# FDA Warning Letters & 483 Inspections API (`nexgendata/fda-warning-letters-483-inspections`) Actor Joined feed of FDA Warning Letters + Form 483 inspection observations โ€” facility-level severity-scored via FEI join. Pharma QA/QC, CMO/CDMO supplier risk, FDA defense counsel. The compliance-grade feed competitors that ship raw HTML scrapes can't match. - **URL**: https://apify.com/nexgendata/fda-warning-letters-483-inspections.md - **Developed by:** [NexGenData](https://apify.com/nexgendata) (community) - **Categories:** Business - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $150.00 / 1,000 compliance actions 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/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 ## ๐Ÿ“‹ FDA Warning Letters + 483 Inspection Observations Tracker **The joined regulatory feed pharma compliance teams actually need.** Every Warning Letter and Form 483 the FDA has issued in the last 5 years, mapped to the same facility via FDA Establishment Identifier (FEI), with a per-facility severity score derived from prior actions. Competing Apify actors ship raw HTML scrapes of a single Warning Letters page; this actor ships the joined CMO/CDMO supplier-risk feed that Verisk SteriQual and 3E sell at five figures a year. The differentiator is the **join** โ€” match Warning Letters on Pfizer's New Jersey plant to the four Form 483s the same plant has received in the last 18 months, and you get a "recurring" severity tag that drives supplier-risk decisions. Raw HTML scrapers can't do this because they only see one side of FDA's enforcement universe. ### ๐Ÿง  What you get | Field | Notes | |---|---| | `type` | `warning_letter` or `form_483` | | `date` | Letter issue date or Form 483 issuance date (ISO-8601) | | `manufacturer` / `facility` | Company Name as published by the FDA | | `fei_number` | FDA Establishment Identifier โ€” canonical facility join key | | `state`, `country` | From the 483 FOIA index (Warning Letters feed has implicit US default) | | `product_type` | `drug` / `device` / `food` / `biologic` / `tobacco` / `veterinary` / `any` โ€” inferred from FDA Issuing Office (Warning Letters) or Establishment Type (Form 483s) | | `issuing_office` | Full FDA office string for Warning Letters (e.g. "Center for Drug Evaluation and Research (CDER)") | | `subject` / `citation_summary` | Subject line of the Warning Letter, or `Form 483 โ€” inspection` | | `citations[]` | List of 21 CFR cites parsed from the Warning Letter HTML (e.g. `21 CFR 211.113`, `21 CFR 820.30(a)`) โ€” Warning Letters only, when the letter URL is resolvable | | `fdc_act_citations[]` | FD&C Act section cites (e.g. `Section 501(a)(2)(B)`) | | `response_deadline_15_working_days` | Boolean โ€” whether the letter requires the standard 15-working-day response | | `fda_division` | The specific FDA division issuing the letter, when parseable from the HTML | | `response_letter_received` / `closeout_letter_issued` | Booleans from the Warning Letters feed | | `full_text_url` | Direct link to the FDA's published letter page (or FOIA index for 483s) | | `establishment_type` | For Form 483s โ€” e.g. "Outsourcing Facility", "Producer of Sterile Drug Products" | | `facility_severity_score` | `{score, label, prior_warning_letters, prior_form_483s}` โ€” `clean` (0 priors), `single-prior` (1), `recurring` (2-3), `systemic` (4+) | ### ๐ŸŽฏ Use cases - **Pharma QA/QC director** โ€” Daily monitoring of CDER Warning Letters against current finished-pharmaceutical CMOs; the severity score surfaces any contractor with a recurring `21 CFR 211.x` posture before audit week. - **CMO/CDMO supplier risk team** โ€” Cross-reference every active supplier's FEI against the joined feed; flag any facility with `severity_label="recurring"` or `"systemic"` for re-qualification or fallback-sourcing. - **FDA defense counsel** โ€” Pull all Warning Letters + 483s for a client facility, sorted by date, in one CSV โ€” replaces the multi-tab spreadsheet that junior associates spend a day building per matter. - **Investment due diligence** โ€” Pre-IPO/M&A: scan the target's manufacturing footprint against the