THIS RESOURCE IS NO LONGER IN SERVICE. Documented on March 12,2025. The AIDSinfo Drug Database provides fact sheets on HIV/AIDS related drugs. The fact sheets describe the drug''s use, pharmacology, side effects, and other information. The database includes: -Approved and investigational HIV/AIDS related drugs -Three versions of each fact sheet: patient, health professional, and Spanish. AIDSinfo is a 100% federally funded U.S. Department of Health and Human Services (DHHS) project that offers the latest federally approved information on HIV/AIDS clinical research, treatment and prevention, and medical practice guidelines for people living with HIV/AIDS, their families and friends, health care providers, scientists, and researchers. Sponsors: -National Institutes of Health (NIH) Office of AIDS Research National Institute of Allergy and Infectious Diseases (NIAID) National Library of Medicine (NLM) -Health Resources and Services Administration (HRSA) -Centers for Disease Control and Prevention (CDC) -Centers for Medicare and Medicaid Services (CMS)

The Drug Product Database (DPD) system captures information on Canadian human, veterinary and disinfectant products approved for use by Health Canada. To facilitate the use of the drug product data, multiple Drug Product files are available. Users can access the complete data set through the “Drug Product” file. Subsets of the data can be accessed in the “Drug Product By …” files. The data in these files are filtered based on the current drug product status. For example, only drug product data for Approved products will be found in the “Drug Product By Approved Status” file.

The Database is a research and analysis tool developed at the University of Washington, in the Department of Pharmaceutics. It contains in vitro and in vivo information on drug interactions in humans from the following sources: * 9648 peer-reviewed journal articles referenced in PubMed * 102 New Drug Applications (NDAs) * 411 excerpts of FDA Prescribing Information * In-depth analyses of drug-drug interactions in the context of 40 diseases / co-morbidities. In addition, the database also provides PK Profiles of drugs, QT Prolongation data, including results of TQT studies from recent NDAs, as well as Regulatory Guidances and Editorial Summaries/Syntheses relevant to advances in the field of drug interactions. Access to the Database is licensed by UW Center for Commercialization (C4C) to organizations interested in in-depth information on drug interactions. The Database is particularly useful to scientists/clinicians working in drug discovery and drug development. Database users can search for information using several families of pre-formulated queries based on drug name, enzyme name, transporter name, therapeutic area, and more.

Gene expression data from published journal articles that test hypotheses relevant to neuroscience of addiction and addictive behavior. Data types include effects of particular drug, strain, or knock out on particular gene, in particular anatomical region. Focuses on gene expression data and exposes data from investigations using DNA microarrays, polymerase chain reaction, immunohistochemistry and in-situ hybridizations. Data are available for query through NIF interface.Data submissions are welcome.

The dataset provides data on suspected side-effects also known as suspected adverse drug reactions for authorised medicines in the European Economic Area (EEA).

For centrally authorised medicines, access to reports is granted both by the name of the medicine or the name of the active substance. For non-centrally authorised medicines, access is granted based on the name of the active substance only.

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 dataset contains the Marketing Status part to build the Drugs@FDA database. Drugs at FDA is a database that provides information of drug (generic) name, active ingredient, form and strength available, FDA application number, label info, dosage form or route, marketing status and pharmaceutical company as well as patient information, approval letters, review and other facts for drugs approved after 1997.

A dataset containing drug response profiles for over 600 compounds across multiple cancer cell lines.

The Medicaid by Drug dataset presents information on spending for covered outpatient drugs prescribed to beneficiaries enrolled in Medicaid by physicians and other healthcare professionals. The dataset focuses on average spending per dosage unit and change in average spending per dosage unit over time. Units refer to the drug unit in the lowest dispensable amount. It also includes spending information for manufacturer(s) of the drugs as well as consumer-friendly information of drug uses and clinical indications. Drug spending metrics for Medicaid represent the total amount reimbursed by both Medicaid and non-Medicaid entities to pharmacies for the drug. Medicaid drug spending contains both the Federal and State reimbursement and is inclusive of any applicable dispensing fees. In addition, this total is not reduced or affected by Medicaid rebates paid to the states.

