Defense Date
10-11-2024
Graduation Date
Winter 12-20-2024
Availability
One-year Embargo
Submission Type
dissertation
Degree Name
PhD
Department
Pharmaceutics
School
School of Pharmacy
Committee Chair
Carl A. Anderson
Committee Member
James K. Drennen, III
Committee Member
Ira S. Buckner
Committee Member
Zhenqi Shi
Committee Member
Steven Doherty
Keywords
Near-Infrared Spectroscopy, Process Analytical Technology, Model Robustness, Calibration Burden, PLS Regression, Iterative Optimization Technology, Continuous Manufacturing, Powder Density, Powder Flow
Abstract
A well-defined action plan to respond effectively to sudden changes in product demand is critical for preventing drug shortages within the pharmaceutical industry. An effective way to increase the output of a continuous manufacturing (CM) process is through flow rate adjustments. However, robust analytical methods must be in place to ensure consistent analytical performance across varying flow rates. Existing approaches for mitigating the physical effects of flow rate on Near-Infrared (NIR) measurements are often burdensome. Thus, efficient robust modeling strategies that reduce the current calibration burden and ensure model insensitivity to the physical variations in CM systems are needed. In this dissertation, powder density is identified as an underlying source of spectral variance due to flow rate and used to develop material-sparing calibration and pure component methods with enhanced robustness to scale (i.e. flow rate). The in-line characterization of density variation for continuous powder streams is facilitated by a process analytical technology method consisting of live imaging and dynamic mass characterization. This work proposes a new robust modeling platform based on a data collection strategy for the development of density-insensitive NIR calibrations for drug content. Additionally, a novel extended version of the base iterative optimization technology (IOT) algorithm that includes density information is proposed as a minimal calibration approach for enhancing the flow rate robustness of IOT models. The methods proposed in this dissertation offer a promising alternative to burdensome robust modeling techniques. They aim to ensure consistent NIR analytical performance across variable production scales, while also achieving material efficiency.
Language
English
Recommended Citation
Velez-Silva, N. L. (2024). Efficient Development of Density-Insensitive Near-Infrared Methods for In-Line Drug Content Monitoring in Continuous Powder Streams (Doctoral dissertation, Duquesne University). Retrieved from https://dsc.duq.edu/etd/2406
Additional Citations
Velez, N. L., Drennen, J. K., & Anderson, C. A. (2022). Challenges, opportunities and recent advances in near infrared spectroscopy applications for monitoring blend uniformity in the continuous manufacturing of solid oral dosage forms. International Journal of Pharmaceutics, 615, 121462. doi:https://doi.org/10.1016/j.ijpharm.2022.121462
Velez-Silva, N. L., Drennen, J. K., & Anderson, C. A. (2023). Influence of powder stream density on near infrared measurements upon scale-up of a simulated continuous process. International Journal of Pharmaceutics, 645, 123354. doi:https://doi.org/10.1016/j.ijpharm.2023.123354
Velez-Silva, N. L., Drennen, J. K., & Anderson, C. A. (2024). Continuous manufacturing of pharmaceutical products: A density-insensitive near infrared method for the in-line monitoring of continuous powder streams. International Journal of Pharmaceutics, 650, 123699. doi:https://doi.org/10.1016/j.ijpharm.2023.123699