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Charles E (Chuck) Miller:

Process Analytical Technology (PAT) across industries

In a series of blogs, I will provide a high-level retrospective look at Process Analytics, and the wide array of value propositions that it provides for various manufacturing industries. I will offer some experiences from various on-line applications that span several industries and in some cases have been in service for several decades. It is safe to say that some of these value propositions were expected, while several of them were unexpected. And finally, realizing that Process Analytics is not without cost or risk, these same experiences will be used to offer some “Best Practices” for Process Analytics – covering seven distinct categories.

In my previous blog post, I provided some background on the use of PAT in the pharmaceutical industry and explored some of the reasons why that industry is lagging behind other industries in applying PAT to manufacturing. Regardless of this, I believe that both pharma and non-pharma industries have made profound contributions to the field of PAT in recent years, and several of these contributions will be reviewed. From there, I will discuss the various ways that PAT brings value to all industries – noting that not all of these value propositions were expected.

Benefits derived from pharma

Although pharma is a relative newcomer to the PAT bandwagon, I believe there are several key contributions that the industry has made to the field. The heightened regulatory environment and strong risk management culture of the industry has driven developments in testing, compliance and quality assurance workflows of PAT instruments and associated data systems. For the same reasons, pharma has also been a strong leader in addressing lifecycle management and change management of PAT methods and multivariate calibrations [1]. In the sampling realm, pharma has driven a strongly needed resurgence of Theory of Sampling (TOS) principles [2-4], as well as the development of unique sampling interfaces for powders [5-6] and aseptic applications [7]. Finally, the recent Continuous Manufacturing initiatives in the industry [8] are essentially forcing PAT technologies to better address challenges in system integration, quality assurance strategy, data management, data integrity and closed loop control support. Until this time, these issues had mainly been in the domain of a few high-throughput chemicals and materials processes.

Benefits derived from other, non-pharma, industries

Of course, the non-pharma applications contributed much to PAT long before pharma got involved, as mentioned in a previous blog post. However, key ongoing contributions of other industries on the hardware side of PAT include ruggedized and robust instrument technology, such as internal and automated referencing schemes that support long-term measurement stability [9] and improved long-term robustness of sampling systems and sampling interfaces. On the software side, these industries drive continued advances in multivariate calibration optimization and data preprocessing math, as well as calibration transfer and instrument standardization methods [10]. An increase in the number of mature PAT applications with several, even dozens, of multivariate calibrations have also exposed significant issues in PAT method and model management [11-13], with several commercial method management solutions becoming available in recent years. Several non-pharma industries, especially food and agriculture, have accumulated valuable experience in the deployment and management of networked and distributed systems, where a central lab having a fewer number of more sophisticated master instruments supports a much larger number of simpler and rugged field instruments that are distributed to the points of use [14]. Last, but not least, long-standing PAT applications in the food and agriculture industries have been forced to address challenges arising from natural compositional variability of materials across crop years, geography and growing seasons. Experiences from these applications can certainly be leveraged towards similar raw material variability challenges frequently cited in pharma and biopharma applications [15-17].

PAT Value Propositions

Based on the long history of PAT applications described above, one would think that the business value of PAT should be self-evident. However, it is worth noting that, from this author’s perspective as a relative newcomer to the field, some value propositions were expected, and some of them were complete surprises. This is especially important to point out to new potential PAT users, who often struggle with providing compelling business cases to justify the significant capital outlay and cost of ownership that is required for many PAT applications.