The NLM Drug Information Portal gives users a gateway to selected drug information from the U.S. National Library of Medicine and other key U.S. Government agencies. At the top of the page are links to individual resources with potential drug information, including summaries tailored to various audiences. Resources include the NLM search systems useful in searching for a drug, NLM research resources, resources organized by audience and class, and other NIH and government resources such as FDA and CDC. The search box in the middle of the page lets you search many of these resources simultaneously. More than 34,000 drugs can be searched using this facility. The portal covers drugs from the time they are entered into clinical trials (Clinicaltrials.gov) through their entry in the U.S. market place (Drugs@FDA). Many drugs in other countries are covered, but not as thoroughly as U.S. drugs. The PubMed link provides medical literature describing research, and TOXLINE provides toxicology literature. Resources such as MedlinePlus provide easy to read summaries of the uses and efficacy of a drug. You may search by a drug's trade name or generic name. For example, the trade name Advil and the generic name ibuprofen will retrieve the same drug record. As you type in a name, suggestions are given beneath the search box. A spell checker gives suggestions if the name is not found. You can find embedded portions of names by using an asterisk at the beginning and/or end of a search term. You can also search by the general Category of usage of a drug by checking that radio button. Suggestions are given as you type here too. Once a drug is found, a summary of the drug's type and usage is given, as well as links leading to further information at one of the portal's resources. Outside links open in a new window. Within a given drug record, you may click on the drug category and retrieve drugs with the same or similar uses. * View drug category descriptions. * View top By Name searches (previous seven days). * View top By Category searches (previous seven days). * View top dispensed prescriptions in the US Market, 2010. * View common drug name list. * View category name list. * View list of resources searched. JavaScript must be enabled in your browser for the NLM Drug Information Portal to work properly.

Our highly-targeted consumer healthcare database includes:

🗸 Name 🗸 Postal Address, Email Address, Telephone Number 🗸 Age, Gender 🗸 Most likely to ask a Doctor About an Advertised Prescription Medicine 🗸 Most likely looked for Medical Information on the Web 🗸 Most Likely to Prefer Brand Name Medicines 🗸 Most Likely to Buy Prescriptions through the Mail

The dataset is available for purchase by US region: 🗸 New England (Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, and Vermont) 🗸 Middle Atlantic (New Jersey, New York, and Pennsylvania) 🗸 East North Central (Illinois, Indiana, Michigan, Ohio, and Wisconsin) 🗸 West North Central (Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, and South Dakota) 🗸 South Atlantic (Delaware; Florida; Georgia; Maryland; North Carolina; South Carolina; Virginia; Washington, D.C. and West Virginia) 🗸 East South Central (Alabama, Kentucky, Mississippi, and Tennessee) 🗸 West South Central (Arkansas, Louisiana, Oklahoma, and Texas) 🗸 Mountain (Arizona, Colorado, Idaho, Montana, Nevada, New Mexico, Utah, and Wyoming) 🗸 Pacific (Alaska, California, Hawaii, Oregon, and Washington)

This dataset is comprised of data submitted to HCAI by prescription drug manufacturers for wholesale acquisition cost (WAC) increases that exceed the statutorily-mandated WAC increase threshold of an increase of more than 16% above the WAC of the drug product on December 31 of the calendar year three years prior to the current calendar year. This threshold applies to prescription drug products with a WAC greater than $40 for a course of therapy. Required WAC increase reports are to be submitted to HCAI within a month after the end of the quarter in which the WAC increase went into effect. Please see the statute and regulations for additional information regarding reporting thresholds and report due dates.

