An inventory of all FDA Datasets

Information about FDA-approved brand name and generic prescription and over-the-counter human drugs and biological therapeutic products. Drugs@FDA includes most of the drug products approved since 1939. The majority of patient information, labels, approval letters, reviews, and other information are available for drug products approved since 1998.

This file contains the data elements used for searching the FDA Online Data Repository including proprietary name, active ingredients, marketing application number or regulatory citation, National Drug Code, and company name.

This data package contains medical datasets that have been used to build the Drugs@FDA. The information is classified by health information, regulatory information and advanced search. This data package contains the necessary technical information (excluding programming scripts) to reproduce the online version of Drugs@FDA. The data package is best to use with a database program.

This database consists of those national and international standards recognized by FDA which manufacturers can declare conformity to and is part of the information the Center can use to make an appropriate decision regarding the clearance or approval of a submission. Information submitted on conformance with such standards will have a direct bearing on safety and effectiveness determinations made during the review of IDEs, HDEs, PMAs, and PDPs. Conformance with recognized consensus standards in and of itself, however, may not always be a sufficient basis for regulatory decisions.

FDA Monoclonal Antibody Regulatory Dataset

About the Dataset

This dataset aggregates comprehensive regulatory documentation and resources from the U.S. Food and Drug Administration (FDA), specifically related to monoclonal antibodies (mAbs). It provides structured access to critical FDA filings, clinical trial documentation, and drug labels, serving as an essential resource for regulatory analysis, clinical research, and AI-driven applications.

Contents

The dataset comprises:

• FDA Documentation

  • New Drug Applications (NDA) submissions and approval summaries.
  • Investigational New Drug (IND) filings, including clinical and preclinical data.
  • International Council for Harmonisation (ICH) guidance documents relevant to monoclonal antibody regulation.

• Clinical Trial Documentation

  • Protocols, study designs, and outcome reports from clinical trials.
  • Regulatory correspondence and approval notices.

• Drug Labels

  • Structured drug labeling information for 180 approved monoclonal antibodies, detailing indications, dosages, adverse reactions, warnings, and clinical pharmacology.

Potential Use Cases

This dataset supports various research and analytical tasks, including:

• Regulatory compliance analysis: Identify key elements and benchmarks for successful FDA approvals.
• Clinical trial design optimization: Inform trial protocols using historical approval data.
• Natural Language Processing (NLP) applications: Enable text classification, information extraction, summarization, and entity recognition tasks.
• Safety and efficacy research: Facilitate comparative analysis of drug labels and clinical outcomes.

Intended Audience

• Regulatory professionals and pharmaceutical industry researchers.
• Biomedical data scientists and informaticians.
• NLP and machine learning practitioners focused on biomedical applications.

Data Format

• All documents and labels are provided in machine-readable PDF format that can be parsed using PyPDF, but some drug labels may be a faxed document in a PDF, which may require OCR to parse via Tesseract.

Acknowledgments

This dataset utilizes publicly available information provided by the FDA and other regulatory bodies.

Citation

If you use this dataset in your research or applications, please provide an appropriate citation referencing this dataset.

The drug labels and other drug-specific information on this Web site represent the most recent drug listing information companies have submitted to the Food and Drug Administration (FDA). (See 21 CFR part 207.) The drug labeling and other information has been reformatted to make it easier to read but its content has neither been altered nor verified by FDA. The drug labeling on this Web site may not be the labeling on currently distributed products or identical to the labeling that is approved. Most OTC drugs are not reviewed and approved by FDA, however they may be marketed if they comply with applicable regulations and policies described in monographs. Drugs marked 'OTC monograph final' or OTC monograph not final' are not checked for conformance to the monograph. Drugs marked 'unapproved medical gas', 'unapproved homeopathic' or 'unapproved drug other' on this Web site have not been evaluated by FDA for safety and efficacy and their labeling has not been approved. In addition, FDA is not aware of scientific evidence to support homeopathy as effective.

Information provided to FDA by manufacturers about current drugs in shortage, resolved shortages, and discontinuations of specific drug products.

The FDA Adverse Event Reporting System (FAERS) is a database that contains information on adverse event and medication error reports submitted to FDA. The database is designed to support the FDA's post-marketing safety surveillance program for drug and therapeutic biologic products.

The FDA Device Dataset by Dataplex provides comprehensive access to over 24 million rows of detailed information, covering 9 key data types essential for anyone involved in the medical device industry. Sourced directly from the U.S. Food and Drug Administration (FDA), this dataset is a critical resource for regulatory compliance, market analysis, and product safety assessment regarding.

