OR WAIT 15 SECS
Jennifer Markarian is manufacturing editor of Pharmaceutical Technology.
Advances in process modeling and use of open-architecture control models will lead to more efficient process control methods.
A transformation in thinking about how to manage data for controlling manufacturing processes is needed to obtain improvements in efficiency and quality in the pharmaceutical industry, said thought leaders at the International Society of Automation (ISA) Food and Pharmaceutical Industry Division (FPID) symposium on Automation & Serialization in the Pharmaceutical & Food Industry, held Feb. 10-11, 2015 in Philadelphia, PA.
Quality must be controlled throughout the supply chain by managing production information from the raw material through to the patient in a holistic, end-to-end process, said Paul McKenzie, currently vice-president of Ethicon global R&D and recently head of Janssen's Manufacturing and Technical Operations. One problem is that information management is often still paper and people based, rather than automated and electronic. Product lifecycle management (PLM) seeks to organize and automate product information handling end to end. However, today’s systems are typically facility based, and various stages of a drug product may be manufactured at many different plants, including both internal facilities and external suppliers or contract facilities. In addition, different facilities may use different information management systems or applications. "We spend too much time recreating the same information in different systems, rather than having one, product and process data-based system," said McKenzie. The solution to the need to manage information from multiple plants, he proposed, is to base production information and recipes on the ISA S88 standard for batch process control, which provides an organized way of defining and describing processes and equipment. Over the past year, Janssen implemented this standard for one of its products. Now, with fully integrated data that feed back in real time, process engineers can optimize the process with cycle-time analysis and troubleshoot out-of-specification deviations. "The key is expression of the product end-to-end, drilling down to the plant and back up with data," said McKenzie. The open S88 standard allows greater transparency with outside partners, because information can be communicated easily. With a standard "language" for data, all parties are seeing the "same truth," whether they are in different companies, at different facilities, or simply in different departments (e.g., quality control or process), he explained.
The S88 recipe structure can also be applied to R&D. An R&D "data warehouse" that can hold data in a standardized recipe context can be used for knowledge management, and data can be drawn from it for data analysis or to compile regulatory filings, said Shaun McWeeney, principal engineer in the API large molecule group at Janssen, in a separate presentation. "Data warehousing makes it easier to analyze data. We don't need to waste time copying and pasting into spreadsheets to make graphs," he explained. Data warehousing is broadly used in other industries, such as retail, and it can help pharmaceutical companies be much more efficient, noted McWeeney.
Electronic production records (EPR) provide another tool for improving efficiency of data management in pharmaceutical manufacturing. Implementing EPR for a site-wide equipment monitoring system enabled electronic reports with review-by-exception and electronic signature approvals; saving several thousand hours per year of employee time previously spent manually logging parameter data and reviewing monthly reports fully paid for the infrastructure needed, noted Ken Kovacs, quality business systems manager at Fujirebio Diagnostics, in a presentation. The system was initially implemented using the ISA95 functional hierarchy model. That first project was completed three years ago, Kovacs said, and provided an infrastructure to add equipment both last year and this year. Having more accessible data allows the company to better analyze equipment performance. "We can address issues that weren't necessarily excursions, but that can be improved to make equipment more efficient. Looking at 24-hour, 7-day-a-week data also gives us a better understanding of the impact of equipment cycling," added Kovacs. The company is now applying the ISA88 process model to expand this system’s infrastructure for use in manufacturing operations management.