Jennifer Methfessel

It has been estimated that reducing inventory levels across the pharmaceutical industry to those already achieved by the very best pharmaceutical manufacturing facilities could deliver a one-off cash release of $76 billion. A further reduction to inventory levels achieved in other manufacturing industries would be worth another $15 billion.

  Process analytical technology (PAT) is a key enabling technology to deliver these savings. 

During the last 20 years, the use of on-line spectroscopic techniques for process monitoring and control has become commonplace in the petroleum, petrochemical and chemical industries.

  This is not true for the pharmaceutical sector, which has been slow to adopt on-line analysis and advanced process control techniques. Even though robust process analysers are available, the use of spectroscopic techniques such as Fourier transform infrared (FTIR), near infrared (NIR), mass spectrometry (MS) and Raman together with chemometric multivariate analysis tools remains primarily the domain of specialist analytical groups and isolated quality assurance (QA) laboratories in the pharmaceutical industry. Implementation on-line has been led by innovative groups of analysts and engineers who have recognized the potential of these techniques to improve understanding, help resolve existing manufacturing problems and decrease operating costs. Their successes are notable in view of the conservatism of the pharmaceutical industry as a whole, which has tended to act as a barrier to innovation and change, particularly in the manufacturing domain.  

The climate for on-line analysis and advanced process control in the pharmaceutical industry is currently undergoing rapid and radical change. "

Pharmaceutical cGMPs for the 21st Century: A Risk-Based Approach

Innovation and Continuous Improvement in the Pharmaceutical Industry

" highlight the support of FDA for continuous improvement in pharmaceutical manufacturing.

  At the same time, the requirement to reduce costs is becoming a high priority in Big Pharma boardrooms.

  The resistance to change of a traditionally conservative industry has been much reduced by this combination of a supportive regulatory environment and external cost pressures.  

The PAT concept is completely aligned with the wider FDA goal of a science and risk-based approach to current good manufacturing practices (cGMPs). PAT is defined as "a system for designing, analysing and controlling manufacturing through timely measurements of critical quality and performance attributes of raw and in-process materials and processes with the goal of ensuring final product quality".

  As a direct consequence of the "cGMPs for the 21st Century" initiative, the pharmaceutical industry is experiencing pressure from the regulator to address concerns around limited process understanding, process inefficiencies and continuous process improvement through the adoption of PAT. This paper will describe four key elements of a PAT implementation: 								

 building a science-based knowledge base 

 implementing a PAT system — regulatory strategies. 

The PAT guidance emphasizes the need to develop a deep understanding of the underlying scientific principles behind pharmaceutical manufacturing processes to determine the parameters critical to process and product quality. The knowledge base provided by the PAT approach is valuable in three main ways: 								

 It is a foundation for robust process and product design. 

 It supports and justifies flexible regulatory paths for innovative new approaches. 

 It facilitates continuous learning throughout the product life cycle. 

The design of experiments, and the capture and evaluation of analytical measurement data are essential parts of building the knowledge base.  

Knowledge capture is recommended at all stages of the product and process life cycle, beginning at the preformulation stage and extending through formulation, lab-scale production, scale-up, pilot plant and full-scale manufacturing (Figure 1). 

 Figure 1 A knowledge base is the foundation upon which a PAT system is based.			

In R&D, the PAT approach is concerned with the capture and evaluation of information from research studies on synthesis characterization, stability and alternative dosage forms using new and conventional methods of analysis. Examples of relevant data include records in laboratory notebooks; measurements recorded by analytical equipment; the results of process modelling; and results from laboratory and pilot-scale experiments. Networked IT systems and data management and analysis software provide the means to store, access and mine the information in a practical and, crucially, traceable manner. 

Data collected from scale-up production batches and manufacturing batches is added to the knowledge base as it becomes available during the product life cycle. Typically, this includes data on raw material attributes, process parameters, product quality attributes and environmental conditions, and with time, the full range of variability is represented. Examples of sources of variability that are often only fully revealed with time include  										

 Variations in the raw material supplier manufacturing processes that impact the chemical and physical attributes of the supplied materials. 

