The CDISC Standard for Exchange of Nonclinical Data (SEND) data standard has created new opportunities for collaborative development of open-source software solutions to facilitate cross-study analyses of toxicology study data. A public–private partnership between BioCelerate and the FDA/Center for Drug Evaluation and Research (CDER) was established in part to develop and publicize novel methods to facilitate cross-study analysis of SEND datasets. As part of this work in collaboration with the Pharmaceutical Users Software Exchange (PHUSE), an R package sendigR has been developed to enable users to construct a relational database from a collection of SEND datasets and then query that database to perform cross-study analyses. The sendigR package also includes an integrated Python package, xptcleaner, which can be used to harmonize the terminology used in SEND datasets by mapping to CDISC controlled terminologies. The sendigR R package is freely available on the comprehensive R Archive Network (CRAN) and at https://github.com/phuse-org/sendigR. An R Shiny web application was included in the R package to enable toxicologists with no coding experience to perform historical control analyses. Experienced R programmers will be able to integrate the package functions into their own custom scripts/packages and potentially contribute improvements to the functionality of sendigR.sendigR reference manual: https://phuse-org.github.io/sendigR/.sendigR R Shiny demo app: https://phuse-org.shinyapps.io/sendigR/.

The CDRH Post-Approval Studies Program encompasses design, tracking, oversight, and review responsibilities for studies mandated as a condition of approval of a premarket approval (PMA) application, protocol development product (PDP) application, or humanitarian device exemption (HDE) application. The program helps ensure that well-designed post-approval studies (PAS) are conducted effectively and efficiently and in the least burdensome manner.

The Adverse Event Reporting System (AERS) is a computerized information database designed to support the FDA's post-marketing safety surveillance program for all approved drug and therapeutic biologic products. The FDA uses AERS to monitor for new adverse events and medication errors that might occur with these marketed products. Reporting of adverse events from the point of care is voluntary in the United States. FDA receives some adverse event and medication error reports directly from health care professionals (such as physicians, pharmacists, nurses and others) and consumers (such as patients, family members, lawyers and others). Healthcare professionals and consumers may also report these events to the products' manufacturers. If a manufacturer receives an adverse event report, it is required to send the report to FDA as specified by regulations. The files listed on this page contain raw data extracted from the AERS database for the indicated time ranges and are not cumulative. Users of these files need to be familiar with creation of relational databases using applications such as ORACLE, Microsoft Office Access, MySQL and IBM DB2 or the use of ASCII files with SAS analytic tools. A simple search of AERS data cannot be performed with these files by persons who are not familiar with creation of relational databases.

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.

Federal regulations require that an assembler who installs one or more certified components of a diagnostic x-ray system submit a report of assembly. This database contains the releasable information submitted including Equipment Location, General Information and Component Information. Note: Data does not include dental system installations.

Medical device manufacturers are required to submit a premarket notification or 510(k) if they intend to introduce a device into commercial distribution for the first time or reintroduce a device that will be significantly changed or modified to the extent that its safety or effectiveness could be affected. This database of releasable 510(k)s can be searched by 510(k) number, applicant, device name or FDA product code. Summaries of safety and effectiveness information is available via the web interface for more recent records.

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.

