# ClinicalTrials.gov Study Scraper (`automation-lab/clinicaltrials-gov-study-scraper`) Actor Monitor ClinicalTrials.gov studies by condition, sponsor, location, status, date window, or NCT ID using the official public API. - **URL**: https://apify.com/automation-lab/clinicaltrials-gov-study-scraper.md - **Developed by:** [Stas Persiianenko](https://apify.com/automation-lab) (community) - **Categories:** Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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 Study Scraper Extract structured clinical trial study records from the official ClinicalTrials.gov v2 API. This actor is built for monitoring public trial data by condition, sponsor, location, recruitment status, update window, or NCT ID. It returns normalized study rows that are ready for BI tools, alerts, competitive intelligence workflows, and research databases. ### What does ClinicalTrials.gov Study Scraper do? ClinicalTrials.gov Study Scraper searches the public ClinicalTrials.gov API and saves one dataset row per study. It can: - ๐Ÿ”Ž Search trials by keyword and medical condition - ๐Ÿข Filter by sponsor, collaborator, city, country, or facility text - ๐Ÿ“Œ Fetch specific trials by NCT ID - ๐Ÿ“… Monitor records updated in a date window - โœ… Filter by recruitment status such as `RECRUITING` or `COMPLETED` - ๐Ÿงพ Include the raw source JSON when you need full auditability ### Who is it for? This scraper is useful for teams that need repeatable access to public clinical trial records. - Pharma competitive intelligence teams tracking rival pipelines - Biotech business development teams watching new studies - CRO and recruitment teams monitoring active recruiting studies - Healthcare market researchers building disease-area datasets - Investors and analysts following sponsor activity - Patient advocacy and nonprofit teams watching trial availability - Data engineers enriching internal trial databases ### Why use this actor? ClinicalTrials.gov is public, but its nested API response is not always convenient for spreadsheet or BI workflows. This actor normalizes core modules into flat fields and preserves arrays for sponsors, conditions, interventions, countries, and locations. It also runs on Apify, so you can schedule it, export results, connect webhooks, or call it from code. ### Data source The actor uses the official ClinicalTrials.gov v2 API. - Source: `https://clinicaltrials.gov/api/v2/studies` - Detail endpoint: `https://clinicaltrials.gov/api/v2/studies/{NCT_ID}` - Authentication: not required for public records - Browser automation: not used - Proxy: not required ### Data fields | Field | Description | | --- | --- | | `nctId` | ClinicalTrials.gov identifier | | `url` | Public study URL | | `briefTitle` | Brief study title | | `officialTitle` | Official study title | | `overallStatus` | Recruitment / study status | | `studyType` | Interventional, observational, etc. | | `phases` | Trial phase values | | `conditions` | Conditions or diseases | | `interventions` | Intervention type and name | | `leadSponsor` | Lead sponsor name | | `collaborators` | Collaborator names | | `enrollmentCount` | Enrollment count when available | | `startDate` | Start date | | `primaryCompletionDate` | Primary completion date | | `completionDate` | Completion date | | `lastUpdatePostDate` | Last update posted date | | `countries` | Countries from facility records | | `locations` | Facility, city, state, country strings | | `eligibilityCriteria` | Eligibility text | | `scrapedAt` | Extraction timestamp | ### How much does it cost to scrape ClinicalTrials.gov studies? The actor uses pay-per-event pricing. You pay a small start fee for each run and a per-study fee for each dataset row saved. The default input is intentionally small so first runs are inexpensive. ### How to use 1. Open the actor on Apify. 2. Enter one or more search filters. 3. Set `maxItems` to the number of studies you need. 4. Run the actor. 5. Download the dataset as JSON, CSV, Excel, XML, or via API. ### Example searches Use cases include: - Recruiting breast cancer trials in the United States - Studies sponsored by Pfizer or Novartis - Trials updated this month for Alzheimer disease - A fixed watchlist of NCT IDs - Completed Phase 3 studies in a therapeutic area ### Input options #### Search terms Use `searchTerms` for free-text search terms. Multiple terms are combined with AND. #### Condition Use `condition` for disease or condition filtering. #### Sponsor Use `sponsor` to find studies connected to a company, university, hospital, or agency. #### Location Use `location` for country, state, city, or facility text. #### Recruitment statuses Use ClinicalTrials.gov status values such as: - `RECRUITING` - `NOT_YET_RECRUITING` - `ACTIVE_NOT_RECRUITING` - `COMPLETED` - `TERMINATED` - `WITHDRAWN` #### NCT IDs Use `nctIds` when you already know the studies you want to enrich. #### Last update dates Use `lastUpdateFrom` and `lastUpdateTo` to monitor newly updated records. #### Advanced query Use `advancedQuery` for ClinicalTrials.gov expressions such as date ranges or field-specific clauses. ### Output