A quality by design approach for longitudinal quality attributes

[en] The concept of quality by design (QbD) as published in ICH-Q8 is currently one of the most recurrent topics in the pharmaceutical literature. This guideline recommends the use of information and prior knowledge gathered during pharmaceutical development studies to provide a scientific rationale for the manufacturing process of a product and provide guarantee of future quality. This poses several challenges from a statistical standpoint and requires a shift in paradigm from traditional statistical practices. First, to provide “assurance of quality” of future lots implies the need to make predictions regarding the quality given past evidence and data. Second, the quality attributes described in the Q8 guidelines are not always a set of unique, independent measurements. In many cases, these criteria are com- plicated longitudinal data with successive acceptance criteria over a defined period of time. A common example is a dissolution profile for a modified or extended-release solid dosage form that must fall within acceptance limits at several time points. A Bayesian approach for longitudinal data obtained in various conditions of a design of experiment is provided to elegantly address the ICH-Q8 recommendation to provide assurance of quality and derive a scientifically sound design space.

Disciplines :

Physical, chemical, mathematical & earth Sciences: Multidisciplinary, general & others

Author, co-author :

Lebrun, Pierre ; Arlenda / Université de Liège > Département de pharmacie > Chimie analytique

Giacoletti, Katherine; Arlenda Inc.

Scherder, Tara; Arlenda Inc.

Rozet, Eric ; Arlenda / Université de Liège > Département de pharmacie > Chimie analytique

Boulanger, Bruno ; Arlenda / Université de Liège > Département de pharmacie > Département de pharmacie

Language :

English

Title :

A quality by design approach for longitudinal quality attributes

Publication date :

2015

Journal title :

Journal of Biopharmaceutical Statistics

ISSN :

1054-3406

eISSN :

1520-5711

Publisher :

Taylor & Francis, Philadelphia, United States - Pennsylvania

Volume :

Pages :

1-13

Peer reviewed :

Peer Reviewed verified by ORBi

Available on ORBi :

since 28 January 2015

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Bibliography

Box, G. E. P., Tiao, G. C. (1973). Bayesian Inference in Statistical Analysis. New York, NY: Wiley Classic Library.
Chib, S., Carlin, B. P. (1999). On MCMC sampling in hierarchical longitudinal models. Statistics and Computing. 9:17-26.
Deming, W. E. (1986). Out of the Crisis. Cambridge, MA: Massachusetts Institute of Technology Center for Advanced Engineering Study.
Guttman, I. (1970). Statistical Tolerance Regions: Classical and Bayesian. Griffin's statistical monographs and courses. London: Griffin.
Hamada, M., Johnson, V., Moore, L. M. (2004). Bayesian prediction intervals and their relationship to tolerance intervals. Technometrics 46:452-459.
ICH-Q2R Guideline. (2005). International Conference on Harmonization (ICH) of Technical Requirements for registration of Pharmaceuticals for Human Use, Topic Q2 (R1): Validation of Analytical Procedures: Text and Methodology, Geneva, 2005.
ICH Q8. (2009). International Conference on Harmonization (ICH) of Technical Requirements for Registration of Pharmaceuticals for Human Use, Topic Q8 (R2): Pharmaceutical Development, Geneva.
ICH Q9. (2005). International Conference on Harmonization (ICH) of Technical Requirements for Registration of Pharmaceuticals for Human Use, Topic Q9: Quality Risk Management, Geneva.
ICH Q10. (2008). International Conference on Harmonization (ICH) of Technical Requirements for Registration of Pharmaceuticals for Human Use, Topic Q10: Pharmaceutical Quality System, Geneva.
Juran, J. M. (1951). Quality Control Handbook, 1st ed. New York: McGraw-Hill.
Lebrun, P., Boulanger, B., Debrus, B., Lambert, P., Hubert, P. (2013). A Bayesian design space for analytical methods based on multivariate models and predictions. Journal of Biopharmaceutical Statistics. 23:186-198.
Miró-Quesada, G., del Castillo, E., Peterson, J. J. (2004). A Bayesian approach for multiple response surface optimization in the presence of noise variable. Journal of Applied Statistics 13:251-270.
Peterson, J. J. (2004). A posterior predictive approach to multiple response surface optimization. Journal Quality Technology 36:139-153.
Peterson, J. J. (2008). A Bayesian approach to the ICH Q8 definition of design Space. Journal Biopharmaceutical Statistics 18:959-975.
Peterson, J. J., Lief, K. (2010). The ICH Q8 definition of design space: A comparison of the overlapping means and the Bayesian predictive approaches. Statistics in Biopharmaceutical Research 2:249-259.
Peterson, J. J., Miró-Quesada, G., del Castillo, E. (2009). A Bayesian reliability approach to multiple response optimization with seemingly unrelated regression models. Journal of Quality Technology Quantitative Management 6:353-369.
Peterson, J. J., Yahyah, M. (2009). A Bayesian design space approach to robustness and system suitability for pharmaceutical assays and other processes. Statistics in Biopharmaceutical Research 1:441-449.
U.S. Food and Drug Administration (FDA). (1997a). Guidance for Industry: Dissolution Testing of Immediate Release Solid Oral Dosage Forms. August 1997.
U.S. Food and Drug Administration (FDA). (1997b). Guidance for Industry: SUPAC-MR. Modified Release Solid Oral Dosage Forms. September 1997.
U.S. Food and Drug Administration (FDA). (2007). Department of Health and Human Services, Pharmaceutical Quality for the 21st Century A Risk-Based Approach Progress Report, May 2007.
U.S. Food and Drug Administration (FDA). (2011a). Applying ICH Q8(R2), Q9, and Q10 principles to CMC review. Chapter 5000 - Pharmaceutical Sciences, MAPP 5016.1, February 2011.
U.S. Food and Drug Administration (FDA). (2011b). Department of Health and Human Services, Guidance for industry; Process Validation: General Principles and Practices, January 2011.
USP 32-NF 27 (2009). Chapter <711> Dissolution, August 2009.
Vaghela, B., Kayastha, R., Bhatt, N., Pathak, N., Rathod, D. (2011). Development and validation of dissolution procedures. Journal of Applied Pharmaceutical Science 01:50-56.
Yu, L. (2008). Pharmaceutical quality by design: product and process development, understanding, and control. Pharmaceutical Research, 25:781-791.