Key data elements in this dataset include the National Drug Code (NDC) maintained by the FDA, narrative descriptions of the reasons for the increase in WAC, and the five-year history of WAC increases for the NDC. A WAC Increase Report consists of 27 data elements that have been divided into two separate Excel data sets: Prescription Drug WAC Increase and Prescription Drug WAC Increase – 5 Year History. The datasets include manufacturer WAC Increase Reports received since January 1, 2019. The Prescription Drugs WAC Increase dataset consists of the information submitted by prescription drug manufacturers that pertains to the current WAC increase of a given report, including the amount of the current increase, the WAC after increase, and the effective date of the increase. The Prescription Drugs WAC Increase – 5 Year History dataset consists of the information submitted by prescription drug manufacturers for the data elements that comprise the 5-year history of WAC increases of a given report, including the amount of each increase and their effective dates.

There are 2 types of WAC Increase datasets below: Monthly and Annual. The Monthly datasets include the data in completed reports submitted by manufacturers for calendar year 2025, as of July 8, 2025. The Annual datasets include data in completed reports submitted by manufacturers for the specified year. The datasets may include reports that do not meet the specified minimum thresholds for reporting.

The Quick Guide explaining how to link the information in each data set to form complete reports is here: https://hcai.ca.gov/wp-content/uploads/2024/03/QuickGuide_LinkingTheDatasets.pdf

The program regulations are available here: https://hcai.ca.gov/wp-content/uploads/2024/03/CTRx-Regulations-Text.pdf

The data format and file specifications are available here: https://hcai.ca.gov/wp-content/uploads/2024/03/Format-and-File-Specifications-version-2.0-ada.pdf

DATA NOTES: Due to recent changes in Excel, it is not recommended that you save these files to .csv format. If you do, when importing back into Excel the leading zeros in the NDC number column will be dropped. If you need to save it into a different format other than .xlsx it must be .txt

DATA UPDATES: Annual datasets of reports from the preceding year are reviewed in the second half of the current year to identify if any revisions or additions have been made since the original release of the datasets. If revisions or additions have been found, an update of the datasets will be released. Datasets will be clearly marked with 'Updated' in their titles for convenient identification. Not all datasets may require an updated release. The review of previously released datasets will only be conducted once to determine if an updated release is necessary. Datasets with revisions or additions that may have been made after the one-time review can be requested. These requests should be sent via email to ctrx@hcai.ca.gov. Due to regulatory changes that went into effect April 1, 2024, reports submitted prior to April 1, 2024, will include the data field "Unit Sales Volume in US" and reports submitted on or after April 1, 2024, will instead include "Total Volume of Gross Sales in US Dollars".

Bioinformatics and cheminformatics database that combines detailed drug (i.e. chemical, pharmacological and pharmaceutical) data with comprehensive drug target (i.e. sequence, structure, and pathway) information.

PHMSA regulations in 49 CFR Part 199 require operators of pipelines, liquefied natural gas plants, and underground natural gas storage facilities to submit data to the DOT DAMIS. Operators with less than 50 covered employees are required to submit once every three years, not annually.

FooDrugs database is a development done by the Computational Biology Group at IMDEA Food Institute (Madrid, Spain), in the context of the Food Nutrition Security Cloud (FNS-Cloud) project. Food Nutrition Security Cloud (FNS-Cloud) has received funding from the European Union's Horizon 2020 Research and Innovation programme (H2020-EU.3.2.2.3. – A sustainable and competitive agri-food industry) under Grant Agreement No. 863059 – www.fns-cloud.eu (See more details about FNS-Cloud below)

FooDrugs stores information extracted from transcriptomics and text documents for foo-drug interactiosn and it is part of a demonstrator to be done in the FNS-Cloud project. The database was built using MySQL, an open source relational database management system. FooDrugs host information for a total of 161 transcriptomics GEO series with 585 conditions for food or bioactive compounds. Each condition is defined as a food/biocomponent per time point, per concentration, per cell line, primary culture or biopsy per study. FooDrugs includes information about a bipartite network with 510 nodes and their similarity scores (tau score; https://clue.io/connectopedia/connectivity_scores) related with possible drug interactions with drugs assayed in conectivity map (https://www.broadinstitute.org/connectivity-map-cmap). The information is stored in eight tables:

Table “study” : This table contains basic information about study identifiers from GEO, pubmed or platform, study type, title and abstract

Table “sample”: This table contains basic information about the different experiments in a study, like the identifier of the sample, treatment, origin type, time point or concentration.

