Improvement Alphabet: QbD, PAT, LSS, DOE, SPC?How Do They Fit Together? | IVT - - BioPharm International

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Improvement Alphabet: QbD, PAT, LSS, DOE, SPC?How Do They Fit Together? | IVT

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Consider the following scenario, a new pharmaceutical or biotech scientist or engineer is assigned the job of solving a problem, improving a process, or just developing better understanding how a process works. Five different people are asked for advice and guidance, and five different recommendations are received and summarized as – quality by design (QbD), process analytical technology (PAT), lean six sigma (LSS), design of experiments (DOE), and statistical process control (SPC). Each “advisor” has had success with their recommended approach. So what should this professional to do? Which approach should the professional use? First some context is needed to aid understanding of the five approaches. 

Problem Solving and Process Improvement Context

It is important to recognize that all five approaches utilize system and process thinking, are helpful, and have merit, particularly when used in the application the approach was designed to handle. There is also considerable overlap by what the approaches can do regarding concepts, methods, and tools. Two guiding considerations that aid selection are:

  • What function is one working in – development or manufacturing?
  • What is the need – process or product design or redesign, process control, or improvement of a product or process?

Understanding is enabled by reviewing the definitions of the approaches.

Quality-by-Design

QbD is defined as a systematic approach to development that begins with predefined objectives, emphasizes product and process understanding and process control, and is based on sound science and quality risk management(1).QbD is about designing quality into a product and its manufacturing process (2) so that in-process and final product inspection is less critical and can be reduced. The quality community learned decades ago that quality must be “built in,” it cannot be “inspected in.” Borman, et al (2007); Schweitzer, et al (2010); and McCurdy, et al (2010) discuss applications of QbD (3-5).

Since announcing the value of QbD, US Food and Drug Administration has continued to emphasize its importance in the recently released Process Validation Guideline (6) and again in 2012, requiring the use of QbD for new abbreviated new drug application (ANDA) filings stating, “We encourage you to apply Quality by Design (QbD) principles to the pharmaceutical development of your future original ANDA product submissions, as of January 1, 2013.” (7).

In 2012, FDA stated that a “risk-based, scientifically sound submission would be expected to include the following: Quality target product profile (QTPP), critical quality attributes (CQAs) of the drug product, product design and understanding including identification of critical attributes of excipients, drug substance(s), and/or container closure systems, process design and understanding including identification of critical process parameters and in-process material attributes control strategy and justification.” (8).

Process Analytical Technology

The following direct quote from the FDA guidance explains PAT well:The Agency considers PAT to be a system for designing, analyzing, and controlling manufacturing through timely measurements (i.e., during processing) of critical quality and performance attributes of raw and in-process materials and processes, with the goal of ensuring final product quality. It is important to note that the term analytical in PAT is viewed broadly to include chemical, physical, microbiological, mathematical, and risk analysis conducted in an integrated manner. The goal of PAT is to enhance understanding and control the manufacturing process, which is consistent with our current drug quality system: quality cannot be tested into products; it should be built-in or should beby design. Consequently, the tools and principles described in this guidance should be used for gaining process understanding and can also be used to meet the regulatory requirements for validating and controlling the manufacturing process.” (9).

PAT has many applications. Some identified by Rathore, Bhambure, and Ghare (2010) include: rapid tablet identification using acoustic resonance spectroscopy, near-infrared spectroscopy (NIR) based powder flow characterization, active drug identification, and content determination using NIR and roller compaction dry granulation based on effusivity sensor measurements. As noted above, PAT is a system for designing, analyzing, and controlling a manufacturing process and is thus a collection of concepts, methods, and tools (10).

Lean Six Sigma

LSS is a business improvement strategy and system with supporting concepts, methods, and tools that focuses on increasing process performance resulting in enhanced customer satisfaction and improved bottom line results (11). One objective of LSS is the reduction of variation in the output of a process. Process performance is measured by the flow of material and information through the process as well as product quality and cost, process cycle time, and customer satisfaction.

