# FDA Drug Approval & Action Monitor - Regulatory Catalysts (`datasignalslab/fda-drug-approval-monitor`) Actor FDA drug approval monitor: approvals and regulatory actions from openFDA Drugs@FDA, scored by impact. Self-serve, pay per company. - **URL**: https://apify.com/datasignalslab/fda-drug-approval-monitor.md - **Developed by:** [DataSignals Lab](https://apify.com/datasignalslab) (community) - **Categories:** AI, 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. 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 ## FDA Drug Approval and Action Monitor: Approvals, Indications and Priority Reviews **Turn one or more company names into a clean, impact-scored feed of FDA drug approval events from the official openFDA Drugs@FDA API.** This Actor monitors FDA drug approval activity per company and classifies each regulatory action: new drug approvals, new indications, priority reviews and other supplements. Instead of a raw application dump, you get the catalyst signal: every event is typed, scored 0 to 100 by potential market impact, ranked most-recent-first, and linked back to its Drugs@FDA record. A new drug approval or a priority-review efficacy supplement can re-rate a biotech or pharma stock in a single session. This FDA drug approval monitor surfaces those regulatory actions for the companies on your watchlist in one structured dataset. ### Why this is different Most openFDA scrapers hand you raw Drugs@FDA application records and leave the interpretation to you. This Actor adds a classification and scoring layer on top of the same official data: - **Event classification** - each submission is labeled as a new drug approval (original NDA/BLA), a new indication or efficacy supplement, a labeling supplement, or another regulatory action. - **Priority-review flag** - submissions marked PRIORITY by the FDA are scored above standard reviews. - **Impact score (0 to 100)** - event type plus recency are combined into a single, transparent number so high-signal actions float to the top. - **Most-recent-first ranking** - the latest actions surface first, then highest impact. - **Verifiable** - every event links to its Drugs@FDA record and includes direct FDA document links where available. Be clear on scope: this is a monitor of regulatory **actions already taken** (approvals, supplements, status changes that are now in the public record). It is not a forward-looking PDUFA calendar and does not predict upcoming decision dates. Those target dates are not published in a single official openFDA feed. ### Who it's for - **Biotech and pharma traders and investors** - a ready-made FDA-catalyst dataset for research, screening and monitoring rather than manual searches. - **Fintech and research apps** - drop classified regulatory events directly into your own product or model. - **Competitive-intelligence teams** - track a competitor's approvals, new indications and pipeline actions over time. ### Use cases - **Watchlist monitoring** - scan a list of drug sponsors on a schedule and flag any new high-impact approval or supplement. - **Catalyst research** - pull a company's recent regulatory actions, sorted by impact. - **Screening** - set a minimum impact score to keep only the most material events across many companies. - **Competitive tracking** - follow a rival's new indications and labeling changes as they hit the public record. - **Data enrichment** - feed structured FDA approval events into dashboards or downstream models. ### Input | Field | Type | Required | Description | |---|---|---|---| | `companies` | array | Yes | One or more drug-sponsor company names (for example Pfizer, Vertex, Eli Lilly). Each is scanned in Drugs@FDA. Vaccines and biologics handled by FDA CBER are not in this CDER dataset. | | `maxEvents` | integer | No | How many of the company's most recent regulatory events to classify and rank. Default 25, range 1 to 100. | | `minImpact` | integer | No | Only return events at or above this impact score (for example 70 keeps high-impact events only). Default 0 returns all. Range 0 to 100. | ### Output One dataset item per company. Each item holds the original query, summary counts, and a ranked list of classified events. ```json { "type": "fda_events", "company_query": "Vertex", "events_found": 12, "approvals_count": 4, "high_impact_count": 5, "events": [ { "sponsor": "VERTEX PHARMACEUTICALS INCORPORATED", "application_number": "NDA207925", "brand_name": "TRIKAFTA", "active_ingredient": "ELEXACAFTOR; IVACAFTOR; TEZACAFTOR", "submission_type": "SUPPL", "submission_class": "Efficacy", "review_priority": "PRIORITY", "status": "Approved", "status_date": "2024-02-08", "catalyst": "TRIKAFTA: New indication / efficacy supplement (priority review)", "impact": 80, "url": "https://www.accessdata.fda.gov/scripts/cder/daf/index.cfm?event=overview.process&ApplNo=207925", "docs": ["https://www.accessdata.fda.gov/drugsatfda_docs/..."] } ] } ```` Field notes: `submission_type` is `ORIG` (original application) or `SUPPL` (supplement). `status` is a readable label such as Approved, Tentative approval or Withdrawn. `docs` holds up to three FDA document links. `approvals_count` counts `ORIG` applications; `high_impact_count` counts events scoring 70 or above. ### How the impact score works The score is computed directly in code from event type, review priority, status and recency, then clamped to the 0 to 100 range: - **Base by event type** - original approval (`ORIG`) starts at 80; a new indication or efficacy supplement (efficacy, indication, Type 6 or Type 8 classes) at 55; a labeling supplement at 30; any other supplement or regulatory action at 35. - **Priority review** - adds 10 when the submission is marked PRIORITY. - **Status adjustments** - a tentative approval is reduced by 25 (floored at 15); a withdrawn application is set to 20 and relabeled. - **Recency** - adds 15 if the status date is within the last 365 days, or 8 if within the last 730 days. So a recent original approval under priority review scores near the top, while a routine, older labeling supplement scores low. The full ranking is most-recent-first, then by impact. ### Use with AI agents and automation This Actor is built to be called programmatically, not just clicked. You can run it from agent and automation stacks including: - **AI agent frameworks** - call it as a tool from LangChain or LlamaIndex pipelines. - **Apify MCP server** - expose it to MCP-capable assistants such as Claude, ChatGPT and Cursor so an agent can fetch FDA approval events on demand. - **No-code automation** - trigger it from Zapier or Make and route results into sheets, alerts or CRMs. - **Webhooks and schedules** - run on a fixed schedule or on demand, and post completed datasets to your own webhook endpoint. Output is plain JSON, so it slots into existing data flows. ### Pricing **Pay-per-event, charged per company analyzed.