September 15, 2008 (Vol. 28, No. 16)
Process Analytical Technology Is a Key Part of Agency’s Plan for the 21st Century
Process analytical technology (PAT) has been the subject of regular discussion since the FDA released its guidance for the industry in September 2004. The goal, then and now, was to provide a mechanism to design, analyze, and control pharmaceutical manufacturing processes through the measurement and control of critical variables such as moisture content, particle size, temperatures, and a host of others.
Quality by design (QbD) and Real-Time Product Release (RTPR) are more recent variations on the theme, but PAT is vital to these, according to Brian Stephens, North American PAT coordinator at ABB, who was a participant in the recent IFPAC/INDUNIV “PAT Summer Summit” held in Puerto Rico.
PAT is a vital tool for drug companies, according to Stephens, as they work to achieve the goals that the FDA describes in its publication “Pharmaceutical cGMPs for the 21st Century—A Risk-Based Approach.” Among FDA’s goals is to encourage the use of the latest scientific advances in pharmaceutical manufacturing technology and to employ the latest quality management techniques in the process.
In short, the FDA wants pharma manufacturing technology to catch up with other industries, and to further assist industry in their efforts, the FDA has released “PAT—A Framework for Innovative Pharmaceutical Development, Manufacturing, and Quality Assurance” and other guidance.
With a notable sense of irony, Stephens recalls a Wall Street Journal article that claimed potato chip manufacturers use more advanced manufacturing technology than pharmaceutical manufacturers. Silicon chip makers, he noted, have increased their manufacturing efficiencies by more than 10,000% in the past few decades by using new technology. In contrast, the pharmaceutical industry has used the same basic manufacturing processes for the past several decades, with the only improvements being made in the equipment, not the process.
Currently, Stephens says, pharmaceutical products being manufactured using traditional control systems require immense amounts of documentation to be released for sale. Laboratory-based QC systems now in use must process numerous variables, which must be interfaced, calculated and reviewed on a batch-by-batch basis. This is usually performed by hand, using printed data, resulting in a process that is much slower than the actual manufacturing steps.
“There are some comments being made that we don’t have this function or that we don’t have that operation to make these improvements, but the tools exist to complete a closed-loop control scenario today,” Stephens stated. He went on to describe how data management can fit into the process control architecture to provide a fully functional PAT system.
Critical Control Attributes
“What needs to be in place are critical quality attributes (CQAs),” Stephens noted, “which are the physical, chemical, biological, or microbiological properties or characteristics in a process that should be within an appropriate limit, range, or distribution to ensure the desired product quality.” These must be related to product properties, and they are not usually simple measurements; examples of CQAs are the product’s identity, strength, potency, purity, density, particle size, and moisture content.
Critical process parameters (CPPs) affect CQAs and must be understood, monitored, and controlled to ensure that the process produces the desired quality in a consistent manner. This can be complex for many reasons, including the management of the collected data.
“Particle size and moisture can both be CQAs in a fluid bed dryer,” Stephens noted, “but the controller outputs to the process can conflict when each CQA is evaluated independently.” Because of this, data from the CQAs must be integrated and managed in a single controller database to provide a single controller output to the process, he emphasized. In fact, he claimed that in a full-blown PAT configuration, data management is often the most critical part of the system.
For example, measurement of CQAs often requires advanced analytical sensor techniques using multivariate calibration for converting the analytically measured data to a physical property. Sophisticated software is required to perform partial least squares, multiple linear regression, multivariate curve solutions, noise reduction, and other calculations. This data must be identified and its interaction defined to produce the optimal control strategy. What is not needed, Stephens stressed, is applying a large number of on-line analyzers to measure everything, collecting “tons and tons of data without regard to CQAs.”
“When you get data management implemented,” Stephens concluded, “you can use it to support QbD and RTPR and you can document the product’s design space that allows the use of a flexible regulatory approach as proposed by the FDA.” Citing the ICH document, “Guidance for Industry, Q8 Pharmaceutical Development,” Stephens described design space as “the multidimensional combination and interaction of input variables and process parameters that have been demonstrated to provide assurance of quality.” This is a different approach than earlier GMP guidance, which depended on documented evidence of a locked-down process for manufacturing quality drugs.
Recently in the Journal of Pharmaceutical Innovation, an article titled, “The Financial Returns on Investments in Process Analytical Technology and Lean Manufacturing: Benchmarks and Case Study,” concluded that a PAT system used with lean manufacturing principals can reduce product manufacturing cycle times from about 25 days to 12 days. Big pharma is critically aware that people and equipment used in manufacturing drugs are expensive, Stephens noted, and with savings such as this, and the promise of further savings and improved quality from improved understanding of the manufacturing process, they are wading into the PAT pool.
Anna Persson from Umetrics, expanded on the use of multivariate analysis (MVA) in her presentation, “Multivariate analysis based on design space.” She noted that the PAT guidance document provided by the FDA points out that MVA is a tool that can help ensure that manufacturing process data is utilized to increase process understanding. Typically, pharmaceutical manufacturing data is multivariate, generated in large amounts and comprises complex data sets.
