Quality Systems for the 21st Century: Process Analytical Technology

In August 2002, the US Food and Drug Administration (FDA) launched the initiative "Pharmaceutical cGMPs for the 21st Century: A Risk-Based Approach."¹ 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.^2,3 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,"⁴ presented in August 2003.

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.⁵ 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 The first draft Guidance for Industry, "PAT - A Framework for Innovative Pharmaceutical Manufacturing and Quality Assurance," was issued in August 2003.⁶

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The FDA draft guidance defines PAT as "A system for designing, analysing and controlling manufacturing through the timely measurements of critical quality and performance attributes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 use of knowledge management tools.

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.

Implementing a PAT system

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.

The impact of PAT on QA

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.

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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.

Integrated systems approach

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.

Regulatory barrier

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.

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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 guidance⁹ according to a risk-based approach, or a comparability protocol can be submitted.¹⁰ Using the SUPAC route, an inspection would be performed (if required) by the PAT team or a PAT-certified inspector before implementation. A comparability protocol¹⁰ 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.

Learning from other industries

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.

Acknowledgements

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

References

1.

www.fda.gov/oc/guidance/gmp.html

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

3. www.fda.gov/cder/gmp/

4. www.fda.gov/oc/mcclellan/strategic.html

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

6. www.fda.gov/cder/guidance/5815dft.pdf

7. www.fda.gov/cber/gdlns/drugsubcmc.htm

8. www.fda.gov/cber/gdlns/drugcmc.htm

9. www.fda.gov/cder/guidance/1721fnl.pdf

10. www.fda.gov/cber/gdlns/cmprprot.htm