 Time-based variations in manufacturing performance (e.g., between equipment maintenance events). 

 Long-term equipment ageing and degradation effects. 

 Effects linked to planned changes to equipment/analyser hardware and software. 

 Individual ways of working (i.e., variations attributable to people). 

 Changes in the local environment (e.g., temperature and humidity). 

This type of PAT knowledge base is of most benefit when it provides a means to evaluate the applicability of the established multifactorial relationships between process and product attributes in different scenarios; for example, by enabling the prediction of the effect of different combinations of process attributes on product quality. This capability is underpinned by general developments in IT technology, which have led to the decrease in costs for hardware and software, and the increasing availability of sophisticated statistical analysis, data modelling and knowledge management applications. As a consequence, establishing and maintaining a knowledge base is well within the reach of all pharmaceutical companies. 

Key differences between current practice in pharmaceutical manufacturing and a PAT approach can be summarized as:  									

 The use of novel analytical technologies.  

 The establishment of multifactorial relationships between materials, process and environmental conditions, and an understanding the consequences of these relationships for product quality and process robustness.  

Following a PAT approach, the understanding of the interaction between process and product is the basis for the design of the process monitoring, process control and QA strategies used in manufacturing.  

It is important to realize that process analysers and PAT tools that are used only for monitoring incoming raw materials, process variables and process end-points do not constitute a PAT approach. Rather, PAT is an integrated approach in which the results obtained from the real-time analysis of critical process control points are used to control the process in some way. During manufacturing, process parameters are adjusted (within clearly defined limits) to produce the desired product quality attributes at the process end-point (this may mean a process unit operation or the process as a whole). The automation systems required for this level of process control are available today and are used extensively in the chemical and petrochemical industries. Figure 2 shows an example of the degree to which the integration of process control sensors has already been realized. Indeed, the same automation systems are already in use in the pharmaceutical industry, but they are rarely integrated with process analysers. Data management is also extremely fragmented, with individual storage, analysis and review tools for each process analyser, preventing the evaluation of trends, and cause and effect relationships. 

Figure 2 Advanced extended process control systems are available and can be integrated with process analysers to realize integrated PAT systems for manufacturing.			

Another outcome of using a PAT approach in manufacturing is a move away from end product quality control to a real-time QA approach, which gives rise to a change in the definition of quality decision criteria, and this includes acceptance criteria. The most important differences are: 									

 A move away from time-based end-points towards the attainment of defined quality attributes. 

 The use of rigorous statistical principles to define acceptance criteria for product attributes based on the nature of the test and the sample size. 

Figure 3 shows the installation of a NIR process analyser probe in a fluid bed dryer. The drying time is controlled by the measurement of the moisture content of the product, moving away from a time-based end-point. Taking the PAT approach a stage further, the dryer operating parameters can be controlled within predefined process limits to ensure that the dryer is operating in the most efficient drying regime. With this approach, drying cycle times can be reduced by 25–50% while delivering product with a more repeatable particle size distribution, as well as moisture content, to the next processing step. 

Figure 3 NIR probe mounted in a fluid bed dryer used to monitor and control the drying process (photo courtesy of Glatt Air Technologies).			

An example of the use of rigorous statistical principles is the real-time release (RTR) of final product based on on-line or in-line analysis. The PAT guidance indicates that on-line analytical methods for real-time release should be managed as an alternative analytical procedure for product release according to existing guidelines and regulations, and a number of pharmaceutical companies are investigating or have already submitted NIR-based methods for release testing to FDA. It follows that batch records will also need to be reported using methods of evaluation that are appropriate for this type of data; that is, as acceptance ranges, confidence intervals and distribution plots showing variation between and within batches. 

