# ClinicalTrials.gov Scraper (Trials, Sponsors & Investigators) (`cblu/clinical-trials-scraper`) Actor
Search ClinicalTrials.gov (official NIH API v2): filter by condition, intervention, phase, status, sponsor type, and site location. Flat JSON with sponsor, sites, and named principal investigators with contacts. Pay per study.
- **URL**: https://apify.com/cblu/clinical-trials-scraper.md
- **Developed by:** [Steven Carleton](https://apify.com/cblu) (community)
- **Categories:** Lead generation
- **Stats:** 2 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet
## Pricing
from $3.00 / 1,000 study records
This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage.
Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event
## What's an Apify Actor?
Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".
## How to integrate an Actor?
If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.
In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js/docs.md):
```bash
npm install apify-client
```
In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.md):
```bash
pip install apify-client
```
In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md):
````bash
# MacOS / Linux
curl -fsSL https://apify.com/install-cli.sh | bash
# Windows
irm https://apify.com/install-cli.ps1 | iex
```bash
In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/integrations/mcp.md).
If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md).
For usage examples, see the [API](#api) section below.
For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).
# README
## ClinicalTrials.gov Scraper — Trials, Sponsors, Sites & Investigators API
Search and export **clinical study records from the official ClinicalTrials.gov API** — the US National Library of Medicine's registry of clinical trials worldwide. Get the **NCT number, title, status, phase, study type, enrollment, conditions, interventions, lead sponsor and sponsor class, central contacts (phone + email), overall officials, and every study site with its principal investigators**. Filter by **condition, intervention, sponsor, location, recruitment status, phase, study type, and sponsor class**, then download clean JSON, CSV, or Excel.
This Actor reads the **official ClinicalTrials.gov API v2** (`clinicaltrials.gov/api/v2`) — **no login, no API key** — and returns **flattened, normalized records** instead of the registry's deeply nested JSON. Most "clinical trials" scrapers either dump the raw nested payload or stop at the summary; this one extracts the parts that matter for outreach and intelligence — **sponsor, site facilities, and named principal investigators with their contact details** — into flat fields you can filter and merge.
### What data do you get?
One result per clinical study:
| Field | Example |
|---|---|
| `nctId` / `studyUrl` | `NCT06739122` + direct study link |
| `title` / `officialTitle` / `acronym` | Brief and official titles |
| `status` / `whyStopped` | `RECRUITING`, `COMPLETED`, `TERMINATED`... |
| `studyType` / `phases` | `INTERVENTIONAL` / `["PHASE3"]` |
| `enrollmentCount` / `enrollmentType` | `55` / `ESTIMATED` |
| `conditions` / `interventions` | `["Type 2 Diabetes"]` / drug & device list |
| `leadSponsor` / `leadSponsorClass` | `Eli Lilly and Company` / `INDUSTRY` |
| `collaborators` | Collaborating organizations |
| `centralContacts` | **Name, role, phone, email** for study contacts |
| `overallOfficials` | Study directors / principal investigators |
| `locations` | Every site: facility, city, state, ZIP, country, status, **principal investigators** |
| `eligibility` | Sex, age range, healthy-volunteers flag, age groups |
| `firstPostedDate` / `lastUpdatePostedDate` | Registry dates |
### Use cases
- **Pharma & biotech competitive intelligence** — track every trial a competitor sponsors by condition, phase, and status; monitor the pipeline in your therapeutic area (`sponsorClasses: ["INDUSTRY"]`).
- **Site selection & CRO business development** — find active sites and named principal investigators for a given condition and geography; build investigator target lists with facility contacts.
- **Patient recruitment** — surface `RECRUITING` trials for a condition near a location, with central-contact phone and email for referral.
- **KOL & investigator mapping** — extract principal investigators and study directors across a disease area for medical-affairs and advisory-board outreach.
- **Market & landscape research** — quantify trial activity by sponsor, phase, enrollment, and region for diligence or strategy.
- **AI agents / MCP** — a clean, filterable interface to the global trial registry for agent-driven research and enrichment.
### How to use it
Every recruiting, industry-sponsored cancer trial with a site in Texas:
```json
{
"condition": "cancer",
"state": "Texas",
"statuses": ["RECRUITING"],
"sponsorClasses": ["INDUSTRY"],
"maxResults": 500
}
````
Every Phase 3 diabetes trial currently recruiting:
```json
{
"condition": "diabetes",
"statuses": ["RECRUITING"],
"phases": ["PHASE3"],
"maxResults": 500
}
```
**Filters:** `condition`, `intervention`, `sponsor`, `locationTerm`, `searchTerm`, `statuses` (recruiting, completed, terminated...), `phases` (early phase 1 → phase 4), `studyType`, `sponsorClasses` (industry, NIH, federal...), `state`, `country`, `maxResults`. At least one narrowing criterion is required.
