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Performance assessment and economic analysis of a human Liver-Chip for predictive toxicology
BACKGROUND: Conventional preclinical models often miss drug toxicities, meaning the harm these drugs pose to humans is only realized in clinical trials or when they make it to market. This has caused the pharmaceutical industry to waste considerable time and resources developing drugs destined to fa...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9727064/ https://www.ncbi.nlm.nih.gov/pubmed/36473994 http://dx.doi.org/10.1038/s43856-022-00209-1 |
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author | Ewart, Lorna Apostolou, Athanasia Briggs, Skyler A. Carman, Christopher V. Chaff, Jake T. Heng, Anthony R. Jadalannagari, Sushma Janardhanan, Jeshina Jang, Kyung-Jin Joshipura, Sannidhi R. Kadam, Mahika M. Kanellias, Marianne Kujala, Ville J. Kulkarni, Gauri Le, Christopher Y. Lucchesi, Carolina Manatakis, Dimitris V. Maniar, Kairav K. Quinn, Meaghan E. Ravan, Joseph S. Rizos, Ann Catherine Sauld, John F. K. Sliz, Josiah D. Tien-Street, William Trinidad, Dennis Ramos Velez, James Wendell, Max Irrechukwu, Onyi Mahalingaiah, Prathap Kumar Ingber, Donald E. Scannell, Jack W. Levner, Daniel |
author_facet | Ewart, Lorna Apostolou, Athanasia Briggs, Skyler A. Carman, Christopher V. Chaff, Jake T. Heng, Anthony R. Jadalannagari, Sushma Janardhanan, Jeshina Jang, Kyung-Jin Joshipura, Sannidhi R. Kadam, Mahika M. Kanellias, Marianne Kujala, Ville J. Kulkarni, Gauri Le, Christopher Y. Lucchesi, Carolina Manatakis, Dimitris V. Maniar, Kairav K. Quinn, Meaghan E. Ravan, Joseph S. Rizos, Ann Catherine Sauld, John F. K. Sliz, Josiah D. Tien-Street, William Trinidad, Dennis Ramos Velez, James Wendell, Max Irrechukwu, Onyi Mahalingaiah, Prathap Kumar Ingber, Donald E. Scannell, Jack W. Levner, Daniel |
author_sort | Ewart, Lorna |
collection | PubMed |
description | BACKGROUND: Conventional preclinical models often miss drug toxicities, meaning the harm these drugs pose to humans is only realized in clinical trials or when they make it to market. This has caused the pharmaceutical industry to waste considerable time and resources developing drugs destined to fail. Organ-on-a-Chip technology has the potential to improve success in drug development pipelines, as it can recapitulate organ-level pathophysiology and clinical responses; however, systematic and quantitative evaluations of Organ-Chips’ predictive value have not yet been reported. METHODS: 870 Liver-Chips were analyzed to determine their ability to predict drug-induced liver injury caused by small molecules identified as benchmarks by the Innovation and Quality consortium, who has published guidelines defining criteria for qualifying preclinical models. An economic analysis was also performed to measure the value Liver-Chips could offer if they were broadly adopted in supporting toxicity-related decisions as part of preclinical development workflows. RESULTS: Here, we show that the Liver-Chip met the qualification guidelines across a blinded set of 27 known hepatotoxic and non-toxic drugs with a sensitivity of 87% and a specificity of 100%. We also show that this level of performance could generate over $3 billion annually for the pharmaceutical industry through increased small-molecule R&D productivity. CONCLUSIONS: The results of this study show how incorporating predictive Organ-Chips into drug development workflows could substantially improve drug discovery and development, allowing manufacturers to bring safer, more effective medicines to market in less time and at lower costs. |
format | Online Article Text |
id | pubmed-9727064 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-97270642022-12-08 Performance assessment and economic analysis of a human Liver-Chip for predictive toxicology Ewart, Lorna Apostolou, Athanasia Briggs, Skyler A. Carman, Christopher V. Chaff, Jake T. Heng, Anthony R. Jadalannagari, Sushma Janardhanan, Jeshina Jang, Kyung-Jin Joshipura, Sannidhi R. Kadam, Mahika M. Kanellias, Marianne Kujala, Ville J. Kulkarni, Gauri Le, Christopher Y. Lucchesi, Carolina Manatakis, Dimitris V. Maniar, Kairav K. Quinn, Meaghan E. Ravan, Joseph S. Rizos, Ann Catherine Sauld, John F. K. Sliz, Josiah D. Tien-Street, William Trinidad, Dennis Ramos Velez, James Wendell, Max Irrechukwu, Onyi Mahalingaiah, Prathap Kumar Ingber, Donald E. Scannell, Jack W. Levner, Daniel Commun Med (Lond) Article BACKGROUND: Conventional preclinical models often miss drug toxicities, meaning the harm these drugs pose to humans is only realized in clinical trials or when they make it to market. This has caused the pharmaceutical industry to waste considerable time and resources developing drugs destined to fail. Organ-on-a-Chip technology has the potential to improve success in drug development pipelines, as it can recapitulate organ-level pathophysiology and clinical responses; however, systematic and quantitative evaluations of Organ-Chips’ predictive value have not yet been reported. METHODS: 870 Liver-Chips were analyzed to determine their ability to predict drug-induced liver injury caused by small molecules identified as benchmarks by the Innovation and Quality consortium, