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Multi‐criteria manufacturability indices for ranking high‐concentration monoclonal antibody formulations

The need for high‐concentration formulations for subcutaneous delivery of therapeutic monoclonal antibodies (mAbs) can present manufacturability challenges for the final ultrafiltration/diafiltration (UF/DF) step. Viscosity levels and the propensity to aggregate are key considerations for high‐conce...

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Detalles Bibliográficos
Autores principales: Yang, Yang, Velayudhan, Ajoy, Thornhill, Nina F., Farid, Suzanne S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5575515/
https://www.ncbi.nlm.nih.gov/pubmed/28464235
http://dx.doi.org/10.1002/bit.26329
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author Yang, Yang
Velayudhan, Ajoy
Thornhill, Nina F.
Farid, Suzanne S.
author_facet Yang, Yang
Velayudhan, Ajoy
Thornhill, Nina F.
Farid, Suzanne S.
author_sort Yang, Yang
collection PubMed
description The need for high‐concentration formulations for subcutaneous delivery of therapeutic monoclonal antibodies (mAbs) can present manufacturability challenges for the final ultrafiltration/diafiltration (UF/DF) step. Viscosity levels and the propensity to aggregate are key considerations for high‐concentration formulations. This work presents novel frameworks for deriving a set of manufacturability indices related to viscosity and thermostability to rank high‐concentration mAb formulation conditions in terms of their ease of manufacture. This is illustrated by analyzing published high‐throughput biophysical screening data that explores the influence of different formulation conditions (pH, ions, and excipients) on the solution viscosity and product thermostability. A decision tree classification method, CART (Classification and Regression Tree) is used to identify the critical formulation conditions that influence the viscosity and thermostability. In this work, three different multi‐criteria data analysis frameworks were investigated to derive manufacturability indices from analysis of the stress maps and the process conditions experienced in the final UF/DF step. Polynomial regression techniques were used to transform the experimental data into a set of stress maps that show viscosity and thermostability as functions of the formulation conditions. A mathematical filtrate flux model was used to capture the time profiles of protein concentration and flux decay behavior during UF/DF. Multi‐criteria decision‐making analysis was used to identify the optimal formulation conditions that minimize the potential for both viscosity and aggregation issues during UF/DF. Biotechnol. Bioeng. 2017;114: 2043–2056. © 2017 The Authors. Biotechnology and Bioengineering Published by Wiley Perodicals, Inc.
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spelling pubmed-55755152017-09-18 Multi‐criteria manufacturability indices for ranking high‐concentration monoclonal antibody formulations Yang, Yang Velayudhan, Ajoy Thornhill, Nina F. Farid, Suzanne S. Biotechnol Bioeng Articles The need for high‐concentration formulations for subcutaneous delivery of therapeutic monoclonal antibodies (mAbs) can present manufacturability challenges for the final ultrafiltration/diafiltration (UF/DF) step. Viscosity levels and the propensity to aggregate are key considerations for high‐concentration formulations. This work presents novel frameworks for deriving a set of manufacturability indices related to viscosity and thermostability to rank high‐concentration mAb formulation conditions in terms of their ease of manufacture. This is illustrated by analyzing published high‐throughput biophysical screening data that explores the influence of different formulation conditions (pH, ions, and excipients) on the solution viscosity and product thermostability. A decision tree classification method, CART (Classification and Regression Tree) is used to identify the critical formulation conditions that influence the viscosity and thermostability. In this work, three different multi‐criteria data analysis frameworks were investigated to derive manufacturability indices from analysis of the stress maps and the process conditions experienced in the final UF/DF step. Polynomial regression techniques were used to transform the experimental data into a set of stress maps that show viscosity and thermostability as functions of the formulation conditions. A mathematical filtrate flux model was used to capture the time profiles of protein concentration and flux decay behavior during UF/DF. Multi‐criteria decision‐making analysis was used to identify the optimal formulation conditions that minimize the potential for both viscosity and aggregation issues during UF/DF. Biotechnol. Bioeng. 2017;114: 2043–2056. © 2017 The Authors. Biotechnology and Bioengineering Published by Wiley Perodicals, Inc. John Wiley and Sons Inc. 2017-05-26 2017-09 /pmc/articles/PMC5575515/ /pubmed/28464235 http://dx.doi.org/10.1002/bit.26329 Text en © 2017 The Authors. Biotechnology and Bioengineering Published by Wiley Perodicals, Inc. This is an open access article under the terms of the Creative Commons Attribution (http://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Articles
Yang, Yang
Velayudhan, Ajoy
Thornhill, Nina F.
Farid, Suzanne S.
Multi‐criteria manufacturability indices for ranking high‐concentration monoclonal antibody formulations
title Multi‐criteria manufacturability indices for ranking high‐concentration monoclonal antibody formulations
title_full Multi‐criteria manufacturability indices for ranking high‐concentration monoclonal antibody formulations
title_fullStr Multi‐criteria manufacturability indices for ranking high‐concentration monoclonal antibody formulations
title_full_unstemmed Multi‐criteria manufacturability indices for ranking high‐concentration monoclonal antibody formulations
title_short Multi‐criteria manufacturability indices for ranking high‐concentration monoclonal antibody formulations
title_sort multi‐criteria manufacturability indices for ranking high‐concentration monoclonal antibody formulations
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5575515/
https://www.ncbi.nlm.nih.gov/pubmed/28464235
http://dx.doi.org/10.1002/bit.26329
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