There are currently four anti-cancer medicinal products approved for a tissue-agnostic indication. This is an indication based on a common biological characteristic rather than the tissue of origin. To date, the regulatory experience with tissue-agnostic approvals is limited. Therefore, we compared decision-making aspects of the first tissue-agnostic approvals between the Food and Drug Administration (FDA), European Medicines Agency (EMA) and Pharmaceuticals and Medical Devices Agency (PMDA). Post-marketing measures (PMMs) related to the tissue-agnostic indication were of specific interest. The main data source was the publicly available review documents. The following data were collected: submission date, approval date, clinical trials and datasets, and PMMs. At the time of data collection, the FDA and PMDA approved pembrolizumab, larotrectinib, and entrectinib for a tissue-agnostic indication, while the EMA approved larotrectinib and entrectinib for a tissue-agnostic indication. There were differences in analysis sets (integrated vs. non-integrated), submission dates and requests for data updates between agencies. All agencies had outstanding issues that needed to be addressed in the post-market setting. For pembrolizumab, larotrectinib and entrectinib, the number of imposed PMMs varied between one and eight, with the FDA requesting the most PMMs compared to the other two agencies. All agencies requested at least one PMM per approval to address the remaining uncertainties related to the tissue-agnostic indication. The FDA and EMA requested data from ongoing and proposed trials, while the PMDA requested data from use-result surveys. Confirmation of benefit in the post-marketing setting is an important aspect of tissue-agnostic approvals, regardless of agency. Nonetheless, each approach to confirm benefit has its inherent limitations. Post-marketing data will be essential for the regulatory and clinical decisions-making of medicinal products with a tissue-agnostic indication.

The 21st Century Cures Act of 2016 includes a provision for the U.S. Food and Drug Administration10.13039/100000038 (FDA) to evaluate the potential use of Real-World Evidence (RWE) to support new indications for use for previously approved drugs, and to satisfy post-approval study requirements. Extracting reliable evidence from Real-World Data (RWD) is often complicated by a lack of treatment randomization, potential intercurrent events, and informative loss to follow-up. Targeted Learning (TL) is a sub-field of statistics that provides a rigorous framework to help address these challenges. The TL Roadmap offers a step-by-step guide to generating valid evidence and assessing its reliability. Following these steps produces an extensive amount of information for assessing whether the study provides reliable scientific evidence, including in support of regulatory decision-making. This article presents two case studies that illustrate the utility of following the roadmap. We used targeted minimum loss-based estimation combined with super learning to estimate causal effects. We also compared these findings with those obtained from an unadjusted analysis, propensity score matching, and inverse probability weighting. Nonparametric sensitivity analyses illuminate how departures from (untestable) causal assumptions affect point estimates and confidence interval bounds that would impact the substantive conclusion drawn from the study. TL’s thorough approach to learning from data provides transparency, allowing trust in RWE to be earned whenever it is warranted.

Amivantamab stands as the pioneering bispecific antibody that targets both EGFR and MET, utilized in the treatment of locally advanced or metastatic non-small cell lung cancer (NSCLC) harboring EGFR ex20ins mutations. Nevertheless, a thorough assessment of its safety characteristics in the real-world remains unknown. The adverse event (AE) reports were collected through a search of the FDA Adverse Event Reporting System (FAERS) database spanning from 2019 Q1 to 2024 Q1, and then disproportionality analysis was utilized. Totally, 9,252,269 AE reports were obtained from the FAERS database, with 893 reports of amivantamab classified as primary suspect AEs. Amivantamab-related AEs were distributed in 23 organ systems, and 87 significant preferred terms (PTs) met the reporting odds ratio criteria. Novel significant AEs were detected, and the median time to onset of amivantamab-associated AEs was 43 days. In subgroup analysis, a higher proportion of patients who were male, over 65 years, and with pneumonitis or pneumonia were reported in the death cases. We also found that AEs may vary between intravenous and subcutaneous administration. This investigation offered novel prospects for monitoring and addressing undesirable medication effects associated with amivantamab, which might improve the clinical medication safety.

This blog post was posted by by Taha A. Kass-Hout, M.D., M.S. on December 15, 2015. It was written by Taha A. Kass-Hout, M.D., M.S. and Elaine Johanson

Context:

Identification of adverse drug reactions (ADRs) during the post-marketing phase is one of the most important goals of drug safety surveillance. Spontaneous reporting systems (SRS) data, which are the mainstay of traditional drug safety surveillance, are used for hypothesis generation and to validate the newer approaches. The publicly available US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) data requires substantial curation before they can be used appropriately, and applying different strategies for data cleaning and normalization can have material impact on analysis results.

