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Big data collection in pharmaceutical manufacturing and its use for product quality predictions

Advances in data science and digitalization are transforming the world, and the pharmaceutical industry is no exception. Multiple sensor-equipped manufacturing processes and laboratory analysis are the main sources of primary data, which have been utilized for the presented dataset of 1005 actual pr...

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Detalles Bibliográficos
Autores principales: Žagar, Janja, Mihelič, Jurij
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8943063/
https://www.ncbi.nlm.nih.gov/pubmed/35322032
http://dx.doi.org/10.1038/s41597-022-01203-x
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author Žagar, Janja
Mihelič, Jurij
author_facet Žagar, Janja
Mihelič, Jurij
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description Advances in data science and digitalization are transforming the world, and the pharmaceutical industry is no exception. Multiple sensor-equipped manufacturing processes and laboratory analysis are the main sources of primary data, which have been utilized for the presented dataset of 1005 actual production batches of selected medicine. This dataset includes incoming raw material quality results, compression process time series and final product quality results for the selected product. The data is highly valuable for it provides an insight into every 10 seconds of the process trajectory for 1005 actual production batches along with product quality collected over several years. It therefore offers an opportunity to develop advanced analysis models and procedures which would lead to the omission of current conventional and time consuming laboratory testing. Benefits for both the industry and patient are obvious: reducing product lead times and costs of manufacture.
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spelling pubmed-89430632022-04-08 Big data collection in pharmaceutical manufacturing and its use for product quality predictions Žagar, Janja Mihelič, Jurij Sci Data Data Descriptor Advances in data science and digitalization are transforming the world, and the pharmaceutical industry is no exception. Multiple sensor-equipped manufacturing processes and laboratory analysis are the main sources of primary data, which have been utilized for the presented dataset of 1005 actual production batches of selected medicine. This dataset includes incoming raw material quality results, compression process time series and final product quality results for the selected product. The data is highly valuable for it provides an insight into every 10 seconds of the process trajectory for 1005 actual production batches along with product quality collected over several years. It therefore offers an opportunity to develop advanced analysis models and procedures which would lead to the omission of current conventional and time consuming laboratory testing. Benefits for both the industry and patient are obvious: reducing product lead times and costs of manufacture. Nature Publishing Group UK 2022-03-23 /pmc/articles/PMC8943063/ /pubmed/35322032 http://dx.doi.org/10.1038/s41597-022-01203-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Data Descriptor
Žagar, Janja
Mihelič, Jurij
Big data collection in pharmaceutical manufacturing and its use for product quality predictions
title Big data collection in pharmaceutical manufacturing and its use for product quality predictions
title_full Big data collection in pharmaceutical manufacturing and its use for product quality predictions
title_fullStr Big data collection in pharmaceutical manufacturing and its use for product quality predictions
title_full_unstemmed Big data collection in pharmaceutical manufacturing and its use for product quality predictions
title_short Big data collection in pharmaceutical manufacturing and its use for product quality predictions
title_sort big data collection in pharmaceutical manufacturing and its use for product quality predictions
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8943063/
https://www.ncbi.nlm.nih.gov/pubmed/35322032
http://dx.doi.org/10.1038/s41597-022-01203-x
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