### Data source & freshness
Every run queries the **official ClinicalTrials.gov API v2** live. ClinicalTrials.gov is maintained by the US National Library of Medicine (NIH) and updated continuously as sponsors submit and revise records — you get whatever the registry has at the moment of your call. Because it reads the official structured API rather than scraping a website, it does not break the way HTML scrapers do; the v2 API has been stable since its 2024 launch.
### Pricing
This Actor uses **pay per result**: you are charged **one `study-record` event per study returned**, and only for records actually delivered. Suggested price **$0.003 per record ($3.00 / 1,000 studies)**.
Why this price: clinical-trials competitors on the store either hide their pricing or use clunky multi-event models — one leading incumbent charges a **$0.10 actor-start fee plus $0.01 per search operation**. At **$0.003 per delivered record with no start fee**, this Actor is dramatically cheaper for real extraction while returning richer records (sponsor, every site, and named investigators flattened out). Start low; you only pay for the studies you keep.
### Why this Actor
- **Official NIH/NLM API v2, no key** — no login, no scraping, no anti-bot arms race.
- **Investigators & site contacts extracted** — principal investigators, facilities, and central contacts pulled into flat fields, not buried in nested JSON.
- **Sponsor-class & phase filtering** — isolate industry-sponsored pipelines or a specific phase in one call.
- **Pay only for results**, transparent per-record pricing, JSON/CSV/Excel export, and an MCP-ready interface for AI agents.
# Actor input Schema
## `condition` (type: `string`):
Condition or disease, e.g. diabetes, breast cancer, Alzheimer.
## `intervention` (type: `string`):
Intervention, drug, or device, e.g. semaglutide, immunotherapy.
## `sponsor` (type: `string`):
Lead sponsor or collaborator, e.g. Pfizer, Mayo Clinic.
## `locationTerm` (type: `string`):
Free-text location (city, facility, region), e.g. Boston, MD Anderson.
## `searchTerm` (type: `string`):
General free-text search across the whole study record.
## `statuses` (type: `array`):
Filter by overall recruitment status.
## `phases` (type: `array`):
Filter interventional trials by phase.
## `studyType` (type: `string`):
Interventional, observational, or expanded access.
## `sponsorClasses` (type: `array`):
Filter by lead-sponsor class (e.g. Industry for pharma-sponsored trials).
## `state` (type: `string`):
Full US state name of a study site, e.g. Texas, California.
## `country` (type: `string`):
Country of a study site, e.g. United States.
## `includeRawData` (type: `boolean`):
Attach the complete raw ClinicalTrials.gov study to every record under raw.
## `maxResults` (type: `integer`):
Maximum number of study records to return. You are only charged for records actually returned.
## Actor input object example
```json
{
"condition": "diabetes",
"statuses": [
"RECRUITING"
],
"includeRawData": false,
"maxResults": 500
}
```
# API
You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.
## JavaScript example
```javascript
import { ApifyClient } from 'apify-client';
// Initialize the ApifyClient with your Apify API token
// Replace the '' with your token
const client = new ApifyClient({
token: '',
});
// Prepare Actor input
const input = {
"condition": "diabetes",
"statuses": [
"RECRUITING"
],
"maxResults": 500
};
// Run the Actor and wait for it to finish
const run = await client.actor("cblu/clinical-trials-scraper").call(input);
// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
console.dir(item);
});
// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs
```
## Python example
```python
from apify_client import ApifyClient
# Initialize the ApifyClient with your Apify API token
# Replace '' with your token.
client = ApifyClient("")
# Prepare the Actor input
run_input = {
"condition": "diabetes",
"statuses": ["RECRUITING"],
"maxResults": 500,
}
# Run the Actor and wait for it to finish
run = client.actor("cblu/clinical-trials-scraper").call(run_input=run_input)
# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(item)
# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start
```
## CLI example
```bash
echo '{
"condition": "diabetes",
"statuses": [
"RECRUITING"
],
"maxResults": 500
}' |
apify call cblu/clinical-trials-scraper --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=cblu/clinical-trials-scraper",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "ClinicalTrials.gov Scraper (Trials, Sponsors & Investigators)",
"description": "Search ClinicalTrials.gov (official NIH API v2): filter by condition, intervention, phase, status, sponsor type, and site location. Flat JSON with sponsor, sites, and named principal investigators with contacts. Pay per study.",
"version": "0.1",
"x-build-id": "90eTjPGb0WdPGJBxQ"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/cblu~clinical-trials-scraper/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-cblu-clinical-trials-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/cblu~clinical-trials-scraper/runs": {
"post": {
"operationId": "runs-sync-cblu-clinical-trials-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/cblu~clinical-trials-scraper/run-sync": {
"post": {
"operationId": "run-sync-cblu-clinical-trials-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
}
},
"components": {
"schemas": {
"inputSchema": {
"type": "object",
"properties": {
"condition": {
"title": "Condition / disease",
"type": "string",
"description": "Condition or disease, e.g. diabetes, breast cancer, Alzheimer."