Table “misc_study”: This table contains additional information about different attributes of the study.

Table “misc_sample”: This table contains additional information about different attributes of the sample.

Table “cmap”: This table contains information about 70895 nodes, compromising drugs, foods or bioactives, overexpressed and knockdown genes (see section 3.4). The information includes cell line, compound and perturbation type.

Table “cmap_foodrugs”: This table contains information about the tau score (see section 3.4) that relates food with drugs or genes and the node identifier in the FooDrugs network.

Table “topTable”: This table contains information about 150 over and underexpressed genes from each GEO study condition, used to calculate the tau score (see section 3.4). The information stored is the logarithmic fold change, average expression, t-statistic, p-value, adjusted p-value and if the gene is up or downregulated.

Table “nodes”: This table stores the information about the identification of the sample and the node in the bipartite network connecting the tables “sample”, “cmap_foodrugs” and “topTable”.

In addition, FooDrugs database stores a total of 6422 food/drug interactions from 2849 text documents, obtained from three different sources: 2312 documents from PubMed, 285 from DrugBank, and 252 from drugs.com. These documents describe potential interactions between 1464 food/bioactive compounds and 3009 drugs. The information is stored in two tables:

Table “texts”: This table contains all the documents with its identifiers where interactions have been identified with strategy described in section 4.

Table “TM_interactions”: This table contains information about interaction identifiers, the food and drug entities, and the start and the end positions of the context for the interaction in the document.

FNS-Cloud will overcome fragmentation problems by integrating existing FNS data, which is essential for high-end, pan-European FNS research, addressing FNS, diet, health, and consumer behaviours as well as on sustainable agriculture and the bio-economy. Current fragmented FNS resources not only result in knowledge gaps that inhibit public health and agricultural policy, and the food industry from developing effective solutions, making production sustainable and consumption healthier, but also do not enable exploitation of FNS knowledge for the benefit of European citizens. FNS-Cloud will, through three Demonstrators; Agri-Food, Nutrition & Lifestyle and NCDs & the Microbiome to facilitate: (1) Analyses of regional and country-specific differences in diet including nutrition, (epi)genetics, microbiota, consumer behaviours, culture and lifestyle and their effects on health (obesity, NCDs, ethnic and traditional foods), which are essential for public health and agri-food and health policies; (2) Improved understanding agricultural differences within Europe and what these means in terms of creating a sustainable, resilient food systems for healthy diets; and (3) Clear definitions of boundaries and how these affect the compositions of foods and consumer choices and, ultimately, personal and public health in the future. Long-term sustainability of the FNS-Cloud will be based on Services that have the capacity to link with new resources and enable cross-talk amongst them; access to FNS-Cloud data will be open access, underpinned by FAIR principles (findable, accessible, interoperable and re-useable). FNS-Cloud will work closely with the proposed Food, Nutrition and Health Research Infrastructure (FNHRI) as well as METROFOOD-RI and other existing ESFRI RIs (e.g. ELIXIR, ECRIN) in which several FNS-Cloud Beneficiaries are involved directly. (https://cordis.europa.eu/project/id/863059)

***** changes between version FooDrugs_v2 and FooDrugs_V3 (31st January 2023) are:

Increased the amount of text documents by 85.675 from PubMed and ClinicalTrials.gov, and the amount of Text Mining interactions by 168.826.

Increased the amount of transcriptomic studies by 32 GEO series.

Removed all rows in table cmap_foodrugs representing interactions with values of tau=0

Removed 43 GEO series that after manually checking didn't correspond to food compounds.

Added a new column to the table texts: citation to hold the citation of the text.

Added these columns to the table study: contributor to contain the authors of the study, publication_date to store the date of publication of the study in GEO and pubmed_id to reference the publication associated with the study if any.

Added a new column to topTable to hold the top 150 up-regulated and 150 down-regulated genes.

