Quality by design (QbD) helps pharmaceutical companies achieve the US Food and Drug Administration’s desired state of manufacturing. The agency intends QbD as a framework to foster process understanding based on science. The goals of QbD are reducing risks and saving time and money by designing processes with reduced variability and low technology-transfer risk early in the manufacturing cycle. Process development (PD) and manufacturing teams can collaborate by developing a design space and using process analytical technology tools and QbD principles with their combined experience to achieve manufacturing-process excellence.
To implement QbD and reduce business risks, teams should begin QbD collaboration early during PD. I mproving the design of a process before manufacturing begins has numerous benefits.
QbD entails scientifically designing product- and process-performance characteristics into the process to meet specific outcomes rather than deriving these characteristics empirically from the performance of test batches. Operating parameters are derived from a combination of previous knowledge and experimental assessment, along with a cause-and-effect model that links critical process parameters to the critical quality attributes.
Today's manual data access and disconnected analytics (sometimes called “spreadsheet madness”) are inconsistent with the goals of QbD. A process is well understood when the following three criteria are met:
It follows that accurate and reliable predictions reflect process understanding and, therefore, risk is inversely proportional to process understanding.
On-demand access to specific data that are available in real time in a collaborative investigational-analytics environment is a fundamental requirement for successful collaboration between the PD and manufacturing teams. Providing interactive, on-demand access for end users to data from disparate systems (e.g., laboratory information management systems, historians, and manufacturing execution systems) in one combined form allows investigations for cause-and-effect relationships that link critical process parameters to critical quality attributes to be completed in minutes instead of months. Analytics should include descriptive (i.e., what happened) as well as investigational (i.e., why it happened) capabilities. A ll types of data—including discrete (off-line), continuous (on-line), and paper-based data—should be included to make meaningful analyses possible.
Collaboration must enable productivity and compliance improvements that transcend logistical and organizational issues. It also must allow nonprogrammers and nonstatisticians to complete tasks quickly and effectively as a team.
Significant business, quality-compliance, and regulatory benefits can be achieved when teams collaborate by using available technology for on-demand data access and analysis. P ayoffs include:
In summary, appropriate technologies such as manufacturing process intelligence software enable PD and manufacturing teams to achieve shared QbD goals. Successful collaboration requires the flow of process data and information from PD into manufacturing. The lessons that manufacturing learns must be transmitted to PD, and the right data access, aggregation, analysis, and collaboration software tools must be in place to allow information to be exchanged in both directions. Only then can PD and manufacturing function as a single team with the best outcomes.
Justin O. Neway, PhD, is executive vice-president and chief science officer at Aegis Analytical Corporation, firstname.lastname@example.org.