feed; `systemic` severity is a Section 8 closing condition issue. - **Plaintiff law / mass tort intake** โ€” Tie individual product complaints (FAERS / MAUDE) to the issuing facility's regulatory posture for proximate-cause arguments. ### ๐Ÿ“Š Sample input ```json { "manufacturer_name": "", "product_type": "drug", "days_back": 180, "severity_filter": "both", "max_records": 50 } ```` ### ๐Ÿ“Š Sample output ```json { "type": "warning_letter", "date": "2026-04-07", "posted_date": "2026-04-28", "manufacturer": "Lexia LLC", "facility": "Lexia LLC", "state": null, "country": null, "fei_number": null, "product_type": "drug", "issuing_office": "Center for Drug Evaluation and Research (CDER)", "subject": "CGMP/Finished Pharmaceuticals/Adulterated", "citation_summary": "CGMP/Finished Pharmaceuticals/Adulterated", "citations": ["21 CFR 211.22", "21 CFR 211.100", "21 CFR 211.113"], "fdc_act_citations": ["section 501(a)(2)(B) of the FD&C Act"], "response_deadline_15_working_days": true, "fda_division": "Center for Drug Evaluation and Research (CDER)", "response_letter_received": false, "closeout_letter_issued": false, "full_text_url": "https://www.fda.gov/inspections-compliance-enforcement-and-criminal-investigations/warning-letters/lexia-llc-...", "facility_severity_score": { "score": 2, "label": "recurring", "prior_warning_letters": 0, "prior_form_483s": 2 }, "source": "FDA Warning Letters" } ``` And a Form 483 record on the same facility: ```json { "type": "form_483", "date": "2026-04-17", "publish_date": "2026-05-27", "manufacturer": "BPI Labs, LLC", "facility": "BPI Labs, LLC", "state": "Florida", "country": "USA", "fei_number": "3015156709", "product_type": "drug", "establishment_type": "Outsourcing Facility", "record_type": "483", "observation_count": null, "severity": "single-prior", "citation_summary": "Form 483 โ€” Outsourcing Facility inspection", "full_text_url": "https://www.fda.gov/about-fda/office-regulatory-affairs/ora-foia-electronic-reading-room", "facility_severity_score": { "score": 1, "label": "single-prior", "prior_warning_letters": 1, "prior_form_483s": 0 }, "source": "FDA OII FOIA Electronic Reading Room (Form 483 index)" } ``` ### ๐Ÿš€ How to use Run the actor from the Apify console, or programmatically: ```python from apify_client import ApifyClient client = ApifyClient("YOUR_APIFY_TOKEN") run = client.actor("nexgendata/fda-warning-letters-483-inspections").call( run_input={ "manufacturer_name": "Intas", "product_type": "drug", "days_back": 365, "severity_filter": "both", "max_records": 100, } ) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item["date"], item["type"], item["manufacturer"], item["facility_severity_score"]["label"]) ``` cURL: ```bash curl -X POST "https://api.apify.com/v2/acts/nexgendata~fda-warning-letters-483-inspections/runs?token=YOUR_APIFY_TOKEN" \ -H "Content-Type: application/json" \ -d '{"product_type":"drug","days_back":180,"max_records":50}' ``` ### โš–๏ธ Compliance & data sources All sources are published by the U.S. Food and Drug Administration under federal public-disclosure mandate (Federal Food, Drug, and Cosmetic Act ยง 705, 21 U.S.C. ยง 375) and are explicitly machine-readable via the XLSX export endpoints: - **Warning Letters:** `fda.gov/inspections-compliance-enforcement-and-criminal-investigations/compliance-actions-and-activities/warning-letters/datatables-data` โ€” covers ~1,000 letters, ~5-year rolling window. - **Form 483 Inspection Observation Index:** `fda.gov/about-fda/office-regulatory-affairs/ora-foia-electronic-reading-room/download` โ€” OII FOIA Electronic Reading Room export, ~2,000 records, covering 483s + EIRs + closeout reports. All HTTP requests are sent with the polite-bot User-Agent `NexGenData FDA Warning Letters Actor hello@thenextgennexus.com`. No anti-bot measures encountered. No FDA rate limits documented for these XLSX endpoints. The actor downloads each XLSX once per run (no per-record requests against FDA). **Future enhancements (paid add-ons under consideration):** OCR of the underlying Form 483 PDFs to extract per-observation numbers (currently the FOIA index publishes the count