“MVA is an analysis technique,” she noted, “that looks at all the data at one time. The design space is established by design of experiments and visualized by MVA.”
Given the noise and missing data that are common in manufacturing processes, MVA helps users build models for trouble-shooting and to increase process understanding. “We can use design to build quality into the process by defining the operating range of parameters that lead to product quality,” she added.
One goal is to build quality systems into what Persson referred to as the “golden batch trajectory.” Using this trajectory as a frame of reference enables QC operations to move from traditional validation to a process focus.
“One challenge is that data is collected and stored in different places,” Persson noted. “We see variability that can affect quality in both the process itself, as well as in the raw materials. It is important to look at both sources to relate variability to final QC results. This places a high demand on infrastructure to maintain data integrity and combine it into a format that suits real-time monitoring and prediction.”
Process analytical technology strongly implies that analytics are a key component of pharmaceutical cGMPs for the 21st century.
At the “Summer Summit,” Pfizer’s Ivelisse Colon-Rivera, Ph.D., addressed the conference in a presentation titled “Analytical instruments that integrate into PAT systems.” Dr. Colon-Rivera discussed a number of technologies that provide better numbers at greatly increased speeds. She noted that a typical LC run sequence includes at least six extra injections for blanks and system suitability, “which can mean 4.5 hours plus samples. This makes it difficult for PAT partners to base their decisions on our data,” she noted. And because PAT is a codevelopment process involving cross-functional teams across sites and divisions, the ability to assess challenges, obstacles, and improvements, as early as possible, is vital.
High-temperature LC (HTLC) is one technique that can pay big dividends. Dr. Colon-Rivera reported that HTLC can cut processing time from 50 minutes at 30°C to 13 min at 90°C with the same number of peaks and excellent resolution. In addition, correlating experimental limit of quantitation with temperature is useful for the development of low-level methods to determine potential impurities by conventional LC/UV.
Dr. Colon-Rivera also discussed ultra high-pressure LC (UPLC), which uses less solvent and less sample for a greener approach. Among UPLC’s advantages are a 90% reduction in run time; an increase in theoretical plates with up to 90% reduction in column load and an even greater reduction in solvent usage due to smaller I.D. columns. The technology could easily be transferred to a manufacturing facility or contract lab, Dr. Colon-Rivera added. Although adopting the technique requires updating to new UHPLC systems, which are not cheap, Dr. Colon-Rivera said the benefits outweigh any disadvantages.
Finally, Dr. Colon-Rivera discussed microextraction as a means of sample enrichment using either small volumes of, or no organic solvents, with no need for further concentration. These techniques—such as solid-phase microextraction, liquid phase microextraction, and microextraction in packed syringe—are fast, simple, some of them disposable, and cost-effective, Dr. Colon-Rivera said.
Bristol-Myers Squibb’s Gary McGeorge spoke in the conference on “Applications of chemical imaging,” a technique that has enjoyed rapid growth over the past decade as the required imaging and computer equipment has become widely available and robust computational software has been developed.
The technique provides both chemical information (spectra) and spatial information. For example, when molecules of a drug crystallize from solution, Raman spectra may show differences due to crystal structure variations, indicating polymorphism that can change pharmaceutical activity. Bristol-Myers Squibb also looks at images of blends during the blending process to measure performance.
A fundamental premise of chemical imaging, McGeorge told the conference, is that the relative location of components can be critical to pharmaceutical performance. Imaging of tablets can determine if aggregates of API influence the dissolution profile, measure the API particle size distribution, correlate laser line scan data with near infrared (NIR) image data, provide indices that can be used to describe spatial distribution and correlate these to dissolution characteristics, and determine why and how agglomerates influence the dissolution profile by imaging water uptake/ingress in situ.
The quantitative measures that result from chemical imaging can provide documented methods both in terms of processing and analysis, McGeorge noted, provide consistency across multiyear project timelines and provide numbers that can be utilized as specifications and in directed decision making. With a library of excipients and active ingredients, analysts could hypothetically evaluate what and how much is in a specific tablet, which would be useful for analyzing problems tablets, counterfeit analysis, and determination of homogeneity. With an archive of results, McGeorge pointed out, one can datamine and statistically compare variance across batches.
Although chemical imaging is not yet a manufacturing technology, it is in relative terms, a high-throughput process, McGeorge observed. He noted that Raman mapping was quite slow requiring about an hour per tablet and that now, using chemical imaging, processing 20 tablets per hour is realistic. In terms of QbD studies of blending time, perhaps 100 samples per day could be tested, effectively replacing HPLC. The technique might also replace time-intensive dissolution testing to improve throughput of manufacturing facilities.
As for the future development of chemical imaging, McGeorge identified three areas: continue exploring spatial distributions of components in drugs products, relate features of NIR images with those of other imaging and nonimaging methods, and relate performance metrics to images’ features across different product to develop formulation rules.
Goals of the FDA Guidance
Reduced cycle times
Increased equipment utilization
Reduced warehousing requirements (quarantine)
Reduced time to market
Reduced QA costs
Higher production inspection rate
Reduced process errors
Decreased product exposure
Improved product consistency
Continuous process improvement