Dataset Overview:

This dataset includes data on medical device registrations, approvals, recalls, and adverse events, among other crucial aspects. The dataset is meticulously cleaned and structured to ensure that it meets the needs of researchers, regulatory professionals, and market analysts.

24 Million Rows of Data:

With over 24 million rows, this dataset offers an extensive view of the regulatory landscape for medical devices. It includes data types such as classification, event, enforcement, 510k, registration listings, recall, PMA, UDI, and covid19 serology. This wide range of data types allows users to perform granular analysis on a broad spectrum of device-related topics.

Sourced from the FDA:

All data in this dataset is sourced directly from the FDA, ensuring that it is accurate, up-to-date, and reliable. Regular updates ensure that the dataset remains current, reflecting the latest in device approvals, clearances, and safety reports.

Key Features:

• Comprehensive Coverage: Includes 9 key device data types, such as 510(k) clearances, premarket approvals, device classifications, and adverse event reports.

• Regulatory Compliance: Provides detailed information necessary for tracking compliance with FDA regulations, including device recalls and enforcement actions.

• Market Analysis: Analysts can utilize the dataset to assess market trends, monitor competitor activities, and track the introduction of new devices.

• Product Safety Analysis: Researchers can analyze adverse event reports and device recalls to evaluate the safety and performance of medical devices.

Use Cases: - Regulatory Compliance: Ensure your devices meet FDA standards, monitor compliance trends, and stay informed about regulatory changes.

• Market Research: Identify trends in the medical device market, track new device approvals, and analyze competitive landscapes with up-to-date and historical data.

• Product Safety: Assess the safety and performance of medical devices by examining detailed adverse event reports and recall data.

Data Quality and Reliability:

The FDA Device Dataset prioritizes data quality and reliability. Each record is meticulously sourced from the FDA's official databases, ensuring that the information is both accurate and up-to-date. This makes the dataset a trusted resource for critical applications, where data accuracy is vital.

Integration and Usability:

The dataset is provided in CSV format, making it compatible with most data analysis tools and platforms. Users can easily import, analyze, and utilize the data for various applications, from regulatory reporting to market analysis.

User-Friendly Structure and Metadata:

The data is organized for easy navigation, with clear metadata files included to help users identify relevant records. The dataset is structured by device type, approval and clearance processes, and adverse event reports, allowing for efficient data retrieval and analysis.

Ideal For:

• Regulatory Professionals: Monitor FDA compliance, track regulatory changes, and prepare for audits with comprehensive and up-to-date product data.

• Market Analysts: Conduct detailed research on market trends, assess new device entries, and analyze competitive dynamics with extensive FDA data.

• Healthcare Researchers: Evaluate the safety and efficacy of medical devices product data, identify potential risks, and contribute to improved patient outcomes through detailed analysis.

This dataset is an indispensable resource for anyone involved in the medical device industry, providing the data and insights necessary to drive informed decisions and ensure compliance with FDA regulations.

Key Features

How to use this dataset

1. Data Access: Retrieve the dataset from the provided source or API to access FDA records of product recalls in the United States.

2. Data Exploration: Thoroughly explore the dataset by loading it into your preferred data analysis tool. Familiarize yourself with the columns and their meanings.

3. Filter and Sort: Tailor your analysis by filtering and sorting the data as per your research needs. For example, filter by "product_type" or sort by "report_date" for specific insights.

4. Recall Analysis: Examine the "reason_for_recall" column to understand the reasons behind product recalls. This is crucial for assessing common issues in recalled products.

5. Visualization: Create visualizations, such as graphs and charts, to convey your findings effectively. These can help in identifying trends and patterns in the recall data.

If you find this dataset useful, give it an upvote – it's a small gesture that goes a long way! Thanks for your support. 😄

Comprehensive FDA database containing 2,469 regulations, 69,496 510(k) clearances, 5,308 product codes, and extensive safety data for medical device research

This dataset includes the Premarket Approval (PMA) data which is the Food and Drug Administration (FDA) process of scientific and regulatory review to assess the safety and effectiveness of Class III medical devices. These devices support human life and prevent impairment of human health. Due to the level of risk associated with Class III devices, general and special controls are insufficient to assure the safety of these devices and require a PMA application to obtain marketing clearance.

