With guidance from the FDA and the ICH, the pharmaceutical industry is seeking to accelerate the pace of manufacturing innovation to meet ever-increasing cost, efficiency, and time-to-market demands.
Since the FDA issued its process analytical technology (PAT) guidance in 2003, PAT has received a considerable amount of attention. Its goal is to assure final product quality through process design, measurement, and analysis of key product-quality attributes, and dynamic control of the manufacturing process.
Pharmaceutical companies have, historically, taken a conservative approach to implementing process changes and upgrading manufacturing technology. But pharmaceutical business models are rapidly changing, and the importance of manufacturing’s role in overall financial performance has become a focal point as demonstrated by the increased adoption of Six Sigma and Lean Strategies throughout the industry.
While the cost of restructuring and potentially retooling production lines is significant, the long-term savings gained from the more efficient use of existing human and capital resources, reduced in-process scrap and waste, greater assurance of product quality, and mitigating the risk of product recalls outweigh the cost of implementing a PAT Program.
Traditional approaches to assuring product quality in pharmaceutical manufacturing depend heavily upon post-process off-line laboratory analyses. This model typically causes intermediate batch materials and the manufacturing line to idle for long periods awaiting the results of the off-line QC tests. The cost of this off-line lab-centric approach was documented in a recent MIT study that demonstrated over 80% of the manufacturing processes evaluated spent more time analyzing the product than manufacturing it.
The reasons that companies sometimes put off plans to address manufacturing inefficiencies are due to tradition, the cost of change, and inertia. Yet, most industry professionals willingly admit the consequences of maintaining this traditional approach include higher costs, higher levels of rejected product and rework, and ultimately slower time to market.
PAT enables manufacturers to consistently meet or exceed their product specifications by giving them the ability to dynamically adjust and control the manufacturing process based on real-time feedback information on critical product quality attributes. Equally important is having the informatics and automated process controls in place to make production adjustments based on analysis results. In other words, the impact of real-time analysis of the production stream is minimal if you are unable to quickly respond to the analytical results.
Implementing PAT is likely to have the biggest immediate impact on products with recurring quality issues because process deviations or exceptions often result in lost or poor product quality—leading to lower yields and higher costs, especially with expensive and hard-to-acquire raw and intermediate materials. Other good candidates are new products that have yet to come on-stream and for which regulatory submissions are in the formative stages.
Room for Improvement
Numerous sensor technologies can be employed throughout the manufacturing process to measure critical quality attributes of the in-process material. Typically, process steps such as reaction monitoring are assessed by spectroscopic sensors, which include near-infrared spectroscopy or Raman spectroscopy. These techniques can provide real-time information about the reaction progression but lack the ability to effectively resolve and quantify product variants or multiple components in a sample, particularly if the amounts of some of the components are at low levels. These analytical techniques rely heavily on chemometrics and are greatly challenged when differentiating chemically similar compounds, while at the same time their sensitivity and dynamic range is relatively poor and their ability to deliver absolute quantification is impractical.
The performance of these sensors is typically benchmarked and confirmed against an analytical reference standard, which in most instances is liquid chromatography (LC). As the predominant analytical reference standard in the QC lab, LC has demonstrated exceptional resolving power, is ultrasensitive, and can detect small amounts of impurities as low as 0.001% even in the presence of main components.
As the gold standard for off-line in-process sample analysis it would seem a likely technology for PAT implementation. The biggest issue with using LC as a PAT sensor has been that analysis time is too slow and existing LC platforms require a high degree of expertise to operate. These primary drawbacks have prevented LC from being routinely deployed for use in at-line or online in-process analysis.