Clinical Trials API - Normalized ClinicalTrials.gov Data

Clean, normalized clinical trials straight from ClinicalTrials.gov. Search by condition, drug, sponsor, location, status, or phase - or pull specific NCT IDs - and get analysis-ready trial records: parsed eligibility, normalized phase and status, sponsors, interventions, outcomes, and flattened study sites with geo coordinates.

ClinicalTrials.gov's raw v2 API hands back a deeply nested study object - 13 protocol modules, eligibility stored as one free-text blob, and a locations array that can exceed 1,000 sites for a single trial. Most "clinical trial scrapers" dump that raw mess on you. This Actor does the hard part: it parses the eligibility free text into inclusion and exclusion lists, converts age strings to numeric years, flattens and dedupes study sites and countries, and pulls phase, status, sponsor, enrollment, and outcomes into one tidy schema - so you get usable rows, not a JSON swamp.

✅ What you get / ❌ what this isn't

This Actor gives you	This Actor is not
One normalized row per trial: status, phase, sponsor, enrollment, dates	Not a raw nested study JSON dump
Eligibility parsed into inclusion / exclusion lists + numeric age in years	Not medical advice or trial recommendations
Flattened, deduped study sites with city, country, status, and lat/lng	Not affiliated with or endorsed by ClinicalTrials.gov or the NIH
Search by condition, drug, sponsor, location, status, and phase	Not a source of patient-level or unpublished data
Direct fetch by NCT ID for known trials	Not a guarantee every field is populated by every sponsor
Reliable official-API reads (no anti-bot, no proxy needed)	Not a substitute for reading the full protocol record

🔎 Why use this Actor

• Pull every recruiting trial for a condition or drug in seconds, already normalized.
• Build site-selection and competitive-intelligence tables across sponsors and phases.
• Power patient-recruitment search by condition plus geography with clean site rows.
• Skip the plumbing - eligibility parsing, age conversion, and site flattening are done.
• Feed clean trial rows into dashboards, newsletters, models, or LLM pipelines.

🗂️ What data you get

One row per trial:

Field	Type	Description
nctId, url	string	Trial identifier and its ClinicalTrials.gov page
briefTitle, officialTitle	string	Public and official study titles
overallStatus	string	Recruitment status (RECRUITING, COMPLETED...)
phase, phases	string / array	Normalized trial phase
studyType	string	INTERVENTIONAL or OBSERVATIONAL
conditions, keywords	array	Conditions studied and indexing keywords
interventions, interventionNames	array	Drug/device/procedure arms
leadSponsor, leadSponsorClass, collaborators	string / array	Who runs and funds the trial
enrollmentCount, enrollmentType	number / string	Target or actual enrollment
startDate, primaryCompletionDate, completionDate	string	Key milestone dates
firstPostedDate, lastUpdatePostedDate	string	Registry posting dates
sex, minimumAge, maximumAge	string	Raw eligibility fields
minimumAgeYears, maximumAgeYears	number	Age parsed to numeric years
healthyVolunteers, stdAges	string / array	Volunteer policy and age groups
inclusionCriteria, exclusionCriteria	array	Eligibility parsed from free text
primaryOutcomes, secondaryOutcomeCount	array / number	Outcome measures and time frames
briefSummary	string	Plain-language study summary
numLocations, numCountries, countries	number / array	Site footprint
locations	array	Flattened sites: facility, city, state, country, status, lat/lng

👥 Who it's for

• Pharma and biotech competitive-intelligence and portfolio teams.
• CROs and site-selection teams mapping where trials run and enroll.
• Patient-recruitment companies searching by condition and geography.
• Biotech investors and equity researchers tracking pipelines.
• Academic researchers, systematic reviewers, and data teams feeding clean trial rows downstream.

Example tasks

• Find recruiting breast cancer trials
• Track a sponsor's trial pipeline
• Find trials for a drug near a location

⚙️ How to get clinical trial data

1. Open the Actor on Apify.
2. Enter a condition, searchTerm, intervention, sponsor, or location - or paste specific nctIds.
3. Optionally filter by recruitment status and phases.
4. Set how many trials to return (maxItems) and the per-trial site cap (maxLocationsPerTrial).
5. Run the Actor.
6. Open the Clinical trials dataset view.
7. Export JSON, CSV, Excel, HTML, or XML, or call the Actor through the Apify API.