FDA encourages the use of the PAT approach for new drug applications (NDAs) and for approved products. In the case of approved products, data can be collected during manufacturing from process sensors and on-line analysers, and evaluated using PAT tools to establish the scientific basis for moving to a PAT approach. The PAT guidance indicates a variety of risk-based approaches to managing the introduction of on-line analysers into existing processes with the aim of minimizing the regulatory burden for the manufacturer and encouraging innovation. The guidance clearly states that data collected in this way from existing processes, for the purpose of improving process understanding, would be considered research data and would not normally be relevant for regulatory inspections. 

To demonstrate the validity of the in-process measurement results and process monitoring and control systems, the elements of the PAT system will need to be validated according to the appropriate GxP regulations. Existing FDA guidelines on computer systems validation and software validation are applicable to PAT systems.

  The International Society for Pharmaceutical Engineering's (ISPE) good automated manufacturing process (GAMP) guide for the validation of automated systems also provides relevant information on good practice.

  ISPE is setting up a Community of Practice specifically to consider PAT systems. The validation plan for a PAT system will typically include the validation of process analyser hardware and software; software packages for data analysis; process control software; and IT systems for the management, storage and backup of results.  

In comparison with laboratory-based methods, analytical method validation for in-process analysers requires the consideration of a greater breadth of sources of error arising from the measurement system and on-line sampling issues: various approaches are required depending on the analytical technique and sampling method used. The PAT guideline highlights the value of using ASTM standards, citing various standards for the multivariate statistical analysis of analytical data, for standard practice for the comparison of test methods and for the on-line spectroscopic analysis of petroleum and petrochemical products in a process stream. The US Pharmacopeia includes general information on a number of spectroscopic techniques that are highly appropriate for the measurement of powders, intact tablets and solid samples on-line; for example, NIR spectroscopy. However, the analyst must currently rely on the scientific literature for guidance with respect to on-line implementation and appropriate statistical measures for system performance. Currently, there is no standardized approach and in each case the validation approach must be justified based on scientific principles and solid statistics. The ASTM E55 committee has been established to provide further guidance on good practice in this area.

The PAT guidance specifically discusses novel analytical techniques that measure and evaluate a process signature, which would be the characteristic response of the system to a given stimulus. Acoustic spectroscopy is perhaps the most familiar of these techniques. In such cases, comparison with the analytical method to a conventional compendial method is often difficult and even inappropriate, and the PAT guidance clearly indicates that test-to-test comparisons are not mandatory in such circumstances. The regulatory agency is prepared to review the use of such techniques based on a comprehensive statistical and risk analysis supported by additional information such as a mechanistic explanation of causal links between the measurement results and observed process variability. The predictive ability of the established correlation functions and multifactorial relationships is considered a key indicator of process understanding in such cases. 

In contrast to the increased requirement for analyser and IT system validation and more sophisticated statistical approaches for analytical method validation, it can well be imagined that the current practice of '3 batch' process validation will be rendered superfluous by the PAT approach. Using a PAT approach, the process will be continuously validated in real-time through control of the process critical control points and process critical end points.  

Implementing a PAT system — regulatory strategies

To facilitate innovation and encourage the adoption of the PAT approach, FDA has consciously sought to remove the perception that technological innovation inevitably carries a heavy regulatory burden. PAT is a joint initiative of the Center for Drug Evaluation and Research (CDER), Office of Regulatory Affairs (ORA) and the Center for Veterinary Medicine (CVM) within the "cGMPs for the 21st Century" framework. A PAT policy development team of four subject matter experts has been established to work with industry to facilitate discussion on proposed PAT approaches at an early stage and support FDA's science and risk-based approach to PAT. Additionally, a PAT review and inspection team has been established, which includes reviewers, compliance officers and inspectors who have been trained and certified in the PAT approach. 

The PAT guidance emphasizes that PAT implementation plans should be risk-based and the risk is regarded as being significantly lower when the process is well understood. This is considered to be the case when: 							

 All critical sources of variability are identified and explained. 

