(565a) Residence Time Distribution and Segregation Studies Trough Real Time Measurements By Near Infrared Spectroscopy | AIChE

(565a) Residence Time Distribution and Segregation Studies Trough Real Time Measurements By Near Infrared Spectroscopy

Authors 

Roman-Ospino, A. - Presenter, Rutgers, The State University of New Jersey
Oka, S., Rutgers, The State University of New Jersey
Moghtadernejad, S., Rutgers University
Escotet-Espinoza, M. S., Rutgers, The State University of New Jersey
Singh, R., Rutgers, The State University of New Jer
Ramachandran, R., Rutgers University
Ierapetritou, M., Rutgers, The State University of New Jersey
Muzzio, F., Rutgers, The State University of New Jersey
Currently, pharmaceutical companies are moving from batch to continuous manufacturing. Blender is a critical unit operation of continuous pharmaceutical manufacturing process that assures the blend uniformity and thereby drug content in final product [1]. The near infrared (NIR) spectroscopy is an important tool to measure the drug concentration at blender outlet and therefore to characterize the continuous blender [2-4]. The characterization of a continuous blender was achieved through residence time distribution (RTD) analysis and segregation studies by using a set of pharmaceutical excipients and active ingredients with material properties that defines a principal components analysis score plot.

RTD characterization was performed through in-line measurements by near infrared (NIR) spectroscopy of changes in composition adding a pulse of tracer into the system. Each material used was analyzed by using a continuous blending setup consisting in one feeder connected to a GEA continuous blender through a hopper. At the blender outlet, a chute was connected with a lateral window to control the level of powder with a rotary valve. Perpendicular to the powder flow, a near infrared probe with the light source was connected for spectral acquisition in diffuse reflectance mode. The reflected light is transferred to the interferometer for processing through fiber optic. Six experiments for each material has been performed at tow flow rates and 3 impeller speeds (revolution per minute (RPM)). A second feeder was necessary in cases where low density exceeds the gear capacity of a single feeder setup. Results of in-line RTD measurements demonstrated the selectivity of calibration models and low limit of detection required for analysis of tracers providing critical information for blender characterization.

Segregation studies were conducted for five formulations in continuous blending. For each configuration in blender RPM, samples were collected for further analysis in an ASTM D 6940-04 segregation tester. Near infrared prediction of ten samples collected for each experiment were performed by using 1-Dimentional sampling approach. Calibration models for each formulation were prepared and implemented. Results indicates that each blender configuration has a different impact in the segregation behavior of the API concentration and the NIR method provided a fast and reliable results.

Reference

[1]. Yijie Gao, Aditya Vanarase, Fernando Muzzio, Marianthi Ierapetritou, Characterizing continuous powder mixing using residence time distribution, Chemical Engineering Science, Volume 66, Issue 3, 1 February 2011, Pages 417-425, ISSN 0009-2509, http://doi.org/10.1016/j.ces.2010.10.045.

[2]. Singh, R.; Sahay, A.; Karry, K. M.; Muzzio, F.; Ierapetritou, M.; Ramachandran, R., Implementation of an advanced hybrid MPC–PID control system using PAT tools into a direct compaction continuous pharmaceutical tablet manufacturing pilot plant. International journal of pharmaceutics 2014, 473 (1–2), 38-54.

[3]. Colón, Y. M.; Florian, M. A.; Acevedo, D.; Méndez, R.; Romañach, R. J., Near Infrared Method Development for a Continuous Manufacturing Blending Process. Journal of Pharmaceutical Innovation 2014, 9 (4), 291-301.

[4]. Cárdenas, V.; Cordobés, M.; Blanco, M.; Alcalà, M., Strategy for design NIR calibration sets based on process spectrum and model space: An innovative approach for process analytical technology. Journal of Pharmaceutical and Biomedical Analysis 2015, 114, 28-33.