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LyoPRONTO: an Open-Source Lyophilization Process Optimization Tool
This work presents a new user-friendly lyophilization simulation and process optimization tool, freely available under the name LyoPRONTO. This tool comprises freezing and primary drying calculators, a design-space generator, and a primary drying optimizer. The freezing calculator performs 0D lumped...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6823652/ https://www.ncbi.nlm.nih.gov/pubmed/31673810 http://dx.doi.org/10.1208/s12249-019-1532-7 |
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author | Shivkumar, Gayathri Kazarin, Petr S. Strongrich, Andrew D. Alexeenko, Alina A. |
author_facet | Shivkumar, Gayathri Kazarin, Petr S. Strongrich, Andrew D. Alexeenko, Alina A. |
author_sort | Shivkumar, Gayathri |
collection | PubMed |
description | This work presents a new user-friendly lyophilization simulation and process optimization tool, freely available under the name LyoPRONTO. This tool comprises freezing and primary drying calculators, a design-space generator, and a primary drying optimizer. The freezing calculator performs 0D lumped capacitance modeling to predict the product temperature variation with time which shows reasonably good agreement with experimental measurements. The primary drying calculator performs 1D heat and mass transfer analysis in a vial and predicts the drying time with an average deviation of 3% from experiments. The calculator is also extended to generate a design space over a range of chamber pressures and shelf temperatures to predict the most optimal setpoints for operation. This optimal setpoint varies with time due to the continuously varying product resistance and is taken into account by the optimizer which provides varying chamber pressure and shelf temperature profiles as a function of time to minimize the primary drying time and thereby, the operational cost. The optimization results in 62% faster primary drying for 5% mannitol and 50% faster primary drying for 5% sucrose solutions when compared with typical cycle conditions. This optimization paves the way for the design of the next generation of lyophilizers which when coupled with accurate sensor networks and control systems can result in self-driving freeze dryers. |
format | Online Article Text |
id | pubmed-6823652 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-68236522019-11-06 LyoPRONTO: an Open-Source Lyophilization Process Optimization Tool Shivkumar, Gayathri Kazarin, Petr S. Strongrich, Andrew D. Alexeenko, Alina A. AAPS PharmSciTech Research Article This work presents a new user-friendly lyophilization simulation and process optimization tool, freely available under the name LyoPRONTO. This tool comprises freezing and primary drying calculators, a design-space generator, and a primary drying optimizer. The freezing calculator performs 0D lumped capacitance modeling to predict the product temperature variation with time which shows reasonably good agreement with experimental measurements. The primary drying calculator performs 1D heat and mass transfer analysis in a vial and predicts the drying time with an average deviation of 3% from experiments. The calculator is also extended to generate a design space over a range of chamber pressures and shelf temperatures to predict the most optimal setpoints for operation. This optimal setpoint varies with time due to the continuously varying product resistance and is taken into account by the optimizer which provides varying chamber pressure and shelf temperature profiles as a function of time to minimize the primary drying time and thereby, the operational cost. The optimization results in 62% faster primary drying for 5% mannitol and 50% faster primary drying for 5% sucrose solutions when compared with typical cycle conditions. This optimization paves the way for the design of the next generation of lyophilizers which when coupled with accurate sensor networks and control systems can result in self-driving freeze dryers. Springer International Publishing 2019-10-31 /pmc/articles/PMC6823652/ /pubmed/31673810 http://dx.doi.org/10.1208/s12249-019-1532-7 Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Research Article Shivkumar, Gayathri Kazarin, Petr S. Strongrich, Andrew D. Alexeenko, Alina A. LyoPRONTO: an Open-Source Lyophilization Process Optimization Tool |
title | LyoPRONTO: an Open-Source Lyophilization Process Optimization Tool |
title_full | LyoPRONTO: an Open-Source Lyophilization Process Optimization Tool |
title_fullStr | LyoPRONTO: an Open-Source Lyophilization Process Optimization Tool |
title_full_unstemmed | LyoPRONTO: an Open-Source Lyophilization Process Optimization Tool |
title_short | LyoPRONTO: an Open-Source Lyophilization Process Optimization Tool |
title_sort | lyopronto: an open-source lyophilization process optimization tool |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6823652/ https://www.ncbi.nlm.nih.gov/pubmed/31673810 http://dx.doi.org/10.1208/s12249-019-1532-7 |
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