Population Balance Model Development, Validation, and Prediction of CQAs of a High-Shear Wet Granulation Process: Towards QbD in Drug Product Pharmaceutical Manufacturing
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This paper focuses on the predictive model development for a pharmaceutically relevant model granulation process. A population balance modeling (PBM) framework has been employed for modeling purposes which is then utilized to obtain accurate predictions of the process. The model is aligned to adequately describe the high-shear mode of granulation operation in a batch process. The model is calibrated using the particle swarm algorithm (PSA) in the form of a multiobjective optimization problem. The multiobjective optimization problem was implemented based on the ε-constraint method which involves the handling of multiple cost functions in the form of constraints with the minimization of one primary objective function from the entire set of cost functions. The resultant solutions obtained from the model are Pareto optimal. The effects of the impeller speed, liquid-to-solid ratio, and wet massing time on the particle size distributions were characterized, and predicted size distributions were in agreement with experimental results. The predictive model framework lends itself to the quality by design (QbD) initiative undertaken by the US Food and Drug Administration (US FDA).
KeywordsGranulation Multidimensional population balance model QbD Predictive modeling Multiobjective optimization Particle size distribution
We are grateful to the National Institute for Pharmaceutical Technology and Education (NIPTE) and the US Food and Drug Administration (FDA) for providing funds for this research. This study was funded by the FDA-sponsored Grant “Critical Path Manufacturing Sector Research Initiative (5U01FD004275-02).” This work was also supported by the National Science Foundation Engineering Research Center on Structured Organic Particulate Systems, via Grant NSF-ECC 0540855. The authors also thank Dilbir Bindra and Jing Tao from Bristol-Myers Squibb for their contributions to this work. The support from various FDA managers including Dr. Mansoor Khan, Dr. Richard Lostritto, Dr. Vincent Vilker, and Mr. Jon E. Clark, and insightful FDA internal review by Dr. Cindy Buhse are acknowledged.
The views and opinions expressed in this work are only of the authors and do not necessarily reflect the policy or statement of the US FDA.
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