  • Benefits in manufacturing:
    Some of the most cited benefits of PAT applications in manufacturing are the savings associated with reduced usage of traditional laboratory methods. These include the cost of consumables and solvents, as well as costs of labor and instrument management and maintenance. Other commonly mentioned benefits are those associated with the increased speed and/or relevance of PAT measurements compared to traditional measurements including enhanced process monitoring capabilities, improved process control, and improved product quality assurance – all of which can lead to fewer process deviations and fewer product discards. From a manufacturing operations perspective, an effective PAT application can drive improved process utility and substantial energy cost savings. In some cases, improved utility of existing manufacturing assets via PAT can enable delay or avoidance of large capital expenditures for new manufacturing assets. All the above-mentioned benefits contribute to a decreased cost of manufacture, which is often expressed on the corporate balance sheet as a reduced Cost of Goods Sold (or COGS).
  • Benefits in R&D:
    When used in research and development, PAT greatly enhances visibility to the process, thus driving improved process understanding, as well as more efficient and effective process scale-up – which ultimately leads to more efficient development and commercialization of new products. Furthermore, it is worth noting that even a “Process Chemometrics” or MSPC type of PAT system (mentioned in part 1 of this series) has clear value in an R&D context, in that it enables proactive aggregation and contextualization of complex data obtained from R&D experiments. This results in data that can be readily leveraged to support product commercialization and manufacturing activities later in the product’s lifecycle.
    Finally, some of the longest-recognized benefits of industrial PAT systems pertain to occupational safety and environmental stewardship. It is worth noting that an increasing number of newer-generation PAT systems are being put into service to support green chemistry and safety initiatives, just as some of the earliest PAT systems did decades ago.
  • Unexpected benefits:
    Throughout my experiences in supporting PAT applications, I’ve encountered some surprising benefits of the technology, which are widely varying in nature. Some of the more interesting ones are mentioned below [18]:
    • Retrospective Investigation Support: Even if it’s too late to “save” a batch from a deviation or Out of Spec (OOS) event, PAT and MSPC data analytics systems can still provide an efficient means of generating plots, tables and reports to support the ensuing investigation, thus streamlining the deviation investigation process.
    • Improved Culture: PAT and data analytics systems provide a means to “democratize” the process data and process monitoring mission, by providing easier access of key process information to a wider range of stakeholders. This can lead to a stronger, more diverse and inclusive process monitoring culture, as well as a stronger sense of process ownership by the stakeholders.
    • Preventive Maintenance: PAT and data analytics systems have shown that they can provide both direct and indirect assessment of the manufacturing system hardware components themselves, and even predict an impending component failure. This benefit extends to localized control loops in the process, where an MSPC monitoring system can immediately detect any breakdown of expected correlation structure between setpoints and process variables. In cases where a data analytics system aggregates data from analyzers and different parts of the process, real-time customized diagnostics could be configured to provide alerts to impending failure modes of the process.[19]
    • Utilities Support: MSPC monitoring systems can be used to monitor the utilities that support manufacturing operations, such as water, steam, coolant and power generation systems. Such monitoring applications could provide earlier warnings of possible process deviations or prompt an adjustment in the maintenance or production schedule.
    • Operational Flexibility: In some cases, the use of PAT for Real Time Release Testing (RTRT) can lead to flexibility in other seemingly unrelated workflows in the supply chain, such as inventory management and import testing. A specific example of this is the relaxing of an import testing requirement when importing a product manufactured using a RTRT release scheme [20]
    • Knowledge Management: For the MSPC monitoring type of PAT, the process model can be thought of as a convenient expression of the historical behavior of the process, covering the time period of the model training data. With the never-ending turnover of personnel in companies these days, such models can be quite useful for getting new personnel better acquainted with the process.

Finally, if you think I made up all these unexpected benefits in my dreams – I didn’t: Each of the above unexpected PAT value cases were demonstrated in actual production settings, and most of them are recited from first-hand knowledge!

PAT is worth the most when domain experts are directly involved. The Unscrambler suite has the intuitive model design and the integration capacity to put the domain experts in the driver’s seat and work side by side with analysts to create the most process value. Discover more >>

My next blogs will cover different real-life PAT case studies including benefits and lessons learned. See you then!