},
"intervention": {
"title": "Intervention / treatment",
"type": "string",
"description": "Intervention, drug, or device, e.g. semaglutide, immunotherapy."
},
"sponsor": {
"title": "Sponsor",
"type": "string",
"description": "Lead sponsor or collaborator, e.g. Pfizer, Mayo Clinic."
},
"locationTerm": {
"title": "Location term",
"type": "string",
"description": "Free-text location (city, facility, region), e.g. Boston, MD Anderson."
},
"searchTerm": {
"title": "Other search terms",
"type": "string",
"description": "General free-text search across the whole study record."
},
"statuses": {
"title": "Recruitment status",
"type": "array",
"description": "Filter by overall recruitment status.",
"items": {
"type": "string",
"enum": [
"RECRUITING",
"NOT_YET_RECRUITING",
"ACTIVE_NOT_RECRUITING",
"COMPLETED",
"ENROLLING_BY_INVITATION",
"SUSPENDED",
"TERMINATED",
"WITHDRAWN",
"UNKNOWN"
],
"enumTitles": [
"Recruiting",
"Not yet recruiting",
"Active, not recruiting",
"Completed",
"Enrolling by invitation",
"Suspended",
"Terminated",
"Withdrawn",
"Unknown"
]
}
},
"phases": {
"title": "Phase",
"type": "array",
"description": "Filter interventional trials by phase.",
"items": {
"type": "string",
"enum": [
"EARLY_PHASE1",
"PHASE1",
"PHASE2",
"PHASE3",
"PHASE4",
"NA"
],
"enumTitles": [
"Early Phase 1",
"Phase 1",
"Phase 2",
"Phase 3",
"Phase 4",
"Not applicable"
]
}
},
"studyType": {
"title": "Study type",
"enum": [
"INTERVENTIONAL",
"OBSERVATIONAL",
"EXPANDED_ACCESS"
],
"type": "string",
"description": "Interventional, observational, or expanded access."
},
"sponsorClasses": {
"title": "Sponsor class",
"type": "array",
"description": "Filter by lead-sponsor class (e.g. Industry for pharma-sponsored trials).",
"items": {
"type": "string",
"enum": [
"NIH",
"FED",
"OTHER_GOV",
"INDIV",
"INDUSTRY",
"NETWORK",
"AMBIG",
"OTHER",
"UNKNOWN"
],
"enumTitles": [
"NIH",
"Federal",
"Other government",
"Individual",
"Industry",
"Network",
"Ambiguous",
"Other",
"Unknown"
]
}
},
"state": {
"title": "US state (site location)",
"type": "string",
"description": "Full US state name of a study site, e.g. Texas, California."
},
"country": {
"title": "Country (site location)",
"type": "string",
"description": "Country of a study site, e.g. United States."
},
"includeRawData": {
"title": "Include raw study record",
"type": "boolean",
"description": "Attach the complete raw ClinicalTrials.gov study to every record under raw.",
"default": false
},
"maxResults": {
"title": "Max results",
"minimum": 1,
"maximum": 100000,
"type": "integer",
"description": "Maximum number of study records to return. You are only charged for records actually returned.",
"default": 500
}
}
},
"runsResponseSchema": {
"type": "object",
"properties": {
"data": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"actId": {
"type": "string"
},
"userId": {
"type": "string"
},
"startedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"finishedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"status": {
"type": "string",
"example": "READY"
},
"meta": {
"type": "object",
"properties": {
"origin": {
"type": "string",
"example": "API"
},
"userAgent": {
"type": "string"
}
}
},
"stats": {
"type": "object",
"properties": {
"inputBodyLen": {
"type": "integer",
"example": 2000
},
"rebootCount": {
"type": "integer",
"example": 0
},
"restartCount": {
"type": "integer",
"example": 0
},
"resurrectCount": {
"type": "integer",
"example": 0
},
"computeUnits": {
"type": "integer",
"example": 0
}
}
},
"options": {
"type": "object",
"properties": {
"build": {
"type": "string",
"example": "latest"
},
"timeoutSecs": {
"type": "integer",
"example": 300
},
"memoryMbytes": {
"type": "integer",
"example": 1024
},
"diskMbytes": {
"type": "integer",
"example": 2048
}
}
},
"buildId": {
"type": "string"
},
"defaultKeyValueStoreId": {
"type": "string"
},
"defaultDatasetId": {
"type": "string"
},
"defaultRequestQueueId": {
"type": "string"
},
"buildNumber": {
"type": "string",
"example": "1.0.0"
},
"containerUrl": {
"type": "string"
},
"usage": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "integer",
"example": 1
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
},
"usageTotalUsd": {
"type": "number",
"example": 0.00005
},
"usageUsd": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "number",
"example": 0.00005
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
}
}
}
}
}
}
}
}
```