The Process Analytical Technology (PAT) Initiative focuses on minimizing risks to public health associated with pharmaceutical product manufacturing. It emerged from the 2004 guidance published by the U.S. Food & Drug Administration (FDA), which was part of the broader initiative Pharmaceutical cGMP for the 21st century – A risk-based approach.

Due to the COVID-19 pandemic, the pharmaceutical industry has seen unprecedented demand for production at massive scale. Improved process understanding is crucial for achieving such ambitious goals at each step of development from research labs, to pilot plants, to full-scale production sites.

That’s why it is more important than ever that companies implement PAT technologies as soon as possible, especially those offering continuous measurements and enabling real-time dosing of nutrients for fed-batch processes or perfusion applications. The goals have always been to increase product yield and product quality, minimize raw materials, and better control for pharmaceutical manufacturing processes, but they are now a critical step in addressing the pandemic.

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PAT Analysis

Manufacturing of biopharmaceutical products is complex due to the various types of bioprocessing and the nature of working with living organisms. PAT analysis establishes a regulatory framework intended to facilitate the voluntary development and implementation of innovation in pharmaceutical development, manufacturing, and quality assurance.

PAT especially focuses on enhancing the understanding and control of the manufacturing process to achieve Quality-by-Design (QbD): quality should be built into a product with an understanding of the product itself and the process by which it is developed and manufactured along with a knowledge of the risks involved in the manufacturing process and how best to mitigate those risks.

QbD and PAT

Quality-by-Design starts with the identification of Critical Quality Attributes (CQAs) that ensure a high performing pharmaceutical product. Next, Process Analytical Technology tools are identified and implemented to control Critical Process Parameters (CPPs) and Key Performance Indicators (KPIs) to improve process quality and yield. These tools, in combination with a formal design of experiments methodology, are essential in determining an ideal design space for product development and optimization.

Common Process Analytical Technology Tools

To develop a reliable control strategy, bioprocess engineers and scientists must assemble a set of PAT tools and PAT systems, especially technologies that provide high quality, real-time, and low-maintenance measurements. For this reason, in-line sensors that withstand the cleaning-in-place (CIP) and sterilization-in-place (SIP) procedures are preferred to on-line or at-line technologies.

PAT Method of Choice Alternative Methods
In-Line/In-Situ Sensor On-line Analyzer At-line & Off-line Analyzer
Critical Process Parameter pH
DO
DCO2
Temperature
Exhaust O2/CO2
Nutrients e.g. Glucose, Glutamine
Metabolites e.g. Lactate, Ammonium
Critical Quality Attribute Protein Quality e.g. Protein Glycosylation
Key Performance Indicator Protein Titer
Total Cell Density
Viable Cell Density

The availability of monitoring methods according to the scientific literature is represented with the indication of the corresponding measurement accuracy and robustness:


Accuracy, robustness, and repeatability good enough to be commonly implemented for process control


Accuracy, robustness, and repeatability not commonly accepted for process control


No true option available


Not required


Read More About Process Analytical Technology

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White Paper: How Does PAT Apply to the Bioreactor?

Your All-Inclusive Guide to Quality Attributes, Critical Process Parameters, and Key Performance Indicators at the Bioreactor

This 20-page white paper provides a comprehensive, but accessible, overview of requirements and recommendations related to the FDA’s Process Analytical Technology (PAT) initiative for the biopharmaceutical industry.

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Download Hamilton's PAT White Paper

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Sensor Systems for Bioprocess Monitoring