Analytics Key to Forging Collaborative Models Between Product Development and Manufacturing

Interphex2007, New York, NY (Apr. 25)-As the pharmaceutical industry moves to a risk-based approach in manufacturing, analytics will play a critical role in not only meeting regulatory requirements but also in building needed collaboration between product development and manufacturing groups. 

“Real-time data access and investigational analysis are required for successful collaboration between process development and manufacturing,” said Justin O. Neway, executive vice-president and chief scientific officer at Aegis Analytical Corporation (Lafayette, CO, www.aegiscorp.com). He spoke at Wednesday’s conference session, “Reducing Tech Transfer Risk with On-Demand Data Access, Trending, Reporting and Analytics That Span Process Development and Manufacturing.”

Neway emphasized that such an approach is necessary to achieve the “desired state,” sought by the US Food and Drug Administration (Rockville, MD, www.fda.gov). In the desired state, FDA wants quality and performance by design of effective and efficient manufacturing processes, product specifications based on a mechanistic understanding, and an ability to affect continuous improvement and continuous real-time assurance of quality.

As a result, pharmaceutical manufacturers will see regulatory policies and procedures tailored to recognize the level of scientific knowledge supporting product applications, process validation, and process capability as well as risk-based regulatory scrutiny that relates to the level of scientific understanding of quality and performance and the capability of process-control strategies to prevent or mitigate risk of producing a poor quality product.

To meet these regulatory requirements, collaboration between product development and manufacturing is required. This collaboration, stressed Neway, means interactive, real-time on-demand access to all the data in combined form to complete investigations in minutes not weeks or months. The data must include all types of data, including paper-based data, to make meaningful analysis possible. Also, such an effort will allow non-programmers and non-statisticians to complete tasks as a collaborative team.

In realizing these objectives, Neway pointed out how today’s technology can achieve desired goals. It allows for real-time access to data from multiple and disparate sources and collaboration across different disciplines, scales, and sites. It also permits data retrieval from previous runs, reduction of time to understand multiple causes, investigations that use continuous and discrete data together, automatically accounting for process splits and recombinations, sharing of data results and reports for widespread teams, and simplified periodic reporting.