Content:

We provide a curated and standardized version of FAERS removing duplicate case records, applying standardized vocabularies with drug names mapped to RxNorm concepts and outcomes mapped to SNOMED-CT concepts, and pre-computed summary statistics about drug-outcome relationships for general consumption. This publicly available resource, along with the source code, will accelerate drug safety research by reducing the amount of time spent performing data management on the source FAERS reports, improving the quality of the underlying data, and enabling standardized analyses using common vocabularies.

Acknowledgements:

Data available from this source.

When using this data, please cite the original publication:

Banda JM, Evans L, Vanguri RS, Tatonetti NP, Ryan PB, Shah NH (2016) A curated and standardized adverse drug event resource to accelerate drug safety research. Scientific Data 3: 160026. http://dx.doi.org/10.1038/sdata.2016.26

Additionally, please cite the Dryad data package:

Banda JM, Evans L, Vanguri RS, Tatonetti NP, Ryan PB, Shah NH (2016) Data from: A curated and standardized adverse drug event resource to accelerate drug safety research. Dryad Digital Repository. http://dx.doi.org/10.5061/dryad.8q0s4

Inspiration:

• This is a large-ish dataset (~4.5 gb uncompressed), so try out your batch processing skills in a Kernel
• What groups of drugs are most risky?
• What medical conditions are most at risk to drug-associated risks?

SUMMARY

DDOD use case to request access historical label revisions of FDA-approved drugs.

WHAT IS A USE CASE?

A “Use Case” is a request that was made by the user community because there were no available datasets that met their particular needs. If this use case is similar to your needs, we ask that you add your own requirements to the specifications section.

The concept of a use case falls within the Demand-Driven Open Data (DDOD) program and gives you a formalized way to identify what data you need. It’s for anyone in industry, research, media, nonprofits or other government agencies. Each request becomes a DDOD use case, so that it can be prioritized and worked on.

Use Cases also provide a wealth of insights about existing alternative datasets and tips for interpreting and manipulating data for specific purposes.

PURPOSE

Drug manufacturers and distributions submit documentation about their products to FDA in Structured Product Labeling (SPL) that is available via openFDA. However, the approved labeling is a "living document" that is updated over time to reflect increased knowledge about the safety and effectiveness of the drug. The real-time nature of the labeling information makes it difficult to track the historical changes to a product's label and indications.

VALUE

The historical context of a product's label and indications is important in understanding how and why product labeling changes and in evaluating pharmaceutical market access.

USE CASE SPECIFICATIONS & SOLUTION

Information about this use cases is maintained in a wiki: http://hhs.ddod.us/wiki/Use_Case_25:_History_for_structured_product_labels

It serves as a knowledge base.

USE CASE DISCUSSION FORUM

All communications between Data Users, DDOD Administrators and Data Owners are logged as discussions within GitHub issues: https://github.com/demand-driven-open-data/ddod-intake/issues/25

It aims to provide complete transparency into the process and ensure the same message gets to all participants.

CASE STATUS

Closed via DailyMed, which has XML files of historical SPLs

This blog post is a cross-post from FDA Voices posted on August 5, 2014

This data package contains all (510(k)) submissions for medical devices, reporting status of post approval studies, Premarket approval (PMA) applications. it lists also all national and international standards recognized by FDA.

There are currently four anti-cancer medicinal products approved for a tissue-agnostic indication. This is an indication based on a common biological characteristic rather than the tissue of origin. To date, the regulatory experience with tissue-agnostic approvals is limited. Therefore, we compared decision-making aspects of the first tissue-agnostic approvals between the Food and Drug Administration (FDA), European Medicines Agency (EMA) and Pharmaceuticals and Medical Devices Agency (PMDA). Post-marketing measures (PMMs) related to the tissue-agnostic indication were of specific interest. The main data source was the publicly available review documents. The following data were collected: submission date, approval date, clinical trials and datasets, and PMMs. At the time of data collection, the FDA and PMDA approved pembrolizumab, larotrectinib, and entrectinib for a tissue-agnostic indication, while the EMA approved larotrectinib and entrectinib for a tissue-agnostic indication. There were differences in analysis sets (integrated vs. non-integrated), submission dates and requests for data updates between agencies. All agencies had outstanding issues that needed to be addressed in the post-market setting. For pembrolizumab, larotrectinib and entrectinib, the number of imposed PMMs varied between one and eight, with the FDA requesting the most PMMs compared to the other two agencies. All agencies requested at least one PMM per approval to address the remaining uncertainties related to the tissue-agnostic indication. The FDA and EMA requested data from ongoing and proposed trials, while the PMDA requested data from use-result surveys. Confirmation of benefit in the post-marketing setting is an important aspect of tissue-agnostic approvals, regardless of agency. Nonetheless, each approach to confirm benefit has its inherent limitations. Post-marketing data will be essential for the regulatory and clinical decisions-making of medicinal products with a tissue-agnostic indication.