[1] ICH Harmonised Guideline, “Technical and Regulatory Considerations for Pharmaceutical Product Lifecycle Management Q12”, Final version Adopted on 20 November 2019.
[2] Gy, P (2004), Chemometrics and Intelligent Laboratory Systems, 74, 61-70.
[3] Kim H. Esbensen, Andrés D. Román-Ospino, Adriluz Sanchez, Rodolfo J. Romañach, Adequacy and verifiability of pharmaceutical mixtures and dose units by variographic analysis (Theory of Sampling) – A call for a regulatory paradigm shift, International Journal of Pharmaceutics 499 (2016) 156–174.
[4] Rodolfo J. Romañach,a Rafael Méndeza and Kim H. Esbensen, Application of Theory of Sampling principles for real-time monitoring of pharmaceutical powder blends by near infrared spectroscopy, Spectroscopy Europe/Asia, column 21 October 2019,
[5] Lighthouse probe,
[6] Fien De Leersnyder, Elisabeth Peeters, Hasna Djalabi, Valérie Vanhoorne,Bernd Van Snick, Ke Hong, Stephen Hammond, Angela Yang Liu, Eric Ziemons, Chris Vervaet, Thomas De Beer, Development and validation of an in-line NIR spectroscopic method for continuous blend potency determination in the feed frame of a tablet press, Journal of Pharmaceutical and Biomedical Analysis 151 (2018) 274–283
[7] Here’s How Aseptic sampling Market Growing by 2029, January 13, 2020,
[8] Quality Considerations for Continuous Manufacturing, Guidance for Industry, U.S. Department of Health and Human Services Food and Drug Administration Center for Drug Evaluation and Research (CDER) February 2019
[9] web site:
[10] C.E. Miller, ”Chemometrics in Process Analytical Chemistry”, in Process Analytical Technology, Blackwell Publishing, Oxford, 2005, pp. 226-324.
[11] C.E. Miller, “The Use of Chemometric Techniques in Process Analytical Method Development and Operation”, Chemomstrics Intell. Lab Syst., 30(1), pp. 11-22 (1995).
[12] C.E. Miller, ”Chemometrics for On-Line Spectroscopy Applications- Theory and Practice”, J. Chemometrics, 14, pp. 513-528 (2000).
[13] C.E. Miller, ”Chemometrics in Process Analytical Technology (PAT)”, in Process Analytical Technology, 2nd edition, K. Bakeev, editor, Wiley, Chichester UK, 2010, pp. 353-438.
[14] D. Ryan, Learning How to Break, So You Will Not Break, Your Calibrations: A Study of Crop Year Effects, IDRC 2014 Conference, August 7th, 2014.
[15] Lanan, M. “QbD for Raw Materials.” In Quality by Design for Biopharmaceuticals, edited by A.S. Rathore and R. Mhatre, 255–86. Hoboken: Wiley Interscience, 2009.
[16] F.Stauffer, V.Vanhoorne, G.Pilcer, P-F.Chavez, S.Rome, M.A.Schubert, L.Aerts, T.De Beera, “Raw material variability of an active pharmaceutical ingredient and its relevance for processability in secondary continuous pharmaceutical manufacturing”, European Journal of Pharmaceutics and Biopharmaceutics, Volume 127, June 2018, Pages 92-103.
[17] Wang, T. Bryan Looze, Tony Wang, Duncan Low, and Cenk Undey, An electronic format for data exchange between raw material suppliers and end users enabling superior knowledge management. Pharmaceutical Engineering 35,71–78 (2015).
[18] Charles E. Miller, John P. Higgins, Louis Obando, Jorge Vazquez, “On-line PAT Support for GMP Vaccines and Biologics Manufacturing- Challenges and Surprises”, IFPAC 2018 Conference
[19] Charles Miller, Nathan Pixley, Bruce Thompson, Manoharan Ramasamy, John Higgins, “Lifecycle Management of NIR Methods: Experiences from Real-Time-Release Testing and In-Process Controls in the Materials and Pharmaceutical Industries”, IFPAC 2016 Conference
[20] Manoharan Ramasamy, Nathan Pixley, Bruce Thompson, Chuck Miller, Louis Obando, John Higgins, Mark Eickhoff, “Realizing Value Through PAT Implementation”, IFPAC 2015.
Charles E (Chuck) Miller:

Career-long journey in Process Analytical Technology

Chuck Miller has over 30 years of experience in chemometrics, near-infrared spectroscopy, and Process Analytical Technologies, and applying these technologies to industrial challenges in R&D and manufacturing.

His career spans 13 years at DuPont, in the Process Analytical group and 10 years in the Process Analytical Technologies group within the Manufacturing Division of Merck Sharp and Dohme. In between he worked at Eigenvector Research in consulting, training and software development for 4 years.

Chuck obtained his Ph.D in Analytical Chemistry from the University of Washington, and did his post-doctoral research at the Max-Planck Institute for Polymer Research in Mainz, Germany and The Norwegian Food Research Institute in Ås, Norway.

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