** There is no subscription. Scanning a 10-company watchlist is 10 charges; you pay only for the companies you actually scan. ### Data source and compliance All data comes from the official **openFDA Drugs@FDA API** (api.fda.gov), a free and public US government data source served as JSON with no API key required. The Actor reads only published regulatory records: applications, submissions, statuses and document links. It collects no personal data. Coverage is FDA CDER drug applications in Drugs@FDA (small molecules and many biologics); vaccines and some biologics handled by CBER are out of scope. ### FAQ **What counts as a high-impact event?** A new drug approval (original NDA/BLA) and priority-review efficacy or new-indication supplements score highest; routine labeling supplements score low. High-impact is defined as a score of 70 or above. **Which companies can I track?** Any drug sponsor in Drugs@FDA. Enter the company name; a wildcard fallback on the first word handles legal-name variations, so "Vertex" matches "VERTEX PHARMACEUTICALS INCORPORATED". **How current is the data?** openFDA mirrors the official Drugs@FDA database, and each run reads the latest available data. **Can an AI agent call this automatically?** Yes. The Actor is available through the Apify MCP server, so MCP-capable agents like Claude, ChatGPT and Cursor can invoke it directly, and it also works as a tool in LangChain or LlamaIndex. **Does it predict upcoming PDUFA dates?** No. It reports regulatory actions already taken and recorded in Drugs@FDA. Forward-looking PDUFA target dates are not part of this dataset. **Why did a company return no results?** The name may not match a CDER sponsor in Drugs@FDA, or its products may be CBER vaccines or biologics, which are not in this dataset. Try the registered legal name. *** *Keywords: FDA drug approval, openFDA, Drugs@FDA, drug approvals monitor, regulatory actions, priority review, new drug application, NDA, BLA, new indication, efficacy supplement, pharma regulatory data, biotech catalyst, FDA approval API.* *** **Disclaimer:** Provided as data for research and monitoring, not investment advice. Historical patterns do not guarantee future results. # Actor input Schema ## `companies` (type: `array`): One or more drug-sponsor company names (e.g. Pfizer, Vertex, Eli Lilly). Each is scanned in Drugs@FDA for approvals and regulatory actions. Note: vaccines/biologics handled by FDA CBER are not in this CDER dataset. ## `maxEvents` (type: `integer`): How many of the company's most recent regulatory events to classify and rank. ## `minImpact` (type: `integer`): Only return events at or above this impact score (e.g. 70 = high-impact: new approvals, priority efficacy supplements). 0 = all. ## Actor input object example ```json { "companies": [ "Vertex", "Eli Lilly" ], "maxEvents": 25, "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 = { "companies": [ "Vertex", "Eli Lilly" ] }; // Run the Actor and wait for it to finish const run = await client.actor("datasignalslab/fda-drug-approval-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 = { "companies": [ "Vertex", "Eli Lilly", ] } # Run the Actor and wait for it to finish run = client.actor("datasignalslab/fda-drug-approval-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 '{ "companies": [ "Vertex", "Eli Lilly" ] }' | apify call datasignalslab/fda-drug-approval-monitor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=datasignalslab/fda-drug-approval-monitor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "FDA Drug Approval & Action Monitor - Regulatory Catalysts", "description": "FDA drug approval monitor: approvals and regulatory actions from openFDA Drugs@FDA, scored by impact. Self-serve, pay per company.", "version": "0.1", "x-build-id": "ohfVJqLvmGytjw2Xu" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/datasignalslab~fda-drug-approval-monitor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-datasignalslab-fda-drug-approval-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/datasignalslab~fda-drug-approval-monitor/runs": { "post": { "operationId": "runs-sync-datasignalslab-fda-drug-approval-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/datasignalslab~fda-drug-approval-monitor/run-sync": { "post": { "operationId": "run-sync-datasignalslab-fda-drug-approval-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "companies" ], "properties": { "companies": { "title": "Companies / drug sponsors", "type": "array", "description": "One or more drug-sponsor company names (e.g. Pfizer, Vertex, Eli Lilly). Each is scanned in Drugs@FDA for approvals and regulatory actions. Note: vaccines/biologics handled by FDA CBER are not in this CDER dataset.", "items": { "type": "string" } }, "maxEvents": { "title": "Max events per company", "minimum": 1, "maximum": 100, "type": "integer", "description": "How many of the company's most recent regulatory events to classify and rank.", "default": 25 }, "minImpact": { "title": "Minimum impact score (0-100)", "minimum": 0, "maximum": 100, "type": "integer", "description": "Only return events at or above this impact score (e.g. 70 = high-impact: new approvals, priority efficacy supplements). 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 } } } } } } } } } } ```