FDA Orange Book Drug Data

Pharmaceutical drug information with patent and exclusivity details

By Basil Hayek [source]

About this dataset

The dataset consists of three main files: patents.csv, reference.csv, and products.csv. The patents.csv file provides information on patent details such as patent numbers, expiration dates, and patent use codes for pharmaceutical drugs listed in the FDA Orange Book. The reference.csv file serves as a reference guide for the exclusivity codes used in the dataset, providing their corresponding meanings and explanations. Lastly, the products.csv file contains comprehensive information about various pharmaceutical drugs including their ingredients, trade names, applicants (companies or organizations), strengths or concentrations, approval dates from the FDA.

Key columns found within these datasets include:

• Applicant_Full_Name: The full name of the company or organization that submitted the application for a particular drug.
• Product_No: A unique numeric identifier assigned by the FDA to each drug product.
• Patent_Expire_Date_Text: The expiration date of the patent associated with a specific drug.
• Delist_Flag: Indicates whether a particular drug has been delisted by the FDA.
• DF;Route: Represents both dosage form and route of administration for a given drug.
• Strength: Indicates concentration or strength of active ingredients present in a particular drug product.
• RS (Reference Standard): A benchmark against which quality measurements are made for each drug's formulation.
• Exclusivity_Code & code_meaning : Describes various types of exclusivities granted to certain drugs based on factors such as new chemical formulations/routes/dosage forms/strengths/indications/patient populations/etc., orphan status eligibility & pediatric exclusivity extensions etc
• Trade_Name : Brand name/commercial/marketing name given to each individual pharmaceutical/drug product -Appl_Type : Classification type describing different types of applications submitted to the FDA for a drug; Abbreviated New Drug Applications (ANDAs), New Drug Applications (NDAs) etc -TE_Code: Exclusivity code indicating the type of exclusivity granted to a drug

Understanding this dataset can be helpful in examining and analyzing specific drugs, their manufacturers, patent expiration dates, exclusivities granted by regulatory authorities, and other relevant information. Researchers, pharmaceutical companies, regulators, and healthcare professionals can utilize this dataset to gain insights into the landscape of pharmaceutical drugs listed in the FDA Orange Book as of July 2017

How to use the dataset

Introduction:

• Understanding the Dataset Structure:

  • The dataset consists of three CSV files: patents.csv, reference.csv, and products.csv.
  • Each file contains specific information about pharmaceutical drugs such as patent numbers, expiration dates, applicants' details, dosage forms, trade names, strengths, approval dates, and more.
  • It is important to familiarize yourself with the column names and their corresponding descriptions in order to make proper use of the data.

• Exploring Pharmaceutical Drugs:

  • Start by examining products.csv as it contains detailed information about various drugs.
    • Columns such as Trade_Name provide brand names of the drugs.
    • Strength describes their concentrations or dosages.
    • Applicant_Full_Name indicates the companies or organizations that submitted applications for these drugs.

• Identifying Patent Details:

  • Turn your attention to patents.csv for patent-related information for each drug in the dataset.
    • Patent_No provides unique identification numbers associated with each drug's patent(s).
    • Patent_Expire_Date_Text shows expiration dates of these patents.

• Analyzing Exclusivities:

  • Reference.csv serves as an excellent resource for understanding exclusivity codes used within this dataset.
    • Exclusivity_code represents codes indicating different types of granted exclusivities for specific drugs.
    • For example D stands for New Dosing Schedule while NCE refers to New Chemical entitling 5 years of exclusivity rights Which are available in code_meaning (Description Meaning)

• Cross-referencing Information:

  • The dataset provides several shared columns across the files, such as Appl_No, Product_No, and Ingredient.
  • By cross-referencing these columns, you can link related information between different files and gain a comprehensive understanding of specific drugs.

• Filtering and Sorting Data: ...

For a drug product that does not have a dissolution test method in the United States Pharmacopeia (USP), the FDA Dissolution Methods Database provides information on dissolution methods presently recommended by the Division of Bioequivalence, Office of Generic Drugs.

🧬 openFDA Drug Labeling – JSON Dataset

This dataset contains structured drug labeling information (FDA labels) provided by DailyMed and made available through the openFDA Drug Labeling endpoint.

The dataset includes 13 compressed .zip files with drug label records in JSON format. Each record reflects the full label submitted to the FDA, and the structure matches what you would receive from the /drug/label API.

