On Friday February 5th FDA posted a new document titled “The Generic Dashboard Review – January 2016” It says that it is the “first publication” of this analysis, inferring that there will be further issues in the future but not saying how often or when. It states that this analysis is part of OGD's ongoing improvements to “transparency and communication” efforts.
For those who may be unfamiliar with English language business slang, a “dashboard” is a document that conveys important status information at a single glance. The analogy is presumably to that of a car instrument panel, usually called the dashboard, which is supposed to provide all the necessary information about the current status of the car at a single glance.
The aim of the Generic Review Dashboard seems to be to show the status of pending ANDAs (and Prior Approval Supplements) in term of where they are, that is waiting for FDA action or with the sponsor waiting for sponsor action. The idea of this snapshot of OGD workload at a glance is very good. It clearly shows where all the ANDAs that are filed but not finally approved are.
The Current Submission Status Snapshot” shows status for pre-GDUFA Year 3 applications and separately for all unapproved applications. These snapshots are as of January 1, 2016. They show that pre-GDUFA Year 3 there are a total of 3,470 ANDAs still waiting for final approval and for all pending applications that number is 4,105. What this analysis does not show is how long applications have been pending, something that is of great interest to industry. Numerous sources state that the current MEDIAN approval time is 42 months.
The Activity and Review Communications Tracking shows the month-by-month detail of Agency actions for calendar year 2015. This is probably of lesser interest to most but dose show the various types of communication. Finally there is a list of definitions of Key Terms which is helpful.
The dashboard document can be found at the link below url, and I recommend that you look at it to see what FDA generic drug workload looks likes.