Quality by Design -- Experimental strategies for implementing Quality by Design (2)

By Ronald D. Snee, PhD, Snee Associates, LLC

Critical Elements ofExperimental Strategies for Implementing QbD Process and product Xs and Ys: TheYs - Critical Quality Attributes (CQAs) are the critical process output measurements linked to patient needs. The Xs - CriticalProcess Parameters (CPPs) encompass theprocess input (API and excipient), control,and environmental factors that have majoreffects on the CQAs. Raw materials factorsare included as Xs.

Experiments define the data collection processes used to study the relationships between the Xs and Ys so as to identifythe critical variables. These experiments typically make use of statistical design of experiment (DOE) techniques.

Strategy of Experimentation is the process of diagnosing the experimental environmentand determining the best experimentalstrategy to satisfy the objectives ofthe experiment. Process and measurement robustness is the ability of the process and measurement system to perform when faced with uncontrolled variation in process, input,and environmental variables.

Analytical Methods plays two roles inthe approach: as the process to collect the data and as the subject of QbD studies to assure the stability, quality of the measurements,and the robustness of the measurement methods (See Schweitzer, et al 2010). Measurement system quality is a critical element of QbD that should not be overlooked.

Process Model provides a quantitative model Y=f(X) relating the product and process outputs (Ys) to the inputs (Xs). The resulting model is typically based on both fundamental and statistical relationships and is used to create the design space.

Design Space is the combination of input variables and process parameters that provide assurance of product quality.

Process and Measurement Control is the use of control procedures, including statisticalprocess control (SPC), to keep the process and measurement system on target and within the desired variation. Processand measurement capability tracks process performance relative to CQA specifications and provides measurement repeatability and reproducibility regarding CQAs.

Reduced Risk and Enhanced Complianceis a function of the design space, processand measurement capability, control, androbustness.

Central to the approach is a strategy for experimentation, summarized in Table 1(Snee 2009c). This strategy identifies three experimental environments: screening, characterization,and optimization. The objectivesof each of the three phases (desired information) are summarized in Table 1.

The Screening Phase explores the effects of a large number of variables with the objective of identifying a smaller number ofvariables to study further in characterization or optimization experiments. Additionalscreening experiments involving additionalfactors may be needed when the results of the initial screening experiments are not promising. On several occasions I’ve seen the screening experiment solve the problem.

When there is very little known about the system being studied, sometimes “rangefinding"experiments are used in which candidate factors are varied one at a time to get an idea of appropriate factor levels. Yes, varying one factor at a time can be useful.

The Characterization Phase helps us better understand the system by estimating interactions as well as linear (main) effects. The process model is thus expanded to quantify how the variables interact with each other as well as to measure the effects of the variables
individually.

The Optimization Phase develops a predictive model for the system that can be used to find useful operating conditions (design space)
using response surface contour plots and, perhaps, mathematical optimization.

The SCO Strategy (screening, characterization,optimization), in fact, embodies several strategies, which are subsets of the overall SCO strategy, namely:
*Screening - Characterization - Optimization.
*Screening - Optimization.
*Characterization - Optimization.
*Screening - Characterization.
*Screening.
*Characterization.
*Optimization.

The end result of each of these sequences is a completed project. There is no guarantee of success in a given instance, only that the SCO strategy will 搑aise your batting average (Snee 2009c). Some examples of using this strategy are discussed in the Sidebar.

Guidance on Using Strategy of Experimentation

The strategy used depends on the experimental environment, which includes the objectives of the experimental program. Criteria that can be used to characterize the experimental environment are outlined in Table 2.

These characteristics involve program objectives, the nature of the factors (Xs) and responses (Ys), resources available, quality of the information to be developed, and the theory available to guide the experiment design and analysis. A careful diagnosis of the experimental environment along these lines
can have a major effect on the success of the experimental program.

Table 3 summarizes some tips and traps that can improve the effectiveness of your experimentation.

The critical issues include strategic thinking, identifying and understanding interactions, running confirmation experiments to check model validity, importance of variation and its relation to process understanding and risk, and process robustness and the measurement system.

Paying attention to these tips and traps which are based on extensive use of the approach can go a long way to speeding up the development process and getting useful results.

Conclusion

Over the years we have learned that experimentation can be used to improve all types of processes, manufacturing and service processes alike.  As with any endeavor it is important to have a disciplined, systematic approach and a strategy to guide your work. Sequential experimentation, sometimes involving several phases of the SCO strategy, has been a high-yield approach to guide experimentation. Integrated with the approach is the diagnosis of the experimental environment, which helps define the appropriate strategy. The approach and its associated tips and traps have stood the test of time and are worthy of your consideration.

References
Aggarwal, V. K., A. C. Staubitz and M. Owen (2006). “Optimization of the Mizoroki - Heck Reaction Using Design of Experiments (DOE)," Organic Process Research and Development, Vol. 10, 64-69.

Covey, Stephen R. (1989) The 7 Habits of Highly effective People - Powerful Lessons in Personal Change, Simon and Schuster, New York, NY.

Hulbert, M. H., L. C. Feely, E. L. Inman, A. D. Johnson, A. S. Kearney, J. Michaels, M. Mitchell and E. Zour (2008) “Risk Management in Pharmaceutical Product Development," White Paper Prepared by the PhRMA Drug Product Technology Group, Journal of Pharmaceutical Innovation, 3: 227-248.

International Committee on Harmonization (2005) "ICH Harmonized Tripartite Guideline: Pharmaceutical Development," Q8, Current
Step 4 Version, and November 10, 2005.

Schweitzer, M., M. Pohl, M. Hanna-Brown, P. Nethercote, P. Borman, G. Hanson, K. Smith, J. Larew (2010) “Implications and Opportunities of Applying QbD Principles to Analytical Measurements," Position Paper: QbD Analytics, Pharmaceutical Technology, February 2010, 52-59

Snee, R. D., P. Cini, J. J. Kamm and C. Meyers (2008) “Quality by Design - Shortening the Path to Acceptance," Pharma Processing, February 2008.

Snee, R. D. (2009a) “Quality by Design - Four Years and Three Myths Later," Pharmaceutical Processing, February 2009, 14-16.

Snee, R. D. (2009b) “Building a Framework for Quality by Design," Pharmaceutical Technology Online, October 2009.

Snee, R. D. (2009c) “'Raising Your Batting Average' Remember the Importance of Strategy in Experimentation," Quality Progress, December 2009, 64-68.

Yan, L. and M. Le-he (2007) “Optimization of Fermentation Conditions for P450 BM- 3 Monooxygenase Production by Hybrid Design Methodology," Journal of Zhejian University SCIENCE B, 8(1): 27-32.

About the author:
Ronald D. Snee, PhD is founder and president of Snee Associates, a firm dedicated to the successful implementation of process and
organizational improvement initiatives. He provides guidance to senior executives in their pursuit of improved business performance
using QbD, Lean Six Sigma, and other improvement approaches. Ron received his BA from Washington and Jefferson College and MS
and PhD degrees from Rutgers University. He is a frequent speaker and has published four books and more than 200 papers.

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