of inspections but not the per-observation cite count from each 483 PDF), HTML enrichment of older Warning Letters whose URLs aren't on the recent-letters HTML index, and a Schedule-A export of the facility severity index alone (for supplier-onboarding KYC use). ### ๐Ÿ”— Related Actors โ€” NexGenData FDA / Healthcare Cluster | Actor | What it does | |---|---| | [โš ๏ธ FDA Recall Monitor](https://apify.com/nexgendata/fda-recall-monitor) | Drug + device safety alerts (recalls, withdrawals) โ€” pairs with this actor for full FDA post-market surveillance | | [Federal Register Rules Scraper](https://apify.com/nexgendata/federal-register-rules-scraper) | Daily federal-register rule publications across all agencies โ€” catches FDA proposed-rules that drive future Warning Letter citations | | [NIH Reporter Grants Scraper](https://apify.com/nexgendata/nih-reporter-grants-scraper) | Federal biomedical research funding โ€” counterpart to FDA enforcement for the full healthcare-funding-and-enforcement picture | | [โš•๏ธ Doctor Directory Lead Finder](https://apify.com/nexgendata/doctor-directory-lead-finder) | NPI-registry-backed physician lookups โ€” useful for the pharma-payment + adverse-event analyst stack | ### ๐Ÿท๏ธ About NexGenData NexGenData publishes the deepest set of public-record data actors on Apify โ€” SEC EDGAR forms (NPORT-P, 8-K, Form 4, Form D, 13F, 13D/G, ADV), CISA KEV + EPSS, DOL Form 5500, FDA Warning Letters + 483s + Recalls, FDA Adverse Events (FAERS+MAUDE), FINRA BrokerCheck, EUIPO trademarks, WIPO Patentscope, and more โ€” all priced as pay-per-event so you only pay for the data you actually pull. Visit [apify.com/nexgendata](https://apify.com/nexgendata) for the full catalog. # Actor input Schema ## `manufacturer_name` (type: `string`): Restrict results to compliance actions whose Company Name contains this substring (case-insensitive, legal suffixes like Inc./LLC/Ltd./Corp./GmbH/SRL stripped before matching). Leave blank to pull every action in the window. Example: `Pfizer`, `Intas Pharmaceuticals`, `Foshan`. ## `product_type` (type: `string`): Filter to a specific FDA product category. The category is inferred from the Issuing Office (Warning Letters) or Establishment Type (Form 483s). `drug`=CDER, `device`=CDRH, `food`=Human Foods Program/CFSAN/Food Operations, `biologic`=CBER, `any`=include everything regardless of category. Records whose category cannot be determined are still included in `any` mode. ## `days_back` (type: `integer`): How far back to scan. FDA Warning Letters typically have a 30-90 day lag between issue date and posting date. 180 days is the default for ongoing monitoring; 365 for quarterly compliance reviews; 1825 (5 years) for litigation discovery of historical regulatory posture. The Warning Letters dataset covers ~5 years back; the 483 inspection index covers a similar window. ## `severity_filter` (type: `string`): Restrict to one or both source feeds. `warning_letter_only`=public Warning Letters only (CDER/CDRH/CBER/Foods enforcement, ~1,000 letters available). `483_only`=Form 483 inspection observation issuances only (~2,000 inspections in the FOIA index). `both`=join both streams into one chronologically-sorted facility-attributed feed (default โ€” this is the differentiating output). ## `max_records` (type: `integer`): Hard cap on records pushed to the dataset (and metered for the per-action billing event). Records are returned newest-first by issue/inspection date, so a low cap returns the most recent activity. Each compliance-action record is billed at $0.10. Default 50 = a comfortable monthly compliance review batch. ## Actor input object example ```json { "product_type": "any", "days_back": 180, "severity_filter": "both", "max_records": 50 } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "manufacturer_name": "", "product_type": "any", "days_back": 180, "severity_filter": "both", "max_records": 50 }; // Run the Actor and wait for it to finish const run = await client.actor("nexgendata/fda-warning-letters-483-inspections").