By Velumani (Lou) A. Pillai and Martin Warman, Pfizer Corp.
HEY, HEY! The control system train has pulled into the pharmaceutical station! All aboard! Though PAT most directly applies to pharmaceuticals, its effects will be felt in other industries regulated by the U.S. Food and Drug Administration (FDA), such as food and beverage, as the FDA tightens its regulatory grip to include all ingestible products.
PAT’s benefits included reduced production cycle times, improved manufacturing efficiency, reduced rejects, and increased production uptime. PAT can also speed time to market for new products, improve operator safety, and improve relationships with regulatory agencies.
Encouraged by the FDA, the pharmaceutical industry is seeking to accelerate its manufacturing innovations. While it continues to spend on research and marketing, the pharmaceutical industry lags behind other automated process industries in manufacturing productivity.
||FIGURE 1: PAT MODE: MONITOR|
In the second paradigm, “Control” the CQA are monitored and controlled to limit or manage process variance within the design space and to ensure end product quality. The PAT software usually will be interfaced to Process Control Systems (PCS) that perform this control. (Click chart to enlarge)
To improve productivity, there is growing enthusiasm in pharmaceuticals for PAT, an FDA initiative to improve manufacturing efficiency and product quality, while also harmonizing regulatory expectations. PAT provides a framework for designing, analyzing, and controlling manufacturing. The PAT initiative focuses on the principles of building quality into products and processes, as well as continuous process improvement.
New century, new hope
Historically, innovation in pharmaceutical manufacturing was largely constrained by regulatory uncertainty. With the subsequent launch of its GMPs for 21st Century initiative, the FDA began calling for innovative approaches for process development, manufacturing, and quality assurance (QA). This was a paradigm shift that required quality to be designed in and not tested into products. Designing quality into products requires a comprehensive understanding of the process, including the impact of product components on process variability, along with mechanisms to manage the process.
Continuous improvement is a critical element in a sound quality system. The FDA expects pharmaceutical manufacturers to implement continuous improvement through the PAT framework. In addition to continuous improvement, the PAT framework also encompasses risk assessment, knowledge management, and on-line analysis.
Though the FDA published guidance on Pharmaceutical cGMP’s for the 21st century to enable innovation and continuous improvement, specific GMP regulations have not yet changed. Despite this delay, the FDA is providing science and risk-based guidance related to GMPs.
|FIGURE 2: PAT MODE: CONTROL
|In the third PAT paradigm, “optimization”, the process variance is reduced and process capability optimized by running controlled Design of Experiments (DoE) on the CQA’s. CQA’s can be predicted based on history and performance of the process using a set of relationships that have been modeled using mathematical models (set of equations). The predictions can be made available to the PCS’s for timely control. (Click chart to enlarge)
Consequently, PAT will help pharmaceutical manufacturers design, monitor, control, and predict process performance. Many of these functions are now implemented separately, but PAT promotes an integrated environment that combines modeling tools for design/analysis, process analyzers, and process control/optimization. Knowledge of all these functions is required to efficiently apply these technological innovations to pharmaceutical manufacturing.
So, how do we in the pharmaceutical and other regulated industries implement PAT successfully? How do we hop on the control system train?
We must first bridge several gaps in existing infrastructure and control system architectures. This article presents a strategic framework for managing this transition effectively. We also identify a new standard PAT software platform that will supplement and improve existing control system architectures.
PAT monitors, controls, optimizes
The most basic implementation of PAT is process monitoring. This involves monitoring critical-to-quality (CQAs) variables to build process knowledge, establish process variance, and set the design space in which the product is robust (See Figure 1 above).
The next level up in PAT implementation adds control to the basic process monitoring functions. CQAs are now monitored and controlled to limit or manage process variance in the design space to ensure product quality. PAT software must be interfaced (See Figure 2 below) to a process control system (PCS) that performs real-time control.