 Product quality attributes can be accurately and reliably predicted. 

Articles

The PAT guidance indicates a variety of risk-based approaches to managing the introduction of on-line analysers into existing processes with the aim of minimizing the regulatory burden for the manufacturer and encouraging innovation.

A practical approach to PAT implementation

In August 2002, the US Food and Drug Administration (FDA) launched the initiative "Pharmaceutical cGMPs for the 21st Century: A Risk-Based Approach."

 Progress reports, covering a range of topics, including risk-based inspections, dispute resolution, process analytical technology (PAT) and 21 CFR Part 11, were issued in February and September 2003.

 The reports focus on the principles of efficient risk management that should be applied to all FDA activities to obtain "the most public health bang for our regulatory buck," to quote FDA commissioner Mark McClellan. This theme was expanded upon in the FDA strategic action plan "Protecting and Advancing America's Health,"

FDA particularly hopes to apply the principles of efficient risk management to current standards and guidelines for pharmaceutical manufacturing - the current good manufacturing practices (cGMPs) - with the objective of encouraging "cost-reducing and precision-enhancing innovation in manufacturing and technology." Compared with other manufacturing industries, pharmaceutical production has invested little in continuous quality improvement; however, a recent study by PricewaterhouseCoopers proposes that large savings in production costs are possible while maintaining or improving product quality.

 FDA is looking to PAT to deliver improvements in process efficiency and process quality. 	

Under the umbrella of cGMPs for the 21st century, high-profile PAT guidance (from FDA's Center for Drug Evaluation and Research, Office of Regulatory Affairs and Center for Veterinary Medicine) is aimed at facilitating the introduction of innovative process monitoring and process control technologies, which in turn lead to the ability to manage quality in an efficient and effective way. The PAT concept is in complete alignment with the wider FDA goal of a science and risk-based approach to cGMPs. The first draft Guidance for Industry, "PAT - A Framework for Innovative Pharmaceutical Manufacturing and Quality Assurance," was issued in August 2003.

PAT: the importance of a science knowledge base

The FDA draft guidance defines PAT as "A system for designing, analysing and controlling manufacturing through the timely measurements of critical quality and performance attributes of raw and in-process materials and processes with the goal of ensuring final product quality." This presumes the existence of knowledge the parameters that are critical for product quality and performance. Preformulation, formulation, development and manufacturing functions have roles to play in building this product and process knowledge base. Knowledge capture begins in preformulation as the outcome of research studies on synthesis, characterization, stability and alternative dosage forms. Formulation and development then add to the knowledge gained in the development of preclinical and clinical formulations, as well as laboratory and pilot scale manufacturing processes. The knowledge base is not limited to the product and process under development; it also encompasses understanding gained from the development of similar products and processes studied in the past or in parallel with current development.				

Examples of relevant data include records in laboratory notebooks, measurements recorded by analytical equipment, the results of process modelling, and results from laboratory-scale experiments and scale-up production runs. In the context of the studies performed, the data form a body of information which, when used to identify trends, determines cause and effect. Underlying physical and chemical principles are also discovered, allowing a deep understanding to be gained about both the product and the processes. The role of a PAT system in research and development (R&D) is twofold: to provide information for the knowledge base from new and conventional methods of analysis, and to enable knowledge capture and the determination of process critical control points and process end-points. 

Figure 1:  An example of scalable and reusable extended automation to enable PAT.	

Key differences between current practice and a PAT approach (Figure 1) are 	

the use of novel analytical technologies 

the evaluation of multifactorial relationships between materials, processes and environmental conditions and their effect on product quality and process robustness

The knowledge base can be further enriched with production-scale data. Inevitably, the R&D departments responsible for manufacturing process descriptions and final product specifications (in the chemistry, manufacturing and controls [CMC] section of a regulatory submission), are unable to observe the full extent of product and process variation revealed during the manufacturing phase. Production engineers and operators have first-hand experience of built-in process inefficiencies and instabilities at the production scale. Examples of variability (genuine or artificial) revealed with time include variations in the supplier manufacturing processes or materials sources; equipment ageing and degradation; analyser drift or unstable calibrations; changes to equipment/analyser hardware and software; individual ways of working; and changes in the local environment. Information from the manufacturing stage combined with information from earlier development phases can provide new insights of value to current and future processes. 	

