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Process analytical technology (PAT) has been defined by the United States Food and Drug Administration (FDA) as a mechanism to design, analyze, and control pharmaceutical manufacturing processes through the measurement of critical process parameters (CPP) which affect critical quality attributes (CQA).
The concept actually aims at understanding the processes by defining their CPPs, and accordingly monitoring them in a timely manner (preferably in-line or on-line) and thus being more efficient in testing while at the same time reducing over-processing, enhancing consistency and minimizing rejects.
The FDA has outlined a regulatory framework for PAT implementation. With this framework – according to Hinz – the FDA tries to motivate the pharmaceutical industry to improve the production process. Because of the tight regulatory requirements and the long development time for a new drug, the production technology is "frozen" at the time of conducting phase-2 clinical trials.
Generally, the PAT initiative from FDA is only one topic within the broader initiative of "Pharmaceutical cGMPs for the 21st century – A risk based approach".
PAT is a term used for describing a broader change in pharmaceutical manufacturing from static batch manufacturing to a more dynamic approach. It involves defining the Critical Process Parameters (CPPs) of the equipment used to make the product, which affect the Critical Quality Attributes (CQAs) of the product and then controlling these CPPs within defined limits. This allows manufacturers to produce products with consistent quality and also helps to reduce waste & overall costs.
This mechanism for producing consistent product quality & reducing waste presents a good case for utilising continuous manufacturing technologies. The control of a steady state process when you understand the upstream & downstream effects is an easier task as common cause variability is easier to define and monitor.
It would be acceptable to consider that raw materials used to manufacture pharmaceutical products can vary in their attributes e.g. moisture content, crystal structure etc. It would also be acceptable to consider that manufacturing equipment does not always operate in exactly the same fashion due to the inherent tolerance of the equipment and its components. It is therefore logical to say that variability in raw materials married with a static batch process with inherent variability in process equipment produces variable product. This is on the basis that a static batch process produces product by following a fixed recipe with fixed set-points.
With this in mind the PAT drive is to have a dynamic manufacturing process that compensates for variability both in raw materials & equipment to produce a consistent product.
The challenge to date with PAT for pharmaceutical manufacturers is knowing how to start. A common problem is picking a complex process and getting mired in the challenge of collecting and analyzing the data.
The following criteria serve as a basic framework for successful PAT roll-outs: (From A PAT Primer)
- Picking a simple process. (Think water for injection (WFI) or building monitoring system (BMS)
- All details and nuances are well understood and explained for that process.
- Determine what information is easily collected and accessible through current instrumentation.
- Understanding the appropriate intervals for collecting that data.
- Evaluating the tools available for reading and synchronizing the data.
In order to implement a successful PAT project, a combination of three main PAT tools is essential:
- Multivariate data acquisition and data analysis tools: usually advanced software packages which aid in design of experiments, collection of raw data and statistically analyzing this data in order to determine what parameters are CPP.
- Process analytical chemistry (PAC) tools: in-line and on-line analytical instruments used to measure those parameters that have been defined as CPP. These include mainly near infrared spectroscopy (NIRS); but also include biosensors, Raman spectroscopy, fiber optics and others.
- Continuous improvement and/or knowledge management tools: paper systems or software packages which accumulate Quality Control data acquired over time for specific processes with the aim of defining process weaknesses and implementing and monitoring process improvement initiatives. These products may be the same or separated from the statistical analysis tools above.
The long-term goals of PAT are to:
- reduce production cycling time
- prevent rejection of batches
- enable real time release
- increase automation and control
- improve energy and material use
- facilitate continuous processing
Currently NIR spectroscopy applications dominate the PAT projects. A possible next-generation solution is Energy Dispersive X-Ray Diffraction (EDXRD). For a detailed review of PAT tools see Scott, or Roggo. For an example of application see Gendre.
Although the FDA's PAT initiative encourages process control based on the real-time acquired data, a small part of PAT applications goes beyond monitoring the processes and follows the PACT (‘Process Analytically Controlled Technology’) approach.
MVA in PAT
Fundamental to process analytical technology (PAT) initiatives are the basics of multivariate analysis (MVDA) and design of experiments (DoE). This is because analysis of the process data is a key to understand the process and keep it under multivariate statistical control.
- FDA, Guidance for industry: PAT – A framework for innovative pharmaceutical development, manufacturing and quality assurance; September 2004
- Hinz, Process analytical technologies in the pharmaceutical industry: the FDA's PAT initiative; Anal Bioanal Chem, Vol 384, p1036-1042, 2006
- FDA, Pharmaceutical cGMPs for the 21st century – A risk based approach; Final Report, September 2004
- Williams, J: Healthcare Distributor, December 2006/January 2007, page 81. Colorado, United States: E.L.F. Publications, Inc. OCLC 833048096
- Scott, B.; Wilcock, A. (2006). "Process analytical technology in the pharmaceutical industry: A toolkit for continuous improvement". PDA Journal of Pharmaceutical Science and Technology / PDA. 60 (1): 17–53. PMID 17089677.
- Roggo, Y.; Chalus, P.; Maurer, L.; Lemamartinez, C.; Edmond, A.; Jent, N. (2007). "A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies". Journal of Pharmaceutical and Biomedical Analysis. 44 (3): 683–700. doi:10.1016/j.jpba.2007.03.023. PMID 17482417.
- Gendre, C.; Genty, M.; Boiret, M.; Julien, M.; Meunier, L. C.; Lecoq, O.; Baron, M.; Chaminade, P.; Péan, J. M. (2011). "Development of a Process Analytical Technology (PAT) for in-line monitoring of film thickness and mass of coating materials during a pan coating operation" (PDF). European Journal of Pharmaceutical Sciences. 43 (4): 244–250. doi:10.1016/j.ejps.2011.04.017. PMID 21569842.
- Nagy, B; et al. (2017). "In-line Raman spectroscopic monitoring and feedback control of a continuous twin-screw pharmaceutical powder blending and tableting process". International Journal of Pharmaceutics. 530 (1–2): 21–29. doi:10.1016/j.ijpharm.2017.07.041. PMID 28723408.