Dec 13, 2014. Drug testing database: ClinicalTrials.gov ―24640 found for 'drug testing' (Search Date: Dec 13, 2014). More data will be added to this figshare article in the future. Analysis will be disclosed in any or all of these sites: figshare, disruptedphysician.com, chaoticpharmacology.com, or a peer-review medical journal. This database complies with terms and conditions of ClinicalTrials.gov: http://clinicaltrials.gov/ct2/about-site/terms-conditions

The Monthly Prescription Drug Plan Formulary and Pharmacy Network Information files contain formulary and pharmacy network data for Medicare Prescription Drug Plans and Medicare Advantage (MA) Prescription Drug Plans (with the exception of employer and Program of All-Inclusive Care for the Elderly plans).

Notice: CMS has identified an issue that resulted in a 15% coinsurance for plans with Defined Standard benefits to be listed rather than a 25% coinsurance in the Beneficiary Cost File under certain scenarios. This issue affected the October 2023 to November 2024 data. CMS will re-post the corrected data in batches between now and May 2025. The following files have already been corrected and re-posted:

October 2024
November 2024

These non-identifiable files are available on a monthly basis and are comprised of the following tables:

Plan Information - Information such as plan name, contract ID, plan ID, service area, and plan type.
Geographic Locator - MA and Prescription Drug Plans region codes and county codes.
Basic Drugs Formulary - Formulary details for each plan including National Drug Codes (NDCs), cost share tier level, and indicators for step therapy, quantity limits, and prior authorization.
Excluded Drugs Formulary - Enhanced alternative plans may elect to provide a supplemental benefit and cover excluded drugs. File includes formulary details for excluded drugs that are covered by the plan (for enhanced alternative plans only).
Beneficiary Cost - Plan level cost sharing details for preferred, non-preferred, and mail order network pharmacies.
Pharmacy Network - National Provider Identifier (NPI) numbers for each network pharmacy including preferred, retail, and mail order indicators.
Indication Based Coverage Formulary File - Includes drugs covered based on FDA-approved indication for each plan.
Insulin Beneficiary Cost File - Plan level cost sharing details for insulin at preferred, non-preferred and mail order network pharmacies.

These are large files and can take time to download.

Please read the “Agreement for Use” in the Resources section below. This document contains important information regarding timeframes for obtaining data as well as data accuracy and integrity.

The Quarterly Prescription Drug Plan Formulary, Pharmacy Network, and Pricing Information is also available to access for the quarterly level information.

Please note: The Part D benefit year information for plans become available in October of the year prior. For example, year 2024 data is available in the October, November and December 2023 monthly files. Year 2024 data continues to be available in the January through September 2024 monthly files, then beginning in October of 2024 year 2025 data becomes available.

Estimated release dates for upcoming 2025 monthly data (files reflect data for the month in which the file is released):

2/26/25
3/26/25
4/23/25
5/21/25
6/18/25
7/30/25
8/27/25
9/24/25

Files older than contract year 2019 can be purchased.

Frequency, in relation to the total number of dispensed drugs, of the 10 most common drugs involved in potential D-interactions.

• Melatonin receptor type 1B was previously repurposed as a target for diabetes treatment.# Information was retrieved from OMIM.Information of the 12 targets and 58 drugs repurposed for treating diabetes based on “omics” data mining.

The Computational Photography Project for Pill Identification (C3PI) was sunset in 2018. No new images will be added to the collection. Identifiers for pills will not be updated. Images and metadata are for research and development purposes only.

The Computational Photography Project for Pill Identification (C3PI) created the RxIMAGE database of freely available high-quality digital images of prescription pills and associated data for use in conducting computer vision research in text- and image-based search and retrieval. Photographs of pills for the RxIMAGE database were taken under laboratory lighting conditions, from a camera directly above the front and the back faces of the pill, at high resolution, and using specialized digital macro-photography techniques. Image segmentation algorithms were then applied to create the JPEG images in the database.

Historical information about the project is available in the NLM archive at https://wayback.archive-it.org/7867/20190423182937/https:/lhncbc.nlm.nih.gov/project/c3pi-computational-photography-project-pill-identification.