example ```json { "nctId": "NCT05162118", "url": "https://clinicaltrials.gov/study/NCT05162118", "briefTitle": "Example Study Title", "overallStatus": "RECRUITING", "conditions": ["Breast Cancer"], "leadSponsor": "Example Sponsor", "countries": ["United States"], "scrapedAt": "2026-06-26T00:00:00.000Z" } ```` ### Tips for best results - Start with a specific condition and status. - Use sponsor filtering for competitive intelligence. - Use direct NCT ID mode for enrichment pipelines. - Use a date window for scheduled monitoring. - Keep `includeRaw` off unless you need the full source object. ### Scheduling and monitoring You can schedule this actor to run daily, weekly, or monthly. Common monitoring workflows: - Daily newly updated recruiting trials - Weekly sponsor pipeline changes - Monthly condition landscape exports - NCT watchlist enrichment ### Integrations Apify datasets and webhooks make the actor useful in automated workflows. You can send results to: - Google Sheets - Snowflake - BigQuery - Airtable - Slack alerts - Email digests - Internal dashboards - Research databases ### API usage #### Node.js ```js import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: process.env.APIFY_TOKEN }); const run = await client.actor('automation-lab/clinicaltrials-gov-study-scraper').call({ condition: 'breast cancer', overallStatuses: ['RECRUITING'], location: 'United States', maxItems: 100 }); console.log(run.defaultDatasetId); ``` #### Python ```python from apify_client import ApifyClient client = ApifyClient('MY-APIFY-TOKEN') run = client.actor('automation-lab/clinicaltrials-gov-study-scraper').call(run_input={ 'condition': 'breast cancer', 'overallStatuses': ['RECRUITING'], 'location': 'United States', 'maxItems': 100, }) print(run['defaultDatasetId']) ``` #### cURL ```bash curl -X POST 'https://api.apify.com/v2/acts/automation-lab~clinicaltrials-gov-study-scraper/runs?token=MY-APIFY-TOKEN' \ -H 'Content-Type: application/json' \ -d '{"condition":"breast cancer","overallStatuses":["RECRUITING"],"location":"United States","maxItems":100}' ``` ### MCP usage Use this actor from Claude Desktop, Claude Code, or other MCP-compatible tools through Apify MCP. Setup for Claude Code: ```bash claude mcp add --transport http apify "https://mcp.apify.com" ``` Setup for Claude Desktop, Cursor, or VS Code: ```json { "mcpServers": { "apify": { "url": "https://mcp.apify.com" } } } ``` Example prompts: - "Find recruiting breast cancer studies in the United States and summarize sponsors." - "Monitor new or updated Alzheimer trials this week." - "Enrich these NCT IDs and return sponsor, status, and locations." ### Legality and responsible use ### Is scraping ClinicalTrials.gov legal? ClinicalTrials.gov provides public study records through an official API. You should still use the data responsibly, comply with ClinicalTrials.gov terms, respect applicable privacy and research rules, and avoid representing the output as medical advice. ### Troubleshooting #### I got no results Try broadening the condition, removing status filters, or checking spelling. Some combinations are too narrow. #### A known study is missing from search Use the `nctIds` input for direct lookup by NCT ID. #### I need every source field Turn on `includeRaw` to attach the full API study object to each row. ### Related scrapers Explore related Automation Lab actors at: - https://apify.com/automation-lab/ ### FAQ #### Does this actor use a browser? No. It uses the official ClinicalTrials.gov API. #### Does it need a proxy? No proxy is required for normal public API use. #### Can I scrape by sponsor? Yes. Use the `sponsor` input. #### Can I monitor new records? Yes. Schedule the actor and use the last-update date window. #### Can I fetch exact NCT IDs? Yes. Add them to `nctIds`. ### Changelog - `0.1` Initial API-backed ClinicalTrials.gov study extraction. # Actor input Schema ## `searchTerms` (type: `array`): Free-text terms combined with AND in the ClinicalTrials.gov query.term parameter (for example oncology, CAR-T, diabetes). ## `condition` (type: `string`): Condition filter sent to query.cond, such as melanoma, migraine, or Alzheimer's disease. ## `sponsor` (type: `string`): Sponsor or collaborator name sent to query.spons, such as Pfizer, Novartis, or NIH. ## `location` (type: `string`): Country, state, city, or facility text sent to query.locn. ## `overallStatuses` (type: `array`): ClinicalTrials.gov overall status values. Common values: RECRUITING, NOT\_YET\_RECRUITING, ACTIVE\_NOT\_RECRUITING, COMPLETED, TERMINATED. ## `nctIds` (type: `array`): Optional direct lookup list. These studies are fetched by ID before search pagination. ## `lastUpdateFrom` (type: `string`): Optional lower bound for LastUpdatePostDate in YYYY-MM-DD format. ## `lastUpdateTo` (type: `string`): Optional upper bound for LastUpdatePostDate in YYYY-MM-DD format. ## `advancedQuery` (type: `string`): Optional ClinicalTrials.gov advanced expression appended to query.term, e.g. AREA\[StartDate]RANGE\[2025/01/01,MAX]. ## `maxItems` (type: `integer`): Maximum number of normalized study records to save. ## `pageSize` (type: `integer`): ClinicalTrials.gov page size per API request. Leave at 100 unless you need smaller pages. ## `includeRaw` (type: `boolean`): Attach the raw ClinicalTrials.gov study object to each row for audits or custom downstream parsing. ## Actor input object example ```json { "searchTerms": [ "breast cancer" ], "condition": "breast cancer", "location": "United States", "overallStatuses": [ "RECRUITING" ], "nctIds": [ "NCT05162118" ], "maxItems": 20, "pageSize": 100, "includeRaw": false } ``` # Actor output Schema ## `overview` (type: `string`): No description # 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 = { "searchTerms": [ "breast cancer" ], "condition": "breast cancer", "location": "United States", "overallStatuses": [ "RECRUITING" ], "nctIds": [ "NCT05162118" ], "maxItems": 20, "pageSize": 100, "includeRaw": false }; // Run the Actor and wait for it to finish const run = await client.actor("automation-lab/clinicaltrials-gov-study-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`๐Ÿ’พ 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); }); // ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ 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 = { "searchTerms": ["breast cancer"], "condition": "breast cancer", "location": "United States", "overallStatuses": ["RECRUITING"], "nctIds": ["NCT05162118"], "maxItems": 20, "pageSize": 100, "includeRaw": False, } # Run the Actor and wait for it to finish run = client.actor("automation-lab/clinicaltrials-gov-study-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "searchTerms": [ "breast cancer" ], "condition": "breast cancer", "location": "United States", "overallStatuses": [ "RECRUITING" ], "nctIds": [ "NCT05162118" ], "maxItems": 20, "pageSize": 100, "includeRaw": false }' | apify call automation-lab/clinicaltrials-gov-study-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=automation-lab/clinicaltrials-gov-study-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "ClinicalTrials.gov Study Scraper", "description": "Monitor ClinicalTrials.gov studies by condition, sponsor, location, status, date window, or NCT ID using the official public API.", "version": "0.1", "x-build-id": "RcdRSwxJtPOfXLRdb" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/automation-lab~clinicaltrials-gov-study-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-automation-lab-clinicaltrials-gov-study-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/automation-lab~clinicaltrials-gov-study-scraper/runs": { "post": { "operationId": "runs-sync-automation-lab-clinicaltrials-gov-study-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/automation-lab~clinicaltrials-gov-study-scraper/run-sync": { "post": { "operationId": "run-sync-automation-lab-clinicaltrials-gov-study-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": { "searchTerms": { "title": "Search terms", "type": "array", "description": "Free-text terms combined with AND in the ClinicalTrials.gov query.term parameter (for example oncology, CAR-T, diabetes).", "items": { "type": "string" } }, "condition": { "title": "Condition or disease", "type": "string", "description": "Condition filter sent to query.cond, such as melanoma, migraine, or Alzheimer's disease." }, "sponsor": { "title": "Sponsor / collaborator", "type": "string", "description": "Sponsor or collaborator name sent to query.spons, such as Pfizer, Novartis, or NIH." }, "location": { "title": "Location", "type": "string", "description": "Country, state, city, or facility text sent to query.locn." }, "overallStatuses": { "title": "Recruitment statuses", "type": "array", "description": "ClinicalTrials.gov overall status values. Common values: RECRUITING, NOT_YET_RECRUITING, ACTIVE_NOT_RECRUITING, COMPLETED, TERMINATED.", "items": { "type": "string" } }, "nctIds": { "title": "NCT IDs", "type": "array", "description": "Optional direct lookup list. These studies are fetched by ID before search pagination.", "items": { "type": "string" } }, "lastUpdateFrom": { "title": "Last update from", "type": "string", "description": "Optional lower bound for LastUpdatePostDate in YYYY-MM-DD format." }, "lastUpdateTo": { "title": "Last update to", "type": "string", "description": "Optional upper bound for LastUpdatePostDate in YYYY-MM-DD format." }, "advancedQuery": { "title": "Advanced API query", "type": "string", "description": "Optional ClinicalTrials.gov advanced expression appended to query.term, e.g. AREA[StartDate]RANGE[2025/01/01,MAX]." }, "maxItems": { "title": "Maximum studies", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Maximum number of normalized study records to save.", "default": 20 }, "pageSize": { "title": "API page size", "minimum": 1, "maximum": 1000, "type": "integer", "description": "ClinicalTrials.gov page size per API request. Leave at 100 unless you need smaller pages.", "default": 100 }, "includeRaw": { "title": "Include raw source JSON", "type": "boolean", "description": "Attach the raw ClinicalTrials.gov study object to each row for audits or custom downstream parsing.", "default": false } } }, "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 } } } } } } } } } } ```