Background
Regulatory authorities require product post-marketing exposure estimates in aggregate safety reports for purposes of signal detection. The calculation of exposure estimates requires multiple data sources between sales data and aggregated de-identified patient data. Traditionally, the calculations were manually handled due to limited countries, products, and indications. With the growth of both markets and products, manual calculations are inefficient to handle the increasing volume of data and regional requests. An approach to automating the entire process has become essential.

Method
R Shiny App is a web application framework that allows us to create interactive web applications for data analysis, data visualization, and interactive reports, making it easier to share data insights with others. We converted monthly patient and sales report into an app-readable format and applied an exposure estimate algorithm to perform calculations by region, product, and indication.

Results
The time to generate a standard report of post-marketing exposure data for regulatory purposes has been reduced from a week to one day. The QC function of application helps check inconsistent numbers from month to month in sales and patient data. In addition to the exposure report, the visualization provided by the application shows trends in sales, patient utilization of products, and exposure by year and geographic regions. The sharing ability of the application enables other team members to generate customized report directly without any prerequisite training.

Conclusion
The application significantly improves the efficiency of delivering post-marketing exposure data and reduces time and resources needed for data management and QC. Accessibility to the application helps disseminate the information to a broader range of teams and functions.
Feifei Yang | Yu Zhang
AstraZeneca | AstraZeneca
Post-Approval Drug Exposure Estimation Using an R Shiny App
Background Regulatory authorities require product post-marketing exposure estimates in aggregate safety reports for purposes of signal detection. The calculation of exposure estimates requires multiple data sources between sales data and aggregated de-identified patient data. Traditionally, the calculations were manually handled due to limited countries, products, and indications. With the growth of both markets and products, manual calculations are inefficient to handle the increasing volume of data and regional requests. An approach to automating the entire process has become essential.  Method R Shiny App is a web application framework that allows us to create interactive web applications for data analysis, data visualization, and interactive reports, making it easier to share data insights with others. We converted monthly patient and sales report into an app-readable format and applied an exposure estimate algorithm to perform calculations by region, product, and indication.  Results The time to generate a standard report of post-marketing exposure data for regulatory purposes has been reduced from a week to one day. The QC function of application helps check inconsistent numbers from month to month in sales and patient data. In addition to the exposure report, the visualization provided by the application shows trends in sales, patient utilization of products, and exposure by year and geographic regions. The sharing ability of the application enables other team members to generate customized report directly without any prerequisite training.  Conclusion The application significantly improves the efficiency of delivering post-marketing exposure data and reduces time and resources needed for data management and QC. Accessibility to the application helps disseminate the information to a broader range of teams and functions.