FAERS (FDA Adverse Events Reporting System) database is designed to support the FDA’s post-marketing safety surveillance program for drug and therapeutic biologic products. The Drug Therapy Dates file contains drug therapy start dates and end dates for the reported drugs (0 or more per drug per event).

Sterile Products

Compounded sterile products can be produced as a patient specific medication or a non-patient specific medication.

Non-Patient Specific

The following list is composed of facilities both within and outside of the state of Connecticut that have appropriately registered with the state of Connecticut and the Food and Drug Administration. The state of Connecticut does not endorse any of these companies and does not maintain a list of the products that they are permitted to compound and distribute. The companies on this list are permitted to send medication that is not patient specific to a pharmacy or practitioner for dispensing or administration. Please contact the individual business to determine what products they can provide.

The Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) data package contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA in 2021.

Large scale analysis of in vivo toxicology studies has been hindered by the lack of a standardized digital format for data analysis. The SEND standard enables the analysis of data from multiple studies performed by different laboratories. The objective of this work is to develop methods to transform, sort, and analyze data to automate cross study analysis of toxicology studies. Cross study analysis can be applied to use cases such as understanding a single compound’s toxicity profile across all studies performed and/or evaluating on- versus off-target toxicity for multiple compounds intended for the same pharmacological target. This collaborative work between BioCelerate and FDA involved development of data harmonization/transformation strategies and analytic techniques to enable cross-study analysis of both numerical and categorical SEND data. Four de-identified SEND data sets from the BioCelerate Toxicology Data Sharing module of DataCelerate® were used for the analyses. Toxicity prof..., Deidentified SEND data was donated by companies participating in BioCelerate’s Toxicology Data Sharing Initiative (TDS module in DataCelerate®).The data included 1-Month Rat and 1-Month Dog SEND datasets for two different compounds intended for the same pharmacological target. To facilitate cross-study analysis of toxicology studies, it is practical to categorize findings within organ systems to provide insights into target organ toxicity. In the proof-of-concept for this application, we focused on the target organs with compound-related effects, namely the kidney, liver, hematopoietic system, endocrine system, and reproductive tract (male). The body weights (BW), food and water consumption (FW), laboratory test results (LB), organ measurements (OM), and microscopic findings (MI) SEND domains were included in the analysis. Each parameter was then assigned to the relevant organ system(s) (Table 1) based on veterinary literature (Faqi 2017) (Stockham 2008), scientific literature on ..., , # Dataset for Cross Study Analyses of SEND Data: Toxicity Profile Classification

https://doi.org/10.5061/dryad.s1rn8pkgr

The data included 1-Month Rat and 1-Month Dog SEND datasets for two different compounds (Compound A and Compound B) intended for the same pharmacological target.Â

Description of the data and file structure

The files contain data from toxicology studies performed in rats and dogs to support clinical development for two different drugs intended for the same pharmacological target. The studies were donated by the pharmaceutical companies involved in development of the compounds. All proprietary and identifying information has been removed and deidentified. Â

The toxicology data is organized based on the CDISC - Standard for Exchange of Nonclinical Data (SEND) data standard (https://www.cdisc.org/standards/foundational/send/sendig-v3-1) and stored in .json a...,