Literature DB >> 31004653

Freeze-Dryer Equipment Capability Limit: Comparison of Computational Modeling With Experiments at Laboratory Scale.

Gayathri Shivkumar1, Vaibhav Kshirsagar1, Tong Zhu1, Israel B Sebastiao1, Steven L Nail2, Gregory A Sacha2, Alina A Alexeenko3.   

Abstract

The equipment capability curve is one of the bounding elements of the freeze-drying design space, and understanding it is critical to process design, transfer, and scale-up. The second bounding element of the design space is the product temperature limit beyond which the product collapses. The high cost associated with freeze-drying any product renders it crucial to operate using the most efficient cycle within the limits of the equipment and the product. In this work, we present a computational model to generate the equipment capability curve for 2 laboratory scale freeze-dryers and compare the results to experimentally generated equipment capability curves. The average deviations of the modeling results from the experiments for the 2 lyophilizers modeled are -4.8% and -7.2%. In addition, we investigate the effect of various numerical and geometric parameters on the simulated equipment capability. Among the numerical parameters, the chamber wall thermal boundary conditions exert the largest influence with a maximum value of 12.3%. Among the geometric parameters, the inclusion of the isolation valve reduces the equipment capability by 23.7%. Larger isolation valves, required for controlled nucleation technology, choke the flow in the duct at lower sublimation rates, thereby lowering the equipment capability limit.
Copyright © 2019. Published by Elsevier Inc.

Keywords:  freeze-drying; lyophilization; quality by design (QBD); thermodynamics; tunable diode laser absorption spectroscopy (TDLAS)

Year:  2019        PMID: 31004653     DOI: 10.1016/j.xphs.2019.04.016

Source DB:  PubMed          Journal:  J Pharm Sci        ISSN: 0022-3549            Impact factor:   3.534


  2 in total

1.  Recommended Best Practices for Lyophilization Validation-2021 Part I: Process Design and Modeling.

Authors:  Feroz Jameel; Alina Alexeenko; Akhilesh Bhambhani; Gregory Sacha; Tong Zhu; Serguei Tchessalov; Lokesh Kumar; Puneet Sharma; Ehab Moussa; Lavanya Iyer; Rui Fang; Jayasree Srinivasan; Ted Tharp; Joseph Azzarella; Petr Kazarin; Mehfouz Jalal
Journal:  AAPS PharmSciTech       Date:  2021-08-18       Impact factor: 3.246

2.  LyoPRONTO: an Open-Source Lyophilization Process Optimization Tool.

Authors:  Gayathri Shivkumar; Petr S Kazarin; Andrew D Strongrich; Alina A Alexeenko
Journal:  AAPS PharmSciTech       Date:  2019-10-31       Impact factor: 3.246

  2 in total

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