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/${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 = { "manufacturer_name": "", "product_type": "any", "days_back": 180, "severity_filter": "both", "max_records": 50, } # Run the Actor and wait for it to finish run = client.actor("nexgendata/fda-warning-letters-483-inspections").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 '{ "manufacturer_name": "", "product_type": "any", "days_back": 180, "severity_filter": "both", "max_records": 50 }' | apify call nexgendata/fda-warning-letters-483-inspections --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=nexgendata/fda-warning-letters-483-inspections", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "FDA Warning Letters & 483 Inspections API", "description": "Joined feed of FDA Warning Letters + Form 483 inspection observations โ€” facility-level severity-scored via FEI join. Pharma QA/QC, CMO/CDMO supplier risk, FDA defense counsel. The compliance-grade feed competitors that ship raw HTML scrapes can't match.", "version": "0.0", "x-build-id": "FjASFsT3h0KwOayOg" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/nexgendata~fda-warning-letters-483-inspections/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-nexgendata-fda-warning-letters-483-inspections", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/nexgendata~fda-warning-letters-483-inspections/runs": { "post": { "operationId": "runs-sync-nexgendata-fda-warning-letters-483-inspections", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/nexgendata~fda-warning-letters-483-inspections/run-sync": { "post": { "operationId": "run-sync-nexgendata-fda-warning-letters-483-inspections", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "manufacturer_name": { "title": "Manufacturer name (optional substring filter)", "type": "string", "description": "Restrict results to compliance actions whose Company Name contains this substring (case-insensitive, legal suffixes like Inc./LLC/Ltd./Corp./GmbH/SRL stripped before matching). Leave blank to pull every action in the window. Example: `Pfizer`, `Intas Pharmaceuticals`, `Foshan`." }, "product_type": { "title": "Product type", "enum": [ "drug", "device", "food", "biologic", "any" ], "type": "string", "description": "Filter to a specific FDA product category. The category is inferred from the Issuing Office (Warning Letters) or Establishment Type (Form 483s). `drug`=CDER, `device`=CDRH, `food`=Human Foods Program/CFSAN/Food Operations, `biologic`=CBER, `any`=include everything regardless of category. Records whose category cannot be determined are still included in `any` mode.", "default": "any" }, "days_back": { "title": "Lookback window (days)", "minimum": 1, "maximum": 1825, "type": "integer", "description": "How far back to scan. FDA Warning Letters typically have a 30-90 day lag between issue date and posting date. 180 days is the default for ongoing monitoring; 365 for quarterly compliance reviews; 1825 (5 years) for litigation discovery of historical regulatory posture. The Warning Letters dataset covers ~5 years back; the 483 inspection index covers a similar window.", "default": 180 }, "severity_filter": { "title": "Action type filter", "enum": [ "warning_letter_only", "483_only", "both" ], "type": "string", "description": "Restrict to one or both source feeds. `warning_letter_only`=public Warning Letters only (CDER/CDRH/CBER/Foods enforcement, ~1,000 letters available). `483_only`=Form 483 inspection observation issuances only (~2,000 inspections in the FOIA index). `both`=join both streams into one chronologically-sorted facility-attributed feed (default โ€” this is the differentiating output).", "default": "both" }, "max_records": { "title": "Max records to return", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Hard cap on records pushed to the dataset (and metered for the per-action billing event). Records are returned newest-first by issue/inspection date, so a low cap returns the most recent activity. Each compliance-action record is billed at $0.10. Default 50 = a comfortable monthly compliance review batch.", "default": 50 } } }, "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 } } } } } } } } } } ```