA MCP)    │
  └──────┬───────┘ └────┬─────┘ └──────┬───────┘
         │              │              │
         ▼              ▼              ▼
    NCBI EUtils   Open Targets     openFDA API
    (literature)  Platform (EBI)   (approvals/
                  (drug pipeline)   FAERS/labels)
         │              │              │
         └──────────────┴──────────────┘
                        │
                        ▼
            5-SECTION INTELLIGENCE REPORT
            (Research · Pipeline · Regulatory
             · Competitive Summary · Outlook)

📋 Course Concepts Demonstrated

1. Multi-Agent Systems (Google ADK)

The root LlmAgent (orchestrator) holds three specialist sub-agents registered via agents=[]. In production ADK mode:

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import MCPToolset, StdioServerParameters

literature_scout = LlmAgent(
    name="literature_scout",
    model="gemini-2.0-flash",
    instruction=SCOUT_SYSTEM_PROMPT,
    tools=[MCPToolset(connection_params=StdioServerParameters(
        command="python", args=["mcp_servers/pubmed_mcp.py"]
    ))],
)

orchestrator = LlmAgent(
    name="orchestrator",
    model="gemini-2.0-flash",
    instruction=ORCHESTRATOR_PROMPT,
    agents=[literature_scout, pipeline_scout, regulatory_watch],
)

Parallel execution uses asyncio.gather, mirroring ADK's built-in agent fan-out. The Kaggle notebook runs all agents as async functions with the same logic — no subprocess overhead.

2. MCP Servers (Model Context Protocol)

Three dedicated MCP servers, each wrapping a public pharma API:

Server	API	Key Tools
`pubmed_mcp.py`	NCBI E-utilities	`search_pubmed`, `fetch_abstract`
`opentargets_mcp.py`	Open Targets Platform (EMBL-EBI) GraphQL	`search_ot_drugs`, `get_drug_details`, `get_disease_drugs`
`fda_mcp.py`	openFDA	`search_drug_approvals`, `search_adverse_events`, `search_drug_labels`

Each server implements list_tools() + call_tool() per the MCP protocol spec with JSON Schema typed inputs/outputs. Agents bind to them via MCPToolset + StdioServerParameters.

3. Security & Guardrails

Four defense layers applied before any query reaches an agent and after any LLM response:

Layer	What it blocks
PHI/PII Detection	SSNs, DOBs, MRNs, emails, phone numbers
Domain Scope Validation	Non-pharma queries (recipes, finance, etc.)
Rate Limiting	Sliding-window, 3 req/sec per API source
Output Sanitization	Redacts PHI from LLM-generated text

🗂 Project Structure

ClinicalEdge/
├── clinicaledge.ipynb          # Kaggle notebook (submit this)
├── main.py                     # CLI entry point
├── requirements.txt
├── agents/
│   ├── orchestrator.py         # Root LlmAgent — entity extraction, fan-out, synthesis
│   ├── literature_scout.py     # PubMed specialist
│   ├── trial_monitor.py        # Open Targets pipeline specialist
│   └── regulatory_watch.py     # openFDA specialist
├── mcp_servers/
│   ├── pubmed_mcp.py           # NCBI E-utilities MCP server
│   ├── opentargets_mcp.py      # Open Targets Platform MCP server
│   └── fda_mcp.py              # openFDA MCP server
├── skills/
│   ├── search_skill.py         # Query building & relevance scoring
│   └── synthesis_skill.py      # Gemini prompting & report parsing
├── security/
│   └── guardrails.py           # PHI, scope, rate-limit, sanitize
└── report/
    └── formatter.py            # Rich / Markdown / JSON output

🚀 Quick Start

Prerequisites

Python 3.10+
A Google AI Studio API key (free)
Optionally: NCBI API key for higher rate limits

Installation

git clone https://github.com/josephardev/ClinicalEdge.git
cd ClinicalEdge
pip install -r requirements.txt
export GOOGLE_API_KEY="your-key-here"
export NCBI_API_KEY="your-ncbi-key"   # optional

Run from CLI

python main.py --query "What is the competitive landscape for KRAS G12C inhibitors in NSCLC?"

Run on Kaggle

Open the live notebook: kaggle.com/code/josephardev/clinicaledge
Enable Internet in Settings → Environment
Add GOOGLE_API_KEY via Add-ons → Secrets (Label: GOOGLE_API_KEY)
Run All — completes in under 90 seconds

📊 Demo Output

Query: "What is the competitive landscape for KRAS G12C inhibitors in non-small cell lung cancer as of 2024?"

══════════════════════════════════════════════════════════════════════
  ClinicalEdge — Pharma Competitive Intelligence Agent
══════════════════════════════════════════════════════════════════════
  Query: What is the competitive landscape for KRAS G12C inhibitors
         in non-small cell lung cancer as of 2024?
  Generated: July 04, 2026

  Data: 10 articles | 15 trials | 4 FDA records

──────────────────────────────────────────────────────────────────────
  SECTION 1 — RESEARCH LANDSCAPE
──────────────────────────────────────────────────────────────────────
  PubMed yielded 10 high-relevance articles (2020–2024). Landmark
  papers include the CodeBreaK 100 trial (sotorasib, ORR 37.1%,
  PMID:34096690) and KRYSTAL-1 (adagrasib, ORR 42.9%, PMID:35658005).
  Recent publications focus on resistance mechanisms (Y96D, G13D
  mutations) and combination strategies targeting KRAS+SHP2,
  KRAS+EGFR, and KRAS+PD-1 axes...

