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Understanding How PAT Works: Lessons Learned Through Other Industries
By Bonnie Haferkamp, Life Sciences Industry Marketing Lead, Rockwell Automation
A global manufacturer operates one of its plants at 105 percent of theoretical maximum production rate. Another company achieves 30 percent reduced product variability and improved process stabilization.
Sound familiar? Maybe not if you’ve spent your career in the pharmaceutical industry.
These examples are from the plastics and cement industries, and they demonstrate the benefits of measuring, analyzing and controlling processes to achieve business benefits using principles that life sciences companies are pursuing today under the Process Analytical Technology (PAT) umbrella. Interestingly, discussions about PAT with people experienced in other industries produce perplexed looks followed by, “I don’t get it – we’ve been doing this for 20 years in our industry.”
Make no mistake – the challenges in the pharmaceutical industry are definitely unique, with a high degree of regulation and complex processes like cell culture and antibiotic fermentations. Still, there are lessons already learned and problems solved in consumer products, refining, metals and a host of other industries. With the FDA’s PAT initiative, the pharmaceutical industry is now heavily engaged in evaluating its own measurement, analysis and control capabilities, and looking to other industries for proven technologies and approaches that transfer to pharmaceutical operations.
PAT goes far beyond closed-loop control, which has been standard operating procedure in other industries for a long time, and it isn’t completely new to life sciences, especially when talking about biotech and fermentation processes. Unfortunately, much of the emphasis in the past on applying good chemical engineering principles to drug manufacturing was put aside during the last several years as the industry became focused on Y2K, business system implementations, and 21CFR Part 11. The result may have enhanced compliance, but did little to impact the end game of pharmaceutical manufacturing, which is making high quality product. PAT lets us refocus on improving the process of making drug products more efficiently and with less variability.
PAT allows companies to focus and determine what variables are most critical to the final desired product and where controls should be inserted into the process. The success of PAT is applying the process monitoring tools needed to analyze each of the critical product attributes and then effectively and accurately control them. Detecting errors or process deviations and correcting them while the product is being made is more cost-efficient and can help justify flexible regulatory paths for innovations in manufacturing and post-approval changes.
Achieving PAT principles identified by the FDA, such as process understanding and real-time releases, requires a change in technology and techniques currently employed in many pharmaceutical operations. Life sciences companies have undertaken numerous pilots evaluating: how to measure what needs to be measured (sensor development); how to validate those measurements to identify meaningful process parameter relationships; and how to integrate advanced strategies to achieve reliable, predictable operations. Defining and implementing an architecture incorporating all of these elements is also critical to achieving real-time releases of product leveraging PAT and realizing the business benefits that are the driving force behind these investments.
The FDA identifies the tools for supporting innovation as multivariate tools for design, data acquisition and analysis; process analyzers; process control tools; and continuous improvement and knowledge management tools. These concepts are illustrated in the following diagram.
Let’s look at the specific elements of measurement, analysis and control, and what other industries have done to address these challenges in successful implementations using concepts that can be applied to the life sciences industry.
Measuring Process Parameters
What sensors need to be in place to measure which process parameters?
Many companies are undergoing sensor development pilots because if you can’t measure it, you can’t analyze or control it. Some sensing problems can be solved with existing analytical instrumentation while others will require new technology in order to measure the parameter of interest in a non-destructive, real-time manner.
Food, beverage, drinking water and wastewater industries are finding multi-element sensors (MEMS) applications can help them identify trace contaminants, degradation patterns in fluids, and subtle patterns in chemical fingerprints correlated to quality. MEMS can simultaneously provide five or more process readings on one sensor, such as pH, oxidation, viscosity, temperature and other parameters, and withstand sterilization and harsh conditions. There is clearly a trend toward digital sensors, smart sensors (e.g. local processing and lab-on-a-chip devices) and distributed sensor networks. Within the sensor arena, there is considerable effort expended at biosensors and MEMs devices such as microfluidics, with new sensors for biological applications appearing almost weekly.
Analyzing Critical Process Parameters
What data is necessary for analyzing critical process parameters, and what tools do I need?
The FDA identifies the tools and concepts supporting innovation as “multivariate tools for design, data acquisition and analysis; process analyzers; process control tools; and continuous improvement and knowledge management tools.” There are plenty of off-the-shelf tools on the market proven in pharmaceutical and other industries using a number of multivariate techniques to analyze process data. But tools like these aren’t magically going to provide you the answers on which parameters are your critical control parameters (CCPs) or how to control your process.
Good process knowledge needs to be applied to determine what data sets to include, how to reconcile disparate data – time-stamped, tag-based data, sample-based analytical data, raw material parameters – and how the data should be cleansed and preprocessed. For example, in a batch or fed-batch operation, what is the best way to unfold the data to capture the maximum information content while producing valuable results? The bottom line on data analysis is that there are many algorithms and tools available, and applying good process knowledge during the analysis will help achieve the best results.
Process Control
What about closed-loop control and real-time releases?
Like other industries, implementing the elements of PAT is only valuable if it ultimately helps you achieve a business goal. Process control tools, the third element of the PAT tools identified by the FDA, help realize the potential of accurate sensor measurements and analysis.
For example, a cement manufacturer achieved more consistent product quality using online closed-loop multivariate control in its milling operations. Employing a multivariable predictive controller and soft sensors, the mill optimizer ensured smooth handling of process nonlinearities across multiple process units. During abnormal conditions, the optimizer switched to a rule-based control mechanism to restore stability. The result – 30 percent reduced product variability and improved process stabilization.
An alphaolefins plant operated at 105 percent of theoretical maximum production rate as a result of optimized closed-loop control. The oxidation rate in the batch processing is now carefully controlled with multiple setpoints to achieve consistent quality with virtually no rework or waste, far improved over the 30 percent rework prior to closed-loop control. Batch processes such as this have parallels to batch processes in the pharmaceutical industry.
The Path To Progress
In a world with one product on a dedicated process line, solving the measure, analysis and control problem could be done with one set of sensors, one analysis tool and one data source. The reality is that most facilities are not dedicated, and each process line may produce many products. In this environment, one of the fundamental challenges with implementing advanced measurement, analysis and optimization, is that you often end up with many different and disparate data sources. On the path to realizing PAT’s potential, industry leaders have proposed structured architectures for implementing PAT tools and concepts within manufacturing environments. Integrating PAT into the architecture of a plant provides a foundation for continuous improvement in operations, the FDA’s fourth PAT tool.
To become more competitive and profitable in the global economy, life sciences companies have had to restructure their operations to cut costs and improve operational efficiencies. Many have embraced FDA initiatives, such as PAT, that promote using advances in automation control and information technology to make process improvement efficiencies possible and reduce business risk.
The potential benefits of PAT for the life sciences industry more than offset the immediate challenges in measuring, analyzing and controlling your processes. By leveraging the successes achieved in other industries where quality is paramount and margins drive efficiency, you may find an unexpected source of new solutions that apply to life sciences in the new world of PAT.
Pharmaceutical Processing
Advantage Business Media
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