📥 Input

{
  "condition": "breast cancer",
  "status": ["RECRUITING"],
  "phases": ["2", "3"],
  "maxItems": 200,
  "maxLocationsPerTrial": 50
}

📤 Output

{
  "recordType": "trial",
  "nctId": "NCT05774678",
  "url": "https://clinicaltrials.gov/study/NCT05774678",
  "briefTitle": "Trial Of PreoperAtive Radiation (TOPAz)",
  "overallStatus": "RECRUITING",
  "phase": "PHASE3",
  "studyType": "INTERVENTIONAL",
  "conditions": ["Breast Cancer"],
  "leadSponsor": "M.D. Anderson Cancer Center",
  "enrollmentCount": 126,
  "startDate": "2023-08-01",
  "primaryCompletionDate": "2026-12-31",
  "sex": "FEMALE",
  "minimumAgeYears": 18.0,
  "inclusionCriteria": ["Age 18 years or older"],
  "exclusionCriteria": ["Patients undergoing treatment for recurrent breast cancer"],
  "numLocations": 1,
  "numCountries": 1,
  "countries": ["United States"],
  "locations": [
    {"facility": "M D Anderson Cancer Center", "city": "Houston", "state": "Texas", "country": "United States", "status": "RECRUITING", "lat": 29.76328, "lng": -95.36327}
  ]
}

💵 How much does it cost?

The launch price is about $4.00 / 1,000 trials, tier-discounted for higher Apify plans. One trial record is one charged result. A search returning 200 trials is 200 results.

🔁 Run it on the Apify platform

Schedule recurring refreshes to track new and updated trials, call it from the Apify API, export to CSV/JSON/Excel, or connect the dataset to Make, Zapier, webhooks, a warehouse, or an LLM pipeline.

⚠️ Limits and caveats

• This Actor reads ClinicalTrials.gov's public v2 API. It is not affiliated with ClinicalTrials.gov or the NIH and provides no medical advice.
• Eligibility parsing is best-effort. Inclusion/exclusion lists are split from a free-text blob; unusual sponsor formatting may merge or miss a bullet. The raw fields are always preserved.
• Site lists can be huge. Some trials register 1,000+ sites; numLocations always reports the full count, while locations embeds up to maxLocationsPerTrial rows.
• Fields a sponsor did not provide are returned as null or empty arrays rather than guessed.
• Data reflects what sponsors have registered, which can lag actual trial activity.

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❓ FAQ

How do I search for trials?

Fill any of condition, searchTerm, intervention, sponsor, or location, and optionally filter by status and phases. To pull known trials directly, paste their nctIds instead.

Where does the data come from?

ClinicalTrials.gov's official v2 REST API - the same registry the NIH publishes. It is free, public, and authoritative.

Why are some fields null or empty?

Because the sponsor did not register that field for that trial. Observational studies, expanded-access records, and older entries populate fewer fields than a large interventional trial.

How is age normalized?

The registry stores age as text like 18 Years or 6 Months. This Actor keeps the raw strings and adds minimumAgeYears / maximumAgeYears as numeric years (6 months becomes 0.5) so you can filter and compare.

Does it need a proxy?

No. ClinicalTrials.gov is a public API served from a CDN and works without a proxy. Enable Apify Proxy only on very large batches.

🛠️ Support

For bugs or missing fields, open an Actor issue with the run URL, the search or NCT ID, and the field or behavior you expected.

⭐ Rate this Actor

If this Actor saved you time, please take 30 seconds to leave a review on the Reviews tab of Clinical Trials API - Normalized ClinicalTrials.gov Data - reviews are the main trust signal other users see, and they directly decide which features get built next. If something is broken or a field is missing, please open an issue first - we typically respond within a day and would love the chance to fix it before you rate.