ClinicalTrials Sponsor Intelligence: Pharma Pipeline Tracking with FDA Approval Cross-Reference

Tracking a pharma company’s pipeline from ClinicalTrials.gov requires filtering by sponsor, pulling phase and status data per trial, and then separately checking FDA drug approval records to see which programs advanced to market. The ClinicalTrials Sponsor Intel actor handles all of this in a single run.

What makes it different from a bulk export

The ClinicalTrials Bulk Exporter gives you raw trial records filtered by condition or phase. The Sponsor Intel actor is purpose-built for competitive pipeline work. It queries by lead sponsor, optionally cross-references each completed trial against the FDA drug approval database, and flags which interventions subsequently received regulatory clearance.

Output fields

Each record returns:

• nct_id — ClinicalTrials.gov identifier
• title — study title
• phase — trial phase
• overall_status — COMPLETED, TERMINATED, RECRUITING, etc.
• start_date and completion_date
• results_first_posted — when the sponsor posted results (if at all)
• lead_sponsor — lead sponsor organization name
• fda_approved — true/false (when crossRefFDA is enabled)
• fda_approval_date — approval date if found

Python example

import requests, time

run = requests.post(
    "https://api.apify.com/v2/acts/themineworks~clinicaltrials-sponsor-intelligence/runs",
    headers={"Authorization": f"Bearer {APIFY_TOKEN}"},
    json={
        "sponsor": "Pfizer",
        "condition": "oncology",
        "status": "COMPLETED,TERMINATED",
        "crossRefFDA": True,
        "maxResults": 100
    }
)

run_id = run.json()["data"]["id"]

while True:
    r = requests.get(
        f"https://api.apify.com/v2/actor-runs/{run_id}",
        headers={"Authorization": f"Bearer {APIFY_TOKEN}"}
    )
    if r.json()["data"]["status"] in ("SUCCEEDED", "FAILED"):
        break
    time.sleep(5)

trials = requests.get(
    f"https://api.apify.com/v2/actor-runs/{run_id}/dataset/items",
    headers={"Authorization": f"Bearer {APIFY_TOKEN}"}
).json()

approved = [t for t in trials if t.get("fda_approved")]
print(f"{len(approved)} of {len(trials)} trials led to FDA approval")

Parameters

Parameter	Default	Notes
sponsor	required	Lead sponsor name (partial match supported)
condition	—	Narrow to a specific indication
status	COMPLETED,TERMINATED	Comma-separated status values
crossRefFDA	true	Cross-reference against FDA approval database
maxResults	100	Maximum trial records to return

Pipeline success rate analysis

import pandas as pd

df = pd.DataFrame(trials)
df["completion_date"] = pd.to_datetime(df["completion_date"], errors="coerce")

# Approval rate by phase
by_phase = df.groupby("phase")["fda_approved"].agg(["sum", "count"])
by_phase["approval_rate"] = by_phase["sum"] / by_phase["count"]
print(by_phase)

# Time from completion to approval
approved_df = df[df["fda_approved"] == True].copy()
approved_df["approval_lag_days"] = (
    pd.to_datetime(approved_df["fda_approval_date"]) - approved_df["completion_date"]
).dt.days
print(approved_df["approval_lag_days"].describe())

Use cases

Competitive intelligence. Map a competitor’s full pipeline by phase, indication, and success rate. Identify which programs are in Phase 3 and how many have converted to approvals historically.

Due diligence. Before licensing or acquiring a drug candidate, pull the sponsor’s full trial history with FDA outcomes to benchmark their regulatory track record.

Market entry analysis. Identify indications where multiple sponsors have completed trials but FDA approval rates are low. This signals opportunity or signals a hard indication depending on the reason.

Investor research. Track pipeline depth and clinical success rates across a sponsor portfolio. A sponsor with 40 completed trials and 3 approvals tells a different story than one with 10 trials and 4 approvals.

Pricing

$0.008 per trial. The FDA cross-reference step is included in that price. Empty runs are not charged.

Available on Apify.