In line with the general shift towards risk-driven approaches in the quality management world, FDA is now taking steps towards applying those same principles to its own auditing schedule.  At the end of July, the Center for Drug Evaluation and Research (CDER) and the Center for Biologics Evaluation and Research (CBER) released the draft guidance “Request for Quality Metrics” and is now seeking public input by September 28, 2015 on a number of key points.

The guidance expands on how the FDA intends to act on its ability to request data it would normally inspect during an audit “in advance or in lieu of” an inspection, as it is authorized to do under Section 704(a)(4)(A) of the FD&C Act (added by FDASIA section 706, Records for Inspection).  It also points out that failure to provide the requested data is tantamount to refusal to permit an inspection and would render the affected product adulterated according to Section 501(j).

What the FDA is hoping to achieve, of course, is to make its auditing program more efficient by directing resources, i.e., actual audits, towards the highest risk establishments and it hopes to use the metrics to identify these establishments.  This should also improve the FDA’s ability to avoid drug shortages by identifying potential problems earlier.

Drug makers with favorable metrics could see a decrease in auditing frequency as a consequence.  At the same time, this shift might be uncomfortable for quality assurance departments, which typically are quite protective of company data and carefully manage any kind of audit.  It may also not be clear for a while what data ranges would be considered “favorable”.

FDA is planning to request the following data:

• The number of lots attempted of the product.
• The number of specification-related rejected lots of the product, rejected during or after manufacturing.
• The number of attempted lots pending disposition for more than 30 days.
• The number of OOS results for the product, including stability testing.
• The number of lot release and stability tests conducted for the product.
• The number of OOS results for lot release and stability tests for the product which are invalidated due to lab error.
• The number of product quality complaints received for the product.
• The number of lots attempted which are released for distribution or for the next stage of manufacturing the product.
• If the associated APRs or PQRs were completed within 30 days of annual due date for the product.
• The number of APRs or PQRs required for the product.

These data points would then be used to calculate:

• Lot Acceptance Rate
• Product Quality Complaint Rate
• Invalidated Out-of-Specification (OOS) Rate
• Annual Product Review (APR) or Product Quality Review (PQR) on Time Rate

The draft guidance is specifically requesting comment on the frequency of data requests as well as a number of optional metrics that are intended to be used to assess the degree to which an establishment has established what FDA calls a “quality culture”.  These metrics focus on things such as senior management engagement, CAPA effectiveness and process capability/performance.

Besides accepting the fact that sensitive quality data is going to be requested (though not made public) by FDA at an arguably higher frequency than the onsite audits of the past, quality departments will have to develop their systems to accurately collect and transmit the requested data.  Submitted data will be subject to verification during onsite audits and inaccuracies could have unpleasant consequences.  The metrics outlined in the guidance would commonly come from three systems: Enterprise/Manufacturing Resource Planning (ERP/MRP), Laboratory Information Management (LIMS) and Quality Management (QMS). Many establishments still operate with paper or hybrid paper systems in one or more of these areas.  Receiving a request for quality metrics may prove burdensome to compile, verify and manage for those that are not prepared.

You can view the FDA Request for Quality Metrics draft guidance here.

Bio:

Oliver Wolf, who joined MasterControl in 2004, spearheads the development of the process and CAPA modules. He has almost two decades of experience in information systems and computer validation in the pharmaceutical and software industries. He has worked at Cephalon Inc. as an IT manager and as a consultant at Pfizer, Amgen, and Eli Lilly. He earned a double bachelor’s degree in biology and environmental studies magna cum laude from Tufts University. He has an MBA, with an emphasis in information technology, from University of Utah.