A pharmaceutical company had concern that one of its blockbuster drugs had considerable finished product inventory, and yet product delivery times were very long. An LSS project was chartered with the goal of reducing the cycle time of batch release by 50%. The analysis of batch release sub-process cycle times showed that review by manufacturing accounted for the major portion on the total cycle time. The review process by manufacturing was revised using lean manufacturing principles. The overall cycle time was reduced by 35-50% depending on the product type, the inventory of the drug was reduced by $5 million (one time reduction), and the annual operating costs were reduced by $200,000 (12).

Design-of-Experiment 

DOE is asystematic approach to experimentation wherein the process variables (X) are changed in a controlled way and the effects on the process outputs (Y) are measured, critical process variables and interactions are identified, experimental knowledge is maximized, and predictive cause-effect relationships [Y=f(X)] are developed. DOE can be used to design experiments for building knowledge about any product and process in manufacturing and service processes alike where X variables can be controlled and where quantitative Y responses can be reliably measured (13). In the pharmaceutical and biotech QbD world, in addition to the uses above, DOE is used to establish a design space and control strategy for a process or test method. Borman, et al, (2007), Schweitzer, et al (2010) and McCurdy, et al, (2010) discuss some examples.

Aggarwal (2006) discussed an API development study that was designed to increase yield of the process, which was approximately 40%. Conducting two designed experiments, the yield was increased to more than 90%; the lab capacity was doubled; and costs were reduced by using less catalyst, as learned from the experiments. In the first experiment, five variables were studied in 20 runs and yields of 75% were observed. The analysis of the data indicated that the ranges of some of the variables should be changed and one variable should be held constant in the next experiment. The resulting 30-run experiment identified a set of conditions that produced more than 97% yield. As a result, the yield of the process was more than doubled using two experiments and 50 runs (14).

Statistical Process Control

SPC is a collection of statistical and non-statistical tools that help manufacturers understand the variation observed in process performance, help maintain the process in a state of statistical control (process variation is predictable within upper and lower limits), and identify opportunities for improvement. SPC has a wide range of applicability and can be used to monitor, improve, and control any product and process in manufacturing and service processes alike (15). PAT described above often uses SPC as part of the process control procedure.

A biopharmaceutical process was exhibiting a low yield in fermentation and there was concern that the process would not be able to meet market demand. A control chart analysis identified the problem; there was significant variation between the batches of media used in the process. A quality control procedure for the batches of media was put in place and the process consistently produced yields 20-25% higher than the previous process had produced, enabling the company to meet market demand for the drug.

At a high level, the relationships between the five approaches, and the areas in which they are used, development and manufacturing, are shown in the Figure. Some conclusions from the figure include:

Improvement Alphabet

  • There is no step-by-step procedure to decide which approach to select. Over time, any organization involving development and manufacturing will use aspects of all of the approaches. At any point in time, the critical question is, “What approach should I use for this need at this time?”
  • Clearly, QbD is the broadest approach. It works in both development and manufacturing, having greater utility in development than in manufacturing. QbD utilizes PAT, DOE, and SPC and intersects with LSS. 

Contrary to the belief of many, QbD is much more than DOE. It also involves things such as QTPP, CQAs of the drug product, product and process design, and understanding (including identification of critical process parameters and attributes of excipients), drug packaging, and process control strategies. DOE is necessary but not sufficient. DOE is a critically important to the successful use of QbD but is not the only element of the system.

  • LSS, which has a large intersection with QbD, also works in both development and manufacturing and utilizes both DOE and SPC.
  • PAT has roots in development, where the information to perform PAT in manufacturing is developed. PAT holds the promise of real time process control and product release.
  • SPC and DOE have utility outside of development and manufacturing, in areas such as laboratory efficiency, change control, business processes, and sales and marketing (16). In general, if a “product or service” that needs to be created or improved can be defined, DOE and SPC will be useful in some way. 

Now return to the question posed at the beginning. What should this engineer or scientist do? First, the approach taken depends on the situation: the problem, the objectives and goals, and the environment – development or manufacturing. If the goal is development of a new product, process, or both, it is good strategy to think QbD with PAT being used to develop the control strategy. Both DOE and SPC will likely be used as part of QbD and PAT in such a situation. 

If one needs to improve product or process performance prior to launch, LSS can be useful. In such a situation, DOE and SPC techniques will often be used as part of the LSS approach.

LSS can also be useful in improving a product or process after launch. What is often overlooked is that processes can frequently be improved while remaining within the bounds of the original filing. Large gains in performance with significant financial are often result. Of course, if a design space was part of the original filing, then changes within the region of the design space are possible without getting approval from FDA. 

