1 June 2005

PAT hums at Interphex

Enthusiasm was palpable at a recent confab of the pharmaceutical industry-Interphex2005-for the many potential gains offered by process analytical technology (PAT), a new U.S. Food and Drug Administration (FDA) initiative that aims to foster improvements in manufacturing efficiency and product quality while creating a harmonization of regulatory expectations.

PAT provides a framework for designing, analyzing, and controlling manufacturing through timely measurements of raw and in-process materials to help ensure desired product quality.

The PAT initiative focuses on the principles of building quality into the product and process, as well as continuous process improvement. The goal of PAT is to encourage the industry to adopt innovative technologies to increase quality without raising concern that a new approach will lead to validation risks and production delays.

The key components of this knowledge-based approach are better understanding of the product manufacturing process, data analysis, process analytical tools, process monitoring, and continuous feedback during the manufacturing process. The underlying premise of PAT is quality cannot be tested into products; instead, it should be built-in or should be by design.

By encouraging the application of advanced analytical technologies and improvements in manufacturing efficiency, companies hope to parlay this strategy into higher quality products, less rework, increased profits, and a distinct competitive advantage.

A key driver of PAT comes from the regulatory side, where the FDA recognized that its traditional approvals procedures were actually hindering manufacturing innovation. With increased guidance and assurance from the FDA, PAT will encourage innovation and to reassure manufacturers that moving toward PAT-based manufacturing is in their best interest. While the benefits are clear, the hesitation toward adopting PAT is not without reservations.

Another important driver for PAT is the pressing need within the industry to reduce production costs and speed time-to-market. Pharmaceutical companies have historically taken a conservative approach when it comes to implementing process changes and upgrading technology. However, business models are changing and the importance of manufacturing's role in the financial performance of pharmaceutical companies is increasing.

While the cost of restructuring production lines may be daunting to smaller companies, the savings gained from more efficient use of resources, reduced waste, faster product approvals, and a lower risk of product recalls more than outweigh the cost to implement PAT.

Historically lab-centric

The traditional approach to regulating quality in pharmaceutical manufacturing involved a laboratory analysis to verify quality after manufacturing the finished product.

Many of these inefficiencies are enduring traditions, cost considerations, and a general reluctance to change. The disadvantages of this approach are continual process optimization, high levels of rejected product, and limited adoption of new technologies.

The pharmaceutical industry has historically been lab-centric-with minimal closed loop, real-time control, and limited enterprise-wide data availability.

A key to optimizing manufacturing in the future will be to make data visible in the context where it applies. Similarly, the ability to manage data with connectivity to the point of use will be important both within the company and with the FDA as part of the approval process.

For example, one of the prominent techniques of PAT is online monitoring, which means it's not only recording information, but it's also closing the loop and making adjustments to the process as the product is being manufactured. In other words, the ability to analyze the production stream is pointless if you can't respond to what the results are telling you.

In today's environment of open-system architecture, access to data is becoming less of a challenge. However, legacy systems persist, and the underlying technology of the legacy system often does not support the open-system philosophy.

Fortunately, control system vendors and third-party support companies have developed communication drivers in support of industry standard communications for many of the older control system platforms.

To provide consistency and seamless connectivity to the enterprise, many pharmaceutical companies are turning to technology suppliers like Rockwell Automation for single, integrated platforms. These modular, scalable, and open platforms help reduce lifecycle costs while assisting manufacturers with ever-changing compliance regulations.

Steps to implementation

While the emergence of PAT is not new, it does require a shift in organizational structure, including the development of in-house expertise and training; changes to existing inspection and validation methodologies; and reliance on specialized PAT support teams.

The implementation of a PAT program requires identifying the relevant technologies that can apply and the creation of an integrated data management infrastructure capable of recording the volume of necessary data. It also requires advanced automation, visualization, and analysis tools to manage the continuous identification and prediction stages in the process. For the majority of manufacturers, the transition to a PAT strategy is too monumental to be take place in a single effort. Instead, manufacturers should look to implement PAT programs in phases, starting with a specific project or production line and then gradually expanding to other areas.

The first step is to conduct a productivity improvement appraisal (PIA) to analyze existing product lines and determine those that may benefit most from PAT.

It is important to note the term analytical in PAT broadly includes chemical, physical, microbiological, mathematical, systemic, control, and risk analysis conducted in an integrated manner.

A PIA report identifies possible productivity improvement opportunities such as:

  • Identification of best practices
  • Identification of acquired critical operating data (COD)
  • Reusable engineering components
  • Cost reduction
  • Overall equipment effectiveness (OEE) data
  • Key performance indicators (KPIs)

Potential costs and benefits can then surface and will help create a list of financially viable projects. By carefully analyzing likely opportunities and implementing PAT projects in phases, companies more accurately assess the potential impact of process changes while managing investment costs.

Defining the business drivers and potential benefits from a PAT initiative are essential for a successful project. This effort also will establish the framework for continuous quality improvement.

Risk assessment, change management systems, and a process-monitoring plan emerge during this effort to establish the importance of the investment strategy.

Once a specific project is identified, the next step-discovery phase-involves reevaluating work practices, process chemistry, manufacturing techniques, and inspection and validation methods.

New products are one area where companies are concentrating their efforts and activities. Products with recurring quality issues are other good candidates, because process deviations or exceptions often result in lost or poor product quality leading to higher costs, especially with expensive and hard to acquire raw and intermediate materials. Management of processes by operators not fully understanding the process is another issue prompting analysis.

Partial PAT adoption is suitable for processes, which can benefit from new technology to correct or prevent a problem in the production process.

In the analysis phase, engineers perform a thorough and systematic review of product filings, exception history, manufacturing and quality data, and other sources for each product to verify if the original critical process parameters (CPPs) are still valid, or whether other parameters not originally identified are now more critical.

The emphasis is on CPPs that affect in-process product quality rather than quantitative measurements. The analysis also involves looking at the critical operator data (COD) necessary and/or for integrated system control or required by an operator to effectively manage the process.

The identification and confirmation of CPPs comes through using neural net, mathematical modeling, and statistical software to find correlations between key quality attributes and measured real-time process parameters.

By using real-time methods, the process endpoint no longer needs to be a fixed time, but rather can be the time required to reach a specific state or condition.

Behind the byline

Nicholas Sheble (nsheble@isa.org) edits the Networking & Communications department. The source for this piece is a Rockwell Automation (www.rockwellautomation.com) white paper issued at Interphex2005 in April.