The knowledge base reflects depth of understanding about the scientific and engineering principles underlying product and process variability. FDA guidance states that evidence of a well-understood process will provide opportunities to develop "less restrictive regulatory approaches to manage change" when implementing and operating PAT systems. 	

The scientific knowledge gained during the preformulation, formulation and development phases is the basis for designing robust processes and reducing end-product variation using PAT. The value of current "three batch" process validation is called into question when the alternative is process monitoring and control for continuous assurance of process quality (based on known multifactorial relationships between materials' attributes, process parameters and environmental conditions).				

In the production environment, a PAT system typically includes process analysers and process analytical chemistry (PAC) tools to monitor and control process critical control points and process end-points (Figure 2). Analysis of the product and process knowledge base, together with a differentiated assessment of the risks to product quality and process robustness, is used to justify selection of these control points and the proposed control strategy. 	

Control points are not restricted to the measurement of in-process materials. Experience indicates that incoming raw material characterization, raw material and in-process material attributes (such as particle size, particle distribution and moisture content - often referred to as characteristics) are of importance. The term "process end-points" refers both to the end-point of the last operation in a manufacturing process that delivers the final product and to the end-point of a critical unit operation within the process that provides the starting material for the next stage of the process.	

Figure 2:  An example of a PAT system. The picture shows a fluid bed dryer being used with an NIR probe for drying end-point determination. (Photo courtesy of Glatt Air Techniques.)	

As discussed, it is inevitable that new variability patterns will be identified with the evaluation of results obtained from process analysers, PAC tools and conventional laboratory analysis during the manufacturing phase. Continuous learning and improvement is an aim of a PAT system, as described in the FDA guidance, and should be a key design goal of any PAT system implemented in a production environment. 	

From a quality assurance (QA) perspective, the introduction of a PAT system represents a change in the established analytical regime. PAT is the basis of a new and different quality system that allows the continuous "real-time" assurance of quality, based on measurements collected concurrently with the manufacturing process and whose natural conclusion is "real-time" product release.

The implications of QA organizational roles and responsibilities are far reaching. QA personnel will need to work with engineers and process chemists to formulate risk-based strategies for real-time process monitoring and control, and they will be responsible for justifying those strategies to regulators. Critically, within analytical laboratories, new competencies will be needed in multivariate analysis and advanced statistics to develop and validate analysis methods based on chemometric models and process signature techniques. By definition, there are no compendial methods for the newest and most innovative analytical techniques and in such instances a rigorous science-based case for the validity of the analysis must be documented. 	

One predictable side-effect of PAT systems is that the specificity of the information will increase (for example, even down to the level of a multicomponent analysis on each individual tablet) and, therefore, the volume of data to be handled and assessed will significantly increase. Current QA criteria designed to draw conclusions for a batch of thousands of items based on small representative samples will not be applicable to this data; rigorous science-based acceptance criteria will need to be defined. Consequently, the design and content of batch records, real-time analysis reports and out-of-specification investigations will change. Batch record review and product release will also require competency in multivariate methods and statistics.	

It is not difficult to imagine that PAT will completely revolutionize the organizational structures and procedures of QA as they are known today. QA can be expected to take a leading role in continuous learning and improvement driven by PAT systems in R&D and manufacturing. In parallel, a shift away from basing batch release on end-product testing can be expected, with a move towards risk-based product and process analysis and real-time release based on continuous QA throughout the manufacturing process.	

