Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
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
_version_ 1783728412012052480
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
work_keys_str_mv AT peddapatlaraghuvg processmodelapproachtopredicttabletweightvariabilityfordirectcompressionformulationsatpilotandproductionscale
AT sheridangerard processmodelapproachtopredicttabletweightvariabilityfordirectcompressionformulationsatpilotandproductionscale
AT slevinconor processmodelapproachtopredicttabletweightvariabilityfordirectcompressionformulationsatpilotandproductionscale
AT swaminathanshrikant processmodelapproachtopredicttabletweightvariabilityfordirectcompressionformulationsatpilotandproductionscale
AT browningivan processmodelapproachtopredicttabletweightvariabilityfordirectcompressionformulationsatpilotandproductionscale
AT oreillyclare processmodelapproachtopredicttabletweightvariabilityfordirectcompressionformulationsatpilotandproductionscale
AT workuzelalema processmodelapproachtopredicttabletweightvariabilityfordirectcompressionformulationsatpilotandproductionscale
AT egandavid processmodelapproachtopredicttabletweightvariabilityfordirectcompressionformulationsatpilotandproductionscale
AT sheehanstephen processmodelapproachtopredicttabletweightvariabilityfordirectcompressionformulationsatpilotandproductionscale
AT creanabinam processmodelapproachtopredicttabletweightvariabilityfordirectcompressionformulationsatpilotandproductionscale