# Biotech Catalyst Monitor - Clinical Trial Readouts & Signals (`datasignalslab/biotech-catalyst-monitor`) Actor Track upcoming clinical trial readouts, phase transitions and results from ClinicalTrials.gov, scored by stock-moving impact. A self-serve catalyst signal for biotech investors and traders. - **URL**: https://apify.com/datasignalslab/biotech-catalyst-monitor.md - **Developed by:** [DataSignals Lab](https://apify.com/datasignalslab) (community) - **Categories:** AI, Agents, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $200.00 / 1,000 company analyzeds 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.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 ## Biotech Catalyst Monitor - Clinical Trial Readouts & Signals **See the trial events that often move biotech stocks, before they happen.** Give one or more company names and get their clinical trials from ClinicalTrials.gov, ranked by potential market impact: upcoming Phase 3 readouts, results just posted, and phase transitions. Not a raw trial dump - a curated catalyst dataset. > A single Phase 2 or Phase 3 readout can move a biotech stock 20-80% in a day. This Actor surfaces those catalysts per company, scored and ranked. **Real example (from live data):** *Moderna - top catalyst: a Phase 3 trial, active, with primary completion 2026-06-30, scored impact 100 (an upcoming readout).* Highest-impact events are ranked first. ### What makes it different Most clinical-trial scrapers return raw study records. This one computes the signal: - **Catalyst classification** - upcoming readout, results posted, completed-awaiting-results, or status change. - **Impact score (0-100)** - later phases and near-term readouts rank highest (Phase 3 readout > Phase 1 status note). - **Per-company ranking** - the trials most likely to move the stock, first. - **Verifiable** - every catalyst includes a direct link to the study on ClinicalTrials.gov. ### Who it's for - **Biotech investors and traders** - a ready-made trial-catalyst dataset for research and monitoring. - **Fintech and research apps** - drop trial catalysts into your product. - **Pharma competitive-intelligence teams** - track a competitor's pipeline and readout timing. Pairs with our SEC insider, 13F and 8-K actors for a full market-catalyst suite. ### Input | Field | Type | Description | |---|---|---| | `sponsors` | array | Company / sponsor names (e.g. Moderna, Vertex Pharmaceuticals, BioNTech). | | `maxStudies` | integer | Trials to scan and rank per company. | | `minImpact` | integer | Only return catalysts at or above this impact score (e.g. 70 = high-impact only). | ### Output One item per company: the company query, trials found, a high_impact_count, and a ranked list of catalysts. Each catalyst has the NCT id, title, phase, status, primary completion date, the catalyst type, an impact score, and a direct study URL. ### How it works Reads trials from the official **ClinicalTrials.gov v2 API** (free, public, no key, JSON). It matches the sponsor field, falls back to full-text when needed, then classifies and scores each trial. Clean, stable, fast - no scraping fragility. ### Pricing **Pay-per-event: per company analyzed.** Scan a 10-company watchlist = 10 charges. You pay only for what you scan. ### FAQ **Which companies can I track?** Any sponsor in ClinicalTrials.gov. Use the company name; the Actor matches the sponsor field and falls back to full-text. **How current is it?** ClinicalTrials.gov is the official U.S. registry, updated continuously; each run reads the latest data. --- *Keywords: clinical trials, biotech catalyst, trial readout, PDUFA, phase 3 results, ClinicalTrials.gov API, biotech investing, catalyst calendar, drug pipeline, pharma intelligence, biotech catalyst data.* --- **Note:** Provided as data for research, screening and monitoring - not investment advice. Historical patterns do not guarantee future results. # Actor input Schema ## `sponsors` (type: `array`): One or more biotech/pharma company names (trial sponsors), e.g. Moderna, Vertex Pharmaceuticals, BioNTech. Each is scanned for stock-moving trial catalysts. ## `maxStudies` (type: `integer`): How many of the company's trials to scan and rank. ## `minImpact` (type: `integer`): Only return catalysts at or above this impact score (e.g. 70 = high-impact: Phase 3 readouts, results). 0 = all. ## Actor input object example ```json { "sponsors": [ "Moderna", "Vertex Pharmaceuticals" ], "maxStudies": 50, "minImpact": 0 } ```` # 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 = { "sponsors": [ "Moderna", "Vertex Pharmaceuticals" ] }; // Run the Actor and wait for it to finish const run = await client.actor("datasignalslab/biotech-catalyst-monitor").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 = { "sponsors": [ "Moderna", "Vertex Pharmaceuticals", ] } # Run the Actor and wait for it to finish run = client.actor("datasignalslab/biotech-catalyst-monitor").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 '{ "sponsors": [ "Moderna", "Vertex Pharmaceuticals" ] }' | apify call datasignalslab/biotech-catalyst-monitor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=datasignalslab/biotech-catalyst-monitor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Biotech Catalyst Monitor - Clinical Trial Readouts & Signals", "description": "Track upcoming clinical trial readouts, phase transitions and results from ClinicalTrials.gov, scored by stock-moving impact. A self-serve catalyst signal for biotech investors and traders.", "version": "0.1", "x-build-id": "3tkHQXf81ySODX0SV" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/datasignalslab~biotech-catalyst-monitor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-datasignalslab-biotech-catalyst-monitor", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/datasignalslab~biotech-catalyst-monitor/runs": { "post": { "operationId": "runs-sync-datasignalslab-biotech-catalyst-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/datasignalslab~biotech-catalyst-monitor/run-sync": { "post": { "operationId": "run-sync-datasignalslab-biotech-catalyst-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "sponsors" ], "properties": { "sponsors": { "title": "Companies / trial sponsors", "type": "array", "description": "One or more biotech/pharma company names (trial sponsors), e.g. Moderna, Vertex Pharmaceuticals, BioNTech. Each is scanned for stock-moving trial catalysts.", "items": { "type": "string" } }, "maxStudies": { "title": "Max studies per company", "minimum": 1, "maximum": 100, "type": "integer", "description": "How many of the company's trials to scan and rank.", "default": 50 }, "minImpact": { "title": "Minimum impact score (0-100)", "minimum": 0, "maximum": 100, "type": "integer", "description": "Only return catalysts at or above this impact score (e.g. 70 = high-impact: Phase 3 readouts, results). 0 = all.", "default": 0 } } }, "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 } } } } } } } } } } ```