📁 Dataset Contents

• 13 ZIP files
• Each file contains multiple JSON documents representing FDA-approved drug labels
• Data fields include (but are not limited to):
  • drug_interactions
  • warnings
  • indications_and_usage
  • contraindications
  • adverse_reactions
  • dosage_and_administration
  • brand_name, generic_name
  • ...and many others

You will also find the 'Human Drug.xlsx' file included in the dataset, which contains the complete data dictionary for reference.

🔄 Updates

This dataset reflects the most recent version available as of April 9, 2025. According to the source, previous records may be modified in future updates. For accuracy and completeness, all files should be downloaded together.

📚 Sources and More Information

• openFDA Drug Labeling Downloads
• API Documentation
• DailyMed Main Site

⚠️ Disclaimer (Please Read Carefully)

Do not rely on openFDA to make decisions regarding medical care. Always speak to your health provider about the risks and benefits of FDA-regulated products. We may limit or otherwise restrict your access to the API in line with our Terms of Service.

Full terms available here: openFDA Terms of Service

🛠️ Notes for Usage

This dataset is ideal for applications involving: - Drug safety analysis - Drug interaction monitoring - Medical language modeling - Retrieval-augmented generation (RAG) agents - Regulatory and pharmacovigilance systems

You may want to extract and preprocess only relevant fields before vectorizing or feeding them into an AI model for efficiency and performance.

Contains data for FDA recalls from 2009 through the present.

FDA-Approved Drugs & Therapeutics

Exploring Human Drug & Biological Therapies

By Health [source]

About this dataset

This dataset contains a wealth of information about FDA-approved human drugs and biological therapeutic products. Whether you are studying the effects of drugs, exploring new treatment methods, or researching potential side effects, this database holds detailed insights into the approved medicines available to individuals today. From brand names to generic prescriptions to over-the-counter products, you can access a variety of important details such as reviews, labels, approval letters and patient information. Gain a comprehensive understanding of the drug products approved since 1939 to develop safer and more effective treatments for patients going forward

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How to use the dataset

This dataset contains information about nearly all of the FDA-approved brand name and generic prescription drugs, as well as biological therapeutic products. It is important to note that most information is available for drug products approved since 1998, meaning that drugs approved before then may have less comprehensive data associated with them.

To get started using this dataset, you should begin by familiarizing yourself with the available columns in the dataset: - Drug Name--The name of the drug (brand name or generic). - Active Ingredient(s)--A list of active ingredients present in each drug product.
- Dosage form--The physical form and route a patient takes a specific drug product (e.g., tablet taken orally).
- Approval Description--A summary of key features and benefits related to the approval process for each product.

• Route(s) -- The manner or way by which a medication has been formulated to be absorbed or introduced into an organism's system (e.g., oral ingestion, injection).

Next, you will want to understand what type of queries can be run on this data set so that you can effectively search for specific items to analyze within your project goals:

•You can search through column headers/specific terms in order to find information related to your query such as active ingredients, dosage forms or routes used by different products;
•You can use simple comparison operators such as “=”, “<” and “>” to find ranges between certain values; •You can utilize Boolean operators such as “AND” & “OR” within SQL statements in order to combine two conditions together; •You can implement searching feature on multiple columns simultaneously using a combination of LIKE commands coupled with wildcard characters (); •Lastly you can build subqueries upon which more complicated queries are applied depending on your research objectives (these advanced scripts often incorporate functions like SUM(), AVG() etc.)

Research Ideas

• Developing a tool to help patients identify potential interactions between different drugs they are taking by cross-referencing this dataset with the patient's records.
• Developing an AI/machine learning model which evaluates all approved drugs and their effects on disease, helping physicians determine the best treatment options for their patients.
• Building an online marketplace, sponsored by health care organizations or private companies, where customers can compare prices and availability of FDA approved drugs before buying them online or in stores

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Open Database License (ODbL) v1.0 - You are free to: - Share - copy and redistribute the material in any medium or format. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices. - No Derivatives - If you remix, transform, or build upon the material, you may not distribute the modified material. - No additional restrictions - You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Columns

Acknowledgements

If you use this dataset in your ...

This data package contains the details of substances in drugs, biologics, foods and devices registered with a Unique Ingredient Identifier (UNII) through the joint FDA/USP Substance Registration System (SRS). It also contains a list of the names used for each UNII and the changes made to Unique Ingredient Identifiers' (UNIIs) descriptions to the latest update.

This blog post was posted on September 4, 2015 and written by Taha Kass-Hout, M.D., M.S., Roselie A. Bright, Sc.D., M.S., P.M.P. and Ann Ferriter. It is a cross post from FDA Voice.