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Process Model Approach to Predict Tablet Weight Variability for Direct Compression Formulations at Pilot and Production Scale
Optimizing processing conditions to achieve a critical quality attribute (CQA) is an integral part of pharmaceutical quality by design (QbD). It identifies combinations of material and processing parameters ensuring that processing conditions achieve a targeted CQA. Optimum processing conditions are...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8308976/ https://www.ncbi.nlm.nih.gov/pubmed/34371725 http://dx.doi.org/10.3390/pharmaceutics13071033 |
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author | Peddapatla, Raghu V. G. Sheridan, Gerard Slevin, Conor Swaminathan, Shrikant Browning, Ivan O’Reilly, Clare Worku, Zelalem A. Egan, David Sheehan, Stephen Crean, Abina M. |
author_facet | Peddapatla, Raghu V. G. Sheridan, Gerard Slevin, Conor Swaminathan, Shrikant Browning, Ivan O’Reilly, Clare Worku, Zelalem A. Egan, David Sheehan, Stephen Crean, Abina M. |
author_sort | Peddapatla, Raghu V. G. |
collection | PubMed |
description | Optimizing processing conditions to achieve a critical quality attribute (CQA) is an integral part of pharmaceutical quality by design (QbD). It identifies combinations of material and processing parameters ensuring that processing conditions achieve a targeted CQA. Optimum processing conditions are formulation and equipment-dependent. Therefore, it is challenging to translate a process design between formulations, pilot-scale and production-scale equipment. In this study, an empirical model was developed to determine optimum processing conditions for direct compression formulations with varying flow properties, across pilot- and production-scale tablet presses. The CQA of interest was tablet weight variability, expressed as percentage relative standard deviation. An experimental design was executed for three model placebo blends with varying flow properties. These blends were compacted on one pilot-scale and two production-scale presses. The process model developed enabled the optimization of processing parameters for each formulation, on each press, with respect to a target tablet weight variability of <1%RSD. The model developed was successfully validated using data for additional placebo and active formulations. Validation formulations were benchmarked to formulations used for model development, employing permeability index values to indicate blend flow. |
format | Online Article Text |
id | pubmed-8308976 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-83089762021-07-25 Process Model Approach to Predict Tablet Weight Variability for Direct Compression Formulations at Pilot and Production Scale Peddapatla, Raghu V. G. Sheridan, Gerard Slevin, Conor Swaminathan, Shrikant Browning, Ivan O’Reilly, Clare Worku, Zelalem A. Egan, David Sheehan, Stephen Crean, Abina M. Pharmaceutics Article Optimizing processing conditions to achieve a critical quality attribute (CQA) is an integral part of pharmaceutical quality by design (QbD). It identifies combinations of material and processing parameters ensuring that processing conditions achieve a targeted CQA. Optimum processing conditions are formulation and equipment-dependent. Therefore, it is challenging to translate a process design between formulations, pilot-scale and production-scale equipment. In this study, an empirical model was developed to determine optimum processing conditions for direct compression formulations with varying flow properties, across pilot- and production-scale tablet presses. The CQA of interest was tablet weight variability, expressed as percentage relative standard deviation. An experimental design was executed for three model placebo blends with varying flow properties. These blends were compacted on one pilot-scale and two production-scale presses. The process model developed enabled the optimization of processing parameters for each formulation, on each press, with respect to a target tablet weight variability of <1%RSD. The model developed was successfully validated using data for additional placebo and active formulations. Validation formulations were benchmarked to formulations used for model development, employing permeability index values to indicate blend flow. MDPI 2021-07-07 /pmc/articles/PMC8308976/ /pubmed/34371725 http://dx.doi.org/10.3390/pharmaceutics13071033 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Peddapatla, Raghu V. G. Sheridan, Gerard Slevin, Conor Swaminathan, Shrikant Browning, Ivan O’Reilly, Clare Worku, Zelalem A. Egan, David Sheehan, Stephen Crean, Abina M. Process Model Approach to Predict Tablet Weight Variability for Direct Compression Formulations at Pilot and Production Scale |
title | Process Model Approach to Predict Tablet Weight Variability for Direct Compression Formulations at Pilot and Production Scale |
title_full | Process Model Approach to Predict Tablet Weight Variability for Direct Compression Formulations at Pilot and Production Scale |
title_fullStr | Process Model Approach to Predict Tablet Weight Variability for Direct Compression Formulations at Pilot and Production Scale |
title_full_unstemmed | Process Model Approach to Predict Tablet Weight Variability for Direct Compression Formulations at Pilot and Production Scale |
title_short | Process Model Approach to Predict Tablet Weight Variability for Direct Compression Formulations at Pilot and Production Scale |
title_sort | process model approach to predict tablet weight variability for direct compression formulations at pilot and production scale |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8308976/ https://www.ncbi.nlm.nih.gov/pubmed/34371725 http://dx.doi.org/10.3390/pharmaceutics13071033 |
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