Reflecting on the impact of PAT in R&D, manufacturing and QA, an integrated systems approach to harmonize new work flows and innovative tools for analysis and knowledge management will be key to PAT's success. To provide relevant information, in real-time, to the right person, at an appropriate level of detail, sophisticated networked process control and information technology systems are required. This will present challenges in terms of designing and implementing cross-functional information and knowledge management systems that meet stakeholder needs and are compliant with FDA requirements (including 21 CFR Part 11). The integration of analysers into existing process control systems - for example, using the S88.01 methodology - and the realization of feed-forward or feedback control strategies will also need addressing by both analyser suppliers and engineering departments to realize the full potential of PAT.				

FDA expects that decisions regarding implementing novel on/in-line analysers to collect GMP manufacturing process data will be made under the company's own quality management system. Such decisions would be based on a risk analysis of how the experimental system may affect the existing process, for example, through the insertion of a measurement probe. Data collected in this way would be considered research data and, therefore, not within the scope of FDA inspections, except in outstanding circumstances.

 In this experimental phase, product release would continue according to established procedures based on conventional analytical methods and acceptance criteria.				

The draft guidance provides four options for implementing PAT as part of the process control and QA regime for manufactured products. PAT can be implemented under the facility's own quality management system and inspected as part of a regular cGMP inspection at a later date. This approach is most suitable for analysers used to monitor a critical unit operation or to determine an end-point based on measured material attributes rather than elapsed time. Similarly, it may also be applicable when real-time testing is used as an "alternative analytical procedure" as allowed under existing regulations. FDA has established a PAT team with relevant technical knowledge; early dialogue with this team about PAT implementation is recommended. 	

More ambitious PAT systems will require greater involvement from the PAT team prior to implementation. For example, the PAT team can conduct a cGMP inspection within the context of a pre-operational review of a PAT-enabled manufacturing facility. The resulting inspection report, and any actions arising, would then serve as a basis for final approval of the process and be filed with the regulatory submission. 	

Alternatively, a supplement can be submitted under existing SUPAC (scale-up and postapproval changes) CMC guidance

 according to a risk-based approach, or a comparability protocol can be submitted.

 Using the SUPAC route, an inspection would be performed (if required) by the PAT team or a PAT-certified inspector before implementation. A comparability protocol

 outlining the intended PAT research, validation and implementation plan would typically be submitted as part of a new drug application (NDA) or abbreviated new drug application (ANDA) for implementation at a later date. The implementation plan would utilize one of the three aforementioned implementation options.	

In all cases, FDA will be looking at the depth and breadth of the product and process knowledge base. The regulatory approach will consider the level of understanding of multifactorial relationships affecting formulation and manufacturing processes, together with the ability of process risk mitigation strategies to prevent or reduce risks to product quality. Companies that can demonstrate their ability to assess and manage risk based on good science can expect to benefit from smoother regulatory submission processes, fast track implementation of PAT systems and financial savings resulting from improved process robustness and efficiency.	

Much can be learned from the experience of other industry sectors, such as the chemical and petrochemical industries, which have gone through a similar transformation from laboratory-based to process-based quality systems during the last 20 years. Project and compliance risk and operational disruption can be minimized by bringing together the experience of diverse industries to deliver sustainable improvement; the pharmaceutical sector's expertise in quality systems can facilitate the implementation of effective PAT systems and deliver the substantial cost savings and quality improvements expected by FDA.				

The author would like to thank the many colleagues at ABB for their stimulating discussions that helped to develop these ideas. 								

www.fda.gov/cder/gmp/21stcenturysummary.htm

www.fda.gov/ohrms/dockets/ac/01/slides/3799s1_02_Dean/

Following the launch of its initiative, "Pharmaceutical cGMPs for the 21st Century: A Risk-Based Approach," FDA has been looking to process analytical technology (PAT) for improvements in process efficiency and quality. This article discusses the implementation of PAT systems into production environments, its impact on quality assurance and the necessity of an integrated approach. Options for implementing PAT are also presented.