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Longitudinal drug synergy assessment using convolutional neural network image-decoding of glioblastoma single-spheroid cultures

BACKGROUND: In recent years, drug combinations have become increasingly popular to improve therapeutic outcomes in various diseases, including difficult to cure cancers such as the brain cancer glioblastoma. Assessing the interaction between drugs over time is critical for predicting drug combinatio...

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Autores principales: Giczewska, Anna, Pastuszak, Krzysztof, Houweling, Megan, Abdul, Kulsoom U, Faaij, Noa, Wedekind, Laurine, Noske, David, Wurdinger, Thomas, Supernat, Anna, Westerman, Bart A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10691443/
https://www.ncbi.nlm.nih.gov/pubmed/38047207
http://dx.doi.org/10.1093/noajnl/vdad134
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author Giczewska, Anna
Pastuszak, Krzysztof
Houweling, Megan
Abdul, Kulsoom U
Faaij, Noa
Wedekind, Laurine
Noske, David
Wurdinger, Thomas
Supernat, Anna
Westerman, Bart A
author_facet Giczewska, Anna
Pastuszak, Krzysztof
Houweling, Megan
Abdul, Kulsoom U
Faaij, Noa
Wedekind, Laurine
Noske, David
Wurdinger, Thomas
Supernat, Anna
Westerman, Bart A
author_sort Giczewska, Anna
collection PubMed
description BACKGROUND: In recent years, drug combinations have become increasingly popular to improve therapeutic outcomes in various diseases, including difficult to cure cancers such as the brain cancer glioblastoma. Assessing the interaction between drugs over time is critical for predicting drug combination effectiveness and minimizing the risk of therapy resistance. However, as viability readouts of drug combination experiments are commonly performed as an endpoint where cells are lysed, longitudinal drug-interaction monitoring is currently only possible through combined endpoint assays. METHODS: We provide a method for massive parallel monitoring of drug interactions for 16 drug combinations in 3 glioblastoma models over a time frame of 18 days. In our assay, viabilities of single neurospheres are to be estimated based on image information taken at different time points. Neurosphere images taken on the final day (day 18) were matched to the respective viability measured by CellTiter-Glo 3D on the same day. This allowed to use of machine learning to decode image information to viability values on day 18 as well as for the earlier time points (on days 8, 11, and 15). RESULTS: Our study shows that neurosphere images allow us to predict cell viability from extrapolated viabilities. This enables to assess of the drug interactions in a time window of 18 days. Our results show a clear and persistent synergistic interaction for several drug combinations over time. CONCLUSIONS: Our method facilitates longitudinal drug-interaction assessment, providing new insights into the temporal-dynamic effects of drug combinations in 3D neurospheres which can help to identify more effective therapies against glioblastoma.
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spelling pubmed-106914432023-12-02 Longitudinal drug synergy assessment using convolutional neural network image-decoding of glioblastoma single-spheroid cultures Giczewska, Anna Pastuszak, Krzysztof Houweling, Megan Abdul, Kulsoom U Faaij, Noa Wedekind, Laurine Noske, David Wurdinger, Thomas Supernat, Anna Westerman, Bart A Neurooncol Adv Basic and Translational Investigations BACKGROUND: In recent years, drug combinations have become increasingly popular to improve therapeutic outcomes in various diseases, including difficult to cure cancers such as the brain cancer glioblastoma. Assessing the interaction between drugs over time is critical for predicting drug combination effectiveness and minimizing the risk of therapy resistance. However, as viability readouts of drug combination experiments are commonly performed as an endpoint where cells are lysed, longitudinal drug-interaction monitoring is currently only possible through combined endpoint assays. METHODS: We provide a method for massive parallel monitoring of drug interactions for 16 drug combinations in 3 glioblastoma models over a time frame of 18 days. In our assay, viabilities of single neurospheres are to be estimated based on image information taken at different time points. Neurosphere images taken on the final day (day 18) were matched to the respective viability measured by CellTiter-Glo 3D on the same day. This allowed to use of machine learning to decode image information to viability values on day 18 as well as for the earlier time points (on days 8, 11, and 15). RESULTS: Our study shows that neurosphere images allow us to predict cell viability from extrapolated viabilities. This enables to assess of the drug interactions in a time window of 18 days. Our results show a clear and persistent synergistic interaction for several drug combinations over time. CONCLUSIONS: Our method facilitates longitudinal drug-interaction assessment, providing new insights into the temporal-dynamic effects of drug combinations in 3D neurospheres which can help to identify more effective therapies against glioblastoma. Oxford University Press 2023-11-05 /pmc/articles/PMC10691443/ /pubmed/38047207 http://dx.doi.org/10.1093/noajnl/vdad134 Text en © The Author(s) 2023. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Basic and Translational Investigations
Giczewska, Anna
Pastuszak, Krzysztof
Houweling, Megan
Abdul, Kulsoom U
Faaij, Noa
Wedekind, Laurine
Noske, David
Wurdinger, Thomas
Supernat, Anna
Westerman, Bart A
Longitudinal drug synergy assessment using convolutional neural network image-decoding of glioblastoma single-spheroid cultures
title Longitudinal drug synergy assessment using convolutional neural network image-decoding of glioblastoma single-spheroid cultures
title_full Longitudinal drug synergy assessment using convolutional neural network image-decoding of glioblastoma single-spheroid cultures
title_fullStr Longitudinal drug synergy assessment using convolutional neural network image-decoding of glioblastoma single-spheroid cultures
title_full_unstemmed Longitudinal drug synergy assessment using convolutional neural network image-decoding of glioblastoma single-spheroid cultures
title_short Longitudinal drug synergy assessment using convolutional neural network image-decoding of glioblastoma single-spheroid cultures
title_sort longitudinal drug synergy assessment using convolutional neural network image-decoding of glioblastoma single-spheroid cultures
topic Basic and Translational Investigations
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10691443/
https://www.ncbi.nlm.nih.gov/pubmed/38047207
http://dx.doi.org/10.1093/noajnl/vdad134
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