Process Analytical Technology And The Search For Quality By Design In Bio-Manufacturing

Process Analytical Technology And The Search For Quality By Design In Bio-Manufacturing

Table of contents

TOC o “1-3” h z u 1Introduction PAGEREF _Toc370560847 h 32Discussion PAGEREF _Toc370560848 h 43PAT Tools PAGEREF _Toc370560849 h 53.1 Multivariate data acquisition and analysis tools PAGEREF _Toc370560850 h 53.2Modern process analyzers or process analytical chemistry tools PAGEREF _Toc370560851 h 63.3Process and endpoint monitoring and control tools PAGEREF _Toc370560852 h 73.4Continuous improvement and knowledge management tools PAGEREF _Toc370560853 h 84Applying PAT tools in bio-manufacturing PAGEREF _Toc370560854 h 95Conclusion PAGEREF _Toc370560855 h 96References PAGEREF _Toc370560856 h 11

IntroductionProcess Analytical Technology (PAT) is a revolution in the pharmaceutical industry that was originated around two decades ago by United States Food and Drug Administration to minimize the risk of producing poor quality products. Shresth et al (2009, p. 225) defines PAT as “ a system for designing, analyzing, and controlling pharmaceutical manufacturing through timely quality measurements and performance attributes of materials and processes.” PAT is a framework that allows for continuous improvement of the quality of a product throughout the manufacturing process. As Shresth et al (2009, p. 225) explains, PAT involves physical, risk, biological, chemical and mathematical analysis which is carried out in a systematic manner. It is a process that requires clear understanding of all the processes that are involved in the process of manufacturing a product. PAT uses real time information to enhance manufacturing capability and to reduce processes variation. In other words, PAT involves real-time testing and using the information acquired to make the right adjustments based on clear understanding of how all activities and processes involved affect the final product (Streefland et al, 2013, p. 212; Shresth et al, 2009, p. 225).

In the manufacturing of chemical drugs, PAT is applied in relatively simple process phases such as drying, milling, granulation or tableting. However, bio-products and their production processes are more complex than chemical drugs and their production processes. Thus, application of PAT in bio-manufacturing processes is relatively more difficult. Despite the challenges, the quality of biochemical products can be enhanced through the use of PAT tools during production processes (Streefland et al, 2013, p. 214). This paper reviews some of the PAT tools that are available currently and examines their applicability in designing and in operating bioprocesses.

DiscussionAccording to Scott (2006a, p. 21), the key objective of developing PAT framework is to design and create processes that guarantee the manufacturer predefined quality of products. The procedures and activities involved enhance efficiency, safety and quality, which may vary depending on the product (Scott, 2006a, p. 21). The aim of introducing PAT framework is to:

Improve material and energy use and increase capacity

To use dedicated manufacturing equipments

To replace large-scale equipments with small-scale ones in order to solve some issues associated with large-scale equipments

To consider continuous processing to manage variability and to improve efficiency

To increase automation in order to reduce human error and enhance operator efficiency

Where possible, to enhance real time production

To prevent re-processing, reject and scraps

To introduce certain measurements and controls that can help to reduce cycle times during production (Scott, 2006a, p. 22).

PAT framework can also be applied in enhancing quality, efficiency and safety in bioprocesses. For instance, in a convectional cell culture bioreactor setting, samples are taken manually several times a day to monitor process performance. The samples are then analyzed using bench-top equipment and the results are finally entered into batch records. The monitoring process can be automated by applying online analyzers that have been developed recently (Streefland et al, 2013, p. 214). Generally, available PAT tools can be modified and applied in bio-manufacturing to achieve desired level of efficiency and quality of bio-products.

PAT ToolsThere are numerous tools that have been developed and implemented in improving efficiency, safety and quality during production of pharmaceutical products. When used within a system, the tools provide efficient and effective ways for gathering critical information regarding processes and materials. As well, the tools facilitate achievement of continuous improvement, development of risk-mitigation strategies and sharing of information and knowledge. Studies have shown that when implemented correctly, the tools can help to achieve monitoring and control goals and to reduce variability in bioprocesses (Streefland et al, 2013, p. 212). The currently available PAT tools can be grouped into the following categories

Multivariate data acquisition and analysis tools

Modern process analyzers or process analytical chemistry tools

Process and endpoint monitoring and control tools

Continuous improvement and knowledge management tools (Roggo, 2007, p. 685).

3.1 Multivariate data acquisition and analysis toolsFrom a biological, physical and chemical perspective, bio-pharmaceutical processes and products can be regarded as complex multi-factorial systems. As Shresth et al (2009, p. 225) explains, numerous developmental programs have been invented which help in identifying and in gathering in-depth information during formulation and processing of these systems. The information gathered using the programs is used in the development and improvement of product and process design. In some cases, producers use multivariate approaches, such as response surface methodologies, statistical design experiments, pattern recognition tools and process simulation. Usually, such approaches are used in conjunction with knowledge management systems. The applicability and reliability of each approach or model is assessed by statistically evaluating the model predictions. As Rathore, Bhambure and Ghare, (2010, p. 138) explain, methodological experiments based on statistical standards of randomization, reference distribution and orthogonality provide effective means for studying and identifying the impact and the results and the interaction of process and product variables.