──────────────────────────────────────────────────────────────────────
  SECTION 2 — CLINICAL PIPELINE
──────────────────────────────────────────────────────────────────────
  15 trials identified. Phase distribution: {PHASE3: 6, PHASE2: 5,
  PHASE1: 4}. Top sponsors: Amgen, Mirati Therapeutics, BMS, Roche,
  Revolution Medicines. Active combinations: sotorasib+panitumumab
  (NCT04117087), adagrasib+pembrolizumab (NCT04613596)...

──────────────────────────────────────────────────────────────────────
  SECTION 3 — REGULATORY STATUS
──────────────────────────────────────────────────────────────────────
  Sotorasib (NDA213756, Lumakras): FDA full approval May 2021 —
  first-in-class KRAS G12C inhibitor for 2L+ NSCLC. Adagrasib
  (NDA216340, Krazati): Accelerated approval December 2022. Both
  carry hepatotoxicity monitoring requirements (boxed warnings)...

──────────────────────────────────────────────────────────────────────
  SECTION 4 — COMPETITIVE SUMMARY
──────────────────────────────────────────────────────────────────────
  Amgen (Lumakras) and Mirati/BMS (Krazati) lead. Both are covalent,
  GDP-state selective inhibitors. Second-generation: divarasib
  (Roche) shows improved CNS penetration; glecirasib advancing in
  Phase 3. Key differentiation: combination regimens, CNS activity,
  AE profile, co-mutation tolerance (STK11, KEAP1)...

──────────────────────────────────────────────────────────────────────
  SECTION 5 — STRATEGIC OUTLOOK
──────────────────────────────────────────────────────────────────────
  Priority gaps: first-line approval (both agents currently 2L+),
  post-progression options after resistance. Resistance via Y96D and
  G13D mutations remains the central durability challenge.
  Opportunity: biomarker-driven patient selection and combination
  agents to overcome resistance and extend benefit...

══════════════════════════════════════════════════════════════════════
  Total pipeline time: 47.3s | Under 90s target: YES
══════════════════════════════════════════════════════════════════════

⚠️ Limitations

Limitation	Impact	Mitigation
3 req/sec API rate limit	~15s overhead	NCBI API key → 10/sec
PubMed abstracts only	Misses full-text data	Add PMC full-text API
Regex entity extraction	May miss novel targets	Replace with NER model
No financial data	Missing market share	Add SEC EDGAR MCP

🛣 Future Improvements

ChEMBL MCP — IC50, selectivity, structural similarity
SEC EDGAR MCP — 10-K pipeline disclosures and R&D spend
Web Search MCP — News, press releases, conference abstracts
Persistent memory — Track evolving landscapes across query sessions
NER-based entity extraction — Replace regex with a fine-tuned biomedical NER model

🔒 Security Notes

No API keys in code — all secrets via os.getenv() or Kaggle Secrets
PHI/PII blocked at the guardrail layer before any API call
Rate limiting prevents accidental API abuse

📄 License

MIT License — see LICENSE

Built by @josephardev for the AI Agents: Intensive Vibe Coding Capstone — 5-Day Google AI Agents Course, July 2026.

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Repository files navigation

⚕ ClinicalEdge — Pharma Competitive Intelligence Agent

🎯 Problem Statement

🏗 Architecture

📋 Course Concepts Demonstrated

1. Multi-Agent Systems (Google ADK)

2. MCP Servers (Model Context Protocol)

3. Security & Guardrails

🗂 Project Structure

🚀 Quick Start

Prerequisites

Installation

Run from CLI

Run on Kaggle

📊 Demo Output

⚠️ Limitations

🛣 Future Improvements

🔒 Security Notes

📄 License

About

Releases

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Name		Name	Last commit message	Last commit date
Latest commit History 5 Commits
agents		agents
mcp_servers		mcp_servers
report		report
security		security
skills		skills
LICENSE		LICENSE
README.md		README.md
clinicaledge.ipynb		clinicaledge.ipynb
main.py		main.py
requirements.txt		requirements.txt
writeup.md		writeup.md

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⚕ ClinicalEdge — Pharma Competitive Intelligence Agent

🎯 Problem Statement

🏗 Architecture

📋 Course Concepts Demonstrated

1. Multi-Agent Systems (Google ADK)

2. MCP Servers (Model Context Protocol)

3. Security & Guardrails

🗂 Project Structure

🚀 Quick Start

Prerequisites

Installation

Run from CLI

Run on Kaggle

📊 Demo Output

⚠️ Limitations

🛣 Future Improvements

🔒 Security Notes

📄 License

About

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Watchers

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Releases

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