On-demand Data Access and Analytics Enable Decisions for Process Excellence

Dec. 6, 2006
Critical to any PAT effort is having a single point of easy, on-demand access to all relevant data, in a context that’s meaningful to diverse groups of users. But data must be presented in a way that facilitates the identification and understanding of cause-and-effect relationships. Justin Neway, CSO and EVP of Aegis Analytics, talks about the issues and what they mean for PAT-related IT.
The results of a benchmarking study released in October by Professors Jeffrey Macher and Jackson Nickerson, based in part on FDA data, suggest that pharmaceutical manufacturers could be wasting more than $50 billion a year. This enormous figure quantifies the FDA’s own longstanding conclusions that the industry has room for improvement.

When looking closely at the role that integrated on-demand data access and analytics play in process improvement, two findings from the study become very relevant: 1) companies with strong manufacturing IT foundations perform better overall and 2) manufacturing decisions are often best initiated and implemented by those closest to the work.

At the intersection of IT and manufacturing decisions lies an important section of the FDA’s PAT Guideline for process improvement, which points to the value of continuous learning that comes from the analysis of process data when coupled with systems that support the acquisition of knowledge from that data:

"Continuous learning through data collection and analysis over the life cycle of a product is important. These data can contribute to justifying proposals for post-approval changes. Approaches and information technology systems that support knowledge acquisition from such databases are valuable for the manufacturers and can also facilitate scientific communication with the Agency."

In fact, the data to which the FDA refers comes not only from process instruments making real-time measurements on the current batch, but also from off-line measurements of the current batch as well as on-line and off-line measurements of previous batches. All these data from the current and previous batches are essential components of the knowledge base that can be tapped by manufacturers – across multiple levels of an organization – to optimize process development and full-scale operations.

Furthermore, these data are fragmented because they have accumulated in the operational data stores of many different systems, as well as paper records. These systems have evolved to serve the needs of groups of professionals with training in quality, engineering, operations, technical support, biology, chemistry, etc. Often these groups rely on others with IT skills to get their data for them. This causes delays that interfere with their effectiveness in developing the understanding of cause-and-effect relationships needed to reduce variability and increase process predictability. These realities must be taken into account in the process of “knowledge acquisition” to which the FDA has referred.

In addition to PAT and quality by design, there are many initiatives under way that share the goal of reducing the variability, cost and risks of life science manufacturing processes. At several points along the way they all depend fundamentally on identifying cause-and-effect relationships among process parameters and using this process understanding to improve process control.

Thus, one of the most critical aspects of many process improvement initiatives is a single point of easy, on-demand access to all the relevant data (including paper-based data) in a context meaningful to diverse groups of users and fully integrated with a collaborative, graphical data analysis and reporting environment for identifying and understanding cause-and-effect relationships in the process data.

The different data types must be easily accessible in a way that automatically accounts for their different formats and naming conventions, as well as their intra- and inter-batch genealogies. The access method must let users move directly into identifying and understanding the cause-and-effect relationships between Critical Process Parameters and Critical Quality Attributes without spending excessive amounts of time on programming tasks and manually collecting and cleaning up data.

Manufacturing IT solutions that can provide collaborative investigational analyses of cause-and-effect relationships help involve those closest to the work in manufacturing decisions – especially if systems are built for users who think in process terms more easily than in IT terms. Such solutions enable a collaborative team to span process development and manufacturing and move well beyond simple logistical and organizational improvements based on descriptive analysis to science-based process understanding and control based on investigational analysis.

Thus, process excellence and quality by design become practical realities only when the barriers to easily accessing and working with all the process data together are removed, and the team can spend its time instead on productive science-based collaboration that will satisfy not only the FDA, but also the many pharmaceutical executives concerned with lowering that $50 billion a year figure prior to Macher and Nickerson’s next benchmark study.

About the Author

Justin Neway, Ph.D., is executive vice president and chief science officer at Aegis Analytical Corp. He has more than 25 years of experience in pharmaceutical and biotechnological manufacturing, and in the development and application of software solutions for quality, compliance and operational efficiency in life science manufacturing. He can be reached at [email protected].


  1. Jeffrey Macher and Jackson Nickerson. 2006. Benchmarking Report on Pharmaceutical Manufacturing. October. (see http://faculty.msb.edu/jtm4/PMRP%20results/)

  2. U.S. Food and Drug Administration. 2004. PAT - A Framework for Innovative Pharmaceutical Manufacturing and Quality Assurance. Pharmaceutical CGMPs, September: 1-21.
About the Author

Justin O. Neway | Ph.D.