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rAAV Manufacturing: The Challenges of Soft Sensing during Upstream Processing

Recombinant adeno-associated virus (rAAV) is the most effective viral vector technology for directly translating the genomic revolution into medicinal therapies. However, the manufacturing of rAAV viral vectors remains challenging in the upstream processing with low rAAV yield in large-scale product...

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Detalles Bibliográficos
Autores principales: Iglesias, Cristovão Freitas, Ristovski, Milica, Bolic, Miodrag, Cuperlovic-Culf, Miroslava
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9951952/
https://www.ncbi.nlm.nih.gov/pubmed/36829723
http://dx.doi.org/10.3390/bioengineering10020229
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author Iglesias, Cristovão Freitas
Ristovski, Milica
Bolic, Miodrag
Cuperlovic-Culf, Miroslava
author_facet Iglesias, Cristovão Freitas
Ristovski, Milica
Bolic, Miodrag
Cuperlovic-Culf, Miroslava
author_sort Iglesias, Cristovão Freitas
collection PubMed
description Recombinant adeno-associated virus (rAAV) is the most effective viral vector technology for directly translating the genomic revolution into medicinal therapies. However, the manufacturing of rAAV viral vectors remains challenging in the upstream processing with low rAAV yield in large-scale production and high cost, limiting the generalization of rAAV-based treatments. This situation can be improved by real-time monitoring of critical process parameters (CPP) that affect critical quality attributes (CQA). To achieve this aim, soft sensing combined with predictive modeling is an important strategy that can be used for optimizing the upstream process of rAAV production by monitoring critical process variables in real time. However, the development of soft sensors for rAAV production as a fast and low-cost monitoring approach is not an easy task. This review article describes four challenges and critically discusses the possible solutions that can enable the application of soft sensors for rAAV production monitoring. The challenges from a data scientist’s perspective are (i) a predictor variable (soft-sensor inputs) set without AAV viral titer, (ii) multi-step forecasting, (iii) multiple process phases, and (iv) soft-sensor development composed of the mechanistic model.
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spelling pubmed-99519522023-02-25 rAAV Manufacturing: The Challenges of Soft Sensing during Upstream Processing Iglesias, Cristovão Freitas Ristovski, Milica Bolic, Miodrag Cuperlovic-Culf, Miroslava Bioengineering (Basel) Review Recombinant adeno-associated virus (rAAV) is the most effective viral vector technology for directly translating the genomic revolution into medicinal therapies. However, the manufacturing of rAAV viral vectors remains challenging in the upstream processing with low rAAV yield in large-scale production and high cost, limiting the generalization of rAAV-based treatments. This situation can be improved by real-time monitoring of critical process parameters (CPP) that affect critical quality attributes (CQA). To achieve this aim, soft sensing combined with predictive modeling is an important strategy that can be used for optimizing the upstream process of rAAV production by monitoring critical process variables in real time. However, the development of soft sensors for rAAV production as a fast and low-cost monitoring approach is not an easy task. This review article describes four challenges and critically discusses the possible solutions that can enable the application of soft sensors for rAAV production monitoring. The challenges from a data scientist’s perspective are (i) a predictor variable (soft-sensor inputs) set without AAV viral titer, (ii) multi-step forecasting, (iii) multiple process phases, and (iv) soft-sensor development composed of the mechanistic model. MDPI 2023-02-08 /pmc/articles/PMC9951952/ /pubmed/36829723 http://dx.doi.org/10.3390/bioengineering10020229 Text en © 2023 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 Review
Iglesias, Cristovão Freitas
Ristovski, Milica
Bolic, Miodrag
Cuperlovic-Culf, Miroslava
rAAV Manufacturing: The Challenges of Soft Sensing during Upstream Processing
title rAAV Manufacturing: The Challenges of Soft Sensing during Upstream Processing
title_full rAAV Manufacturing: The Challenges of Soft Sensing during Upstream Processing
title_fullStr rAAV Manufacturing: The Challenges of Soft Sensing during Upstream Processing
title_full_unstemmed rAAV Manufacturing: The Challenges of Soft Sensing during Upstream Processing
title_short rAAV Manufacturing: The Challenges of Soft Sensing during Upstream Processing
title_sort raav manufacturing: the challenges of soft sensing during upstream processing
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9951952/
https://www.ncbi.nlm.nih.gov/pubmed/36829723
http://dx.doi.org/10.3390/bioengineering10020229
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