# ClinicalTrials.gov Scraper (`foo121/clinical-trials-scraper`) Actor Export clinical trials by condition, status and phase from ClinicalTrials.gov: NCT ID, title, sponsor, phase, enrollment, locations, interventions, dates. For pharma, CRO and research. Pay per result. - **URL**: https://apify.com/foo121/clinical-trials-scraper.md - **Developed by:** [ziv shay](https://apify.com/foo121) (community) - **Categories:** Business - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing $4.00 / 1,000 result items This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. 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.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.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/platform/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 **Clinical trials scraper** for **ClinicalTrials.gov data** — pull structured **clinical trial data by condition**, status, phase, sponsor, and location in one run. Built for **pharma and biotech business development, CROs, and medical researchers** who need a current, structured view of the trial landscape for a disease area or competitor. This **clinical trials scraper** reads the **official ClinicalTrials.gov API v2**, so every study comes straight from the NIH-maintained registry of record. No API key required. Give it a condition (and optionally a drug, sponsor, or keyword) and filter by recruitment status — get back NCT IDs, titles, conditions, phase, study type, lead sponsor, enrollment, dates, and location counts as clean **ClinicalTrials.gov data**. ### What you get | Field | Description | |---|---| | `nctId` | ClinicalTrials.gov registry identifier (NCT number) | | `title` | Study title | | `conditions` | Array of conditions / diseases studied | | `overallStatus` | Overall recruitment status (e.g. RECRUITING, COMPLETED) | | `phase` | Trial phase (e.g. PHASE2, PHASE3) | | `studyType` | Study type (e.g. INTERVENTIONAL, OBSERVATIONAL) | | `leadSponsor` | Lead sponsor organization | | `enrollmentCount` | Target or actual enrollment count | | `startDate` | Study start date | | `completionDate` | Study completion (or estimated completion) date | | `locationCount` | Number of study locations | | `leadLocationCountry` | Country of the primary study location | | `url` | Direct link to the study page on ClinicalTrials.gov | ### Input | Field | Type | Default | Description | |---|---|---|---| | `condition` | string | `"diabetes"` | **Required.** Condition / disease to search | | `terms` | string | — | Optional free-text term (drug, sponsor, keyword) | | `status` | array of enums | `["RECRUITING"]` | One or more of: `RECRUITING`, `NOT_YET_RECRUITING`, `ENROLLING_BY_INVITATION`, `ACTIVE_NOT_RECRUITING`, `SUSPENDED`, `TERMINATED`, `COMPLETED`, `WITHDRAWN` | | `maxItems` | integer | `500` | Max studies to return | #### Example input ```json { "condition": "diabetes", "terms": "semaglutide", "status": ["RECRUITING", "NOT_YET_RECRUITING"], "maxItems": 500 } ```` ### Example run output ```json { "nctId": "NCT05731674", "title": "A Study of Semaglutide in Adults With Type 2 Diabetes and Obesity", "conditions": ["Type 2 Diabetes Mellitus", "Obesity"], "overallStatus": "RECRUITING", "phase": "PHASE3", "studyType": "INTERVENTIONAL", "leadSponsor": "Novo Nordisk A/S", "enrollmentCount": 1240, "startDate": "2023-04-18", "completionDate": "2026-09-30", "locationCount": 86, "leadLocationCountry": "United States", "url": "https://clinicaltrials.gov/study/NCT05731674" } ``` ### Use cases - **Pharma and biotech BD teams** tracking competitor pipelines and recruiting trials in a disease area. - **CROs** sizing the trial landscape and identifying active sponsors for partnership outreach. - **Biotech market intelligence** monitoring phase, enrollment, and status shifts across a condition. - **Medical researchers** assembling a structured dataset of trials by condition for review or analysis. ### vs Citeline / Informa trial-intelligence Citeline and Informa trial-intelligence products are enterprise-priced subscriptions. This **clinical trials scraper** pulls the same **official ClinicalTrials.gov registry data** the registry publishes — pay per result, no subscription. Honest caveat: this is **registry data only**, not Citeline's curated analyst overlay (de-duplicated drug programs, competitive read-throughs, hand-coded outcomes). If you need the analyst layer, that's a paid product. If you need accurate, current registry facts at scale, this is it. ### Pricing Pay per result — billed only for the studies returned, no subscription. ### Keywords clinical trials scraper, ClinicalTrials.gov data, clinical trial data by condition, clinical trials API, trial pipeline data, recruiting clinical trials, clinical trial sponsor data, NCT data scraper, biotech trial intelligence, clinical trial dataset # Actor input Schema ## `condition` (type: `string`): Medical condition or disease to search for (e.g. "diabetes", "breast cancer", "Alzheimer"). ## `terms` (type: `string`): Free-text terms to further filter studies — drug name, sponsor, intervention, keyword, etc. ## `status` (type: `array`): Overall study status(es) to include. ## `maxItems` (type: `integer`): Maximum number of trials to output. ## Actor input object example ```json { "condition": "diabetes", "status": [ "RECRUITING" ], "maxItems": 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" }; // Run the Actor and wait for it to finish const run = await client.actor("foo121/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" } # Run the Actor and wait for it to finish run = client.actor("foo121/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" }' | apify call foo121/clinical-trials-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=foo121/clinical-trials-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "ClinicalTrials.gov Scraper", "description": "Export clinical trials by condition, status and phase from ClinicalTrials.gov: NCT ID, title, sponsor, phase, enrollment, locations, interventions, dates. For pharma, CRO and research. Pay per result.", "version": "1.0", "x-build-id": "ZfAVUPUUfed7aP7bg" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/foo121~clinical-trials-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-foo121-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/foo121~clinical-trials-scraper/runs": { "post": { "operationId": "runs-sync-foo121-clinical-trials-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/foo121~clinical-trials-scraper/run-sync": { "post": { "operationId": "run-sync-foo121-clinical-trials-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "condition" ], "properties": { "condition": { "title": "Condition / disease", "type": "string", "description": "Medical condition or disease to search for (e.g. \"diabetes\", \"breast cancer\", \"Alzheimer\")." }, "terms": { "title": "Other terms (optional)", "type": "string", "description": "Free-text terms to further filter studies — drug name, sponsor, intervention, keyword, etc." }, "status": { "title": "Recruiting status", "type": "array", "description": "Overall study status(es) to include.", "items": { "type": "string", "enum": [ "RECRUITING", "NOT_YET_RECRUITING", "ENROLLING_BY_INVITATION", "ACTIVE_NOT_RECRUITING", "SUSPENDED", "TERMINATED", "COMPLETED", "WITHDRAWN" ], "enumTitles": [ "Recruiting", "Not yet recruiting", "Enrolling by invitation", "Active, not recruiting", "Suspended", "Terminated", "Completed", "Withdrawn" ] }, "default": [ "RECRUITING" ] }, "maxItems": { "title": "Max items", "minimum": 1, "maximum": 50000, "type": "integer", "description": "Maximum number of trials to output.", "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 } } } } } } } } } } ```