Another situation is the need to create and implement a monitoring system to better control the process and comply with the guidance provided in Stage 3 of the FDA Process Validation Guidance (6). Such a system will focus on assuring process stability and capability and use the SPC tools of control charts and process capability indices (17).

These approaches are most effectively utilized when viewed from a systems perspective. All of these approaches are in fact systems that include a set of concepts, methods, and tools. The systems’ thinking, which underlies these approaches, increases the effectiveness of the methods. It has been learned over the years that the effectiveness of any approach is greatly enhanced when a system is created and deployed to implement the approach. 

Other strategies are possible, as QbD, PAT, LSS, DOE, and SPC have many embedded elements and tools. The author’s hope is that this discussion will help the reader understand the uses and value of the approaches and provide an aid that will be useful as one works to use and implement these approaches to improve products and processes.  

© 2013 Ronald D. Snee

This article originally appeared in IVT - Read original article here

References
  1. ICH Q8(R2) Pharmaceutical Development.
  2. J.M. Juran, Juran on Quality by Design: The New Steps for Planning Quality into Goods and Services, The Free Press, New York, NY, 1992.
  3. P. Bourman, P. Nethercote, M. Chatfield, D. Thompson, and K. Truman, “Application of Quality by Design to Analytical Methods,” Pharmaceutical Technology, 142-152, 2007.
  4. M. Schweitzer, M. Pohl, M. Hanna-Brown, P. Nethercote, P. Borman, P. Smith, and J. Larew “Implications and Opportunities of Applying QbD Principles to Analytical Measurements,” Pharmaceutical Technology 34 (2), 52-59, 2010.
  5. V. McCurdy, M.T. am Ende, F.R. Busch, J. Mustakis, P. Rose. and M. R. Berry (2010) “Quality by Design using an Integrated Active Pharmaceutical Ingredient – Drug Product Approach to Development,” Pharmaceutical Engineering, 28-38, July/Aug 2010.
  6. FDA, Guidance for Industry - Process Validation: General Principles and Practices (Rockville, MD, Jan. 2011).
  7. FDA Information for Industry Webpage, available here.
  8. Pharmaceutical Manufacturing, July/August 2012.
  9. FDA, Guidance for Industry PAT — A Framework for Innovative Pharmaceutical Development, Manufacturing, and Quality Assurance (Rockville, MD, Sept. 2004).
  10. A.S. Rathore, R. Bhambure, and V. Ghare, “Process Analytical Technology (PAT) for Biopharmaceutical Products,” Annals of Bioanalytical  Chemistry 398 (1), 137-154, 2010.
  11. R.D. Snee, and R.W. Hoerl, Leading Six Sigma – A Step by Step Guide Based on Experience With General Electric and Other Six Sigma Companies (FT Prentice Hall, New York, NY) 2003.
  12. R.D. Snee, and R.W. Hoerl, Six Sigma beyond the Factory Floor – Deployment Strategies for Financial Services, Healthcare, and the Rest of the Real Economy, Financial Times Prentice Hall, New York, NY, 2005.
  13. D.C. Montgomery, Design and Analysis of Experiments, John Wiley and Sons, New York, NY, 8th ed., 2012.
  14. V.K. Aggarwal, A.C. Staubitz, and M. Owen “Optimization of the Mizoroki-Heck Reaction Using Design of Experiment (DOE)”, Organic Research and Development 10, 64-69, 2006.
  15. D.C. Montgomery, Introduction to Statistical Quality Control, John Wiley and Sons, New York, NY, 7th ed., 2011.
  16. J. Ledolter and A.J. Swersey, Testing 1-2-3: Experiment Design with Applications to Marketing and Service Operations, Stanford Business Books, Stanford, CA, 2007.
  17. R.D. Snee, “Using QbD to Enable CMO Manufacturing Process Development, Control and Improvement,” Pharmaceutical Outsourcing, 10-18, January/February 2011.

General References

  1. G.E.P Box, J.S. Hunter, and W.G. Hunter, Statistics for Experimenters – Design, Innovation and Discovery (2nd ed., John Wiley and Sons, New York, NY) 2005. 
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