As Rathore et al (2010, p. 138) explains, experiments that are based on the traditional one-factor-at-time model are not effective in assessing interactions between process and product variables. Experiments that are carried out during process and product development help to identify critical areas of improvement. A higher degree of complexity in the life-cycle of a product is achieved as the information gathered through continuous experiment grows. Information gathered from such experiments is utilized in the development of knowledge system for production process and products. As well, such information can become part of the overall organizational knowledge base. As the information grows in the organizational knowledge base, it can be mined to come up with useful patterns for current and future project. As Kamble (2013, p. 52) explains, information kept in experimental databases can also be used in development of simulation models which can help to significantly reduce overall development time and to facilitate continuous learning.

Modern process analyzers or process analytical chemistry toolsAs Mendhe, Rathore and Krull, (2012, p. 16) noted, the concept of process analytical chemistry has received significant attention over the last several decades. This has largely been caused by an increasing appreciation for the importance of collecting process data during production. Modern tools that measures physical attributes and chemical components during production have been developed from simple process measurements, such as pressure, temperature and pH (Mendhe, Rathore & Krull, 2012, p. 16). The tools provide effective measurements that contain data related to both chemical and physical attributes of materials used in the production process. The measurements are carried out in the following manner:

Off-line in a laboratory

At-line when the manufacturing process is set to begin

On-line during the manufacturing process. The measurement system is connected to the process and a sample is diverted for measurement. The sample may be returned back to proceed with processing after measurement

In-line where the production process may be stopped to allow adjustments or probe. Measurements after the correction are done in real time.

Noninvasive and nondestructive when the sensor for the measurement system is not in contact with the material being processed. Thus, the production process is not disturbed (Mendhe et al, 2012, p. 17)

Process and endpoint monitoring and control toolsThe following steps can be incorporated within the PAT framework for the optimization and development of design for manufacturing processes and drug formulations.

Identification and measurement of crucial process and material attributes relating the quality of a product

Development of a process measurement system to facilitate real-time monitoring of all crucial attributes

Develop process controls that aid in the collection and adjustment to enhance control of all crucial attributes

Developing mathematical relationships between measurements of critical process and material attributes and product quality attributes (Challa & Potumarthi, 2013, p. 67).

Generally, as Challa and Potumarthi (2013, p. 67) explain, a strong link between process development and product design is essential to ensure that all critical quality attributes are effectively controlled. The control and monitoring strategies applied to the production process are intended to closely monitor the process and to manipulate it in order to achieve a predefined outcome or to maintain a desired state. While developing strategies, it is vital to consider all information related to reliability and ability of analyzers to measure crucial process and material attributes, the attributes of input materials and the expected achievements at the endpoints of all phases within production process. This helps to ensure that consistent quality of materials is achieved at all endpoints of phases within production process (Challa & Potumarthi, 2013, p. 67). Within the PAT framework, the endpoint of a phase may not be necessarily tied to specific time fame; rather a phase can end after achievement of a desired material attribute. However, this does not imply that process time should be ignored. As Kourti (2006, p. 259) explains, there is need to develop a range of acceptable process times. The production process should be evaluated and considerations be made to address deviations from the acceptable process times. According to Kourti (2006, p. 259), it is essential to emphasize that process end points that are meant for real time release and more critical in comparison to those that are simply used for in-process control.

Continuous improvement and knowledge management toolsAs mentioned earlier, information collected during production process is quite important for continuous learning. Such information can be used to justify proposals for changes prior to the commencement of the production process (Alford, 2013, p. 40). For instance, such information can justify a proposal for change in technology. It is therefore essential for producers to develop knowledge management systems and approaches that support acquisition of such information. As Alford (2013, p. 40) explains, the knowledge management systems can also facilitate communication between manufacturers and regulatory agencies.

Applying PAT tools in bio-manufacturingAs Shresth et al (2009, p. 225) explains, implementation of PAT tools in bio-manufacturing means applying the collect sensors or tools to measure critical process parameters and critical quality attributes of a bioreactor process. There are numerous tools that can be applied in bio-manufacturing, depending on the critical attributes that need to be monitored or controlled during processing. Examples of available PAT tools that can be applied in bio-manufacturing are; optical spectroscopic tools (near infrared spectroscopy, infrared and RAMAN spectroscopy, photoacoustic spectroscopy, 2D fluorescence spectroscopy, UV spectroscopy and real-time imaging), biomolecular tools (biosensors/biochips, transcriptome and proteome measurements) and other tools such as bio calorimetry, on-line HPLC, flow cytometry and soft sensors (Shresth et al, 2009, p. 225). The PAT tools described above have different uses in bio-manufacturing processes. Some tools, such as on-line HPLC, allow online measurements for critical quality attributes and critical quality parameters. Some of them, such as near infrared spectroscopy, produce multivariate data that requires chemometric processing before use. Some tools such as deoxyribonucleic acid (DNA) microarrays cannot be used directly for process monitoring and control. However, such offline tools provide essential biological information about materials being processes.

ConclusionIn conclusion, the use of PAT can provide numerous benefits in biomanufacturing industry by improving product quality, enhancing efficiency and delivering financial value and superior asset utilization. PAT enables manufacturers to acquire in-depth knowledge of law materials and production processes. Critical information about raw material is acquired by characterizing it biologically, chemically and physically. PAT framework also provides an opportunity to understand manufacturing parameters. Combined together, the information acquired is used in making adjustments and corrections at various points within the production process. Any variations in the expected outcomes at endpoints of production phases are identified and corrections are made in real time. As a result, the PAT framework facilitates the achievement of a predefined product quality attributes. If they are applied in bioprocesses correctly, the available PAT tools can result in better products, huge time saving, better process control and more robust process. Ultimately, this may bring some benefits to a manufacturer, such as unique brand image and enhanced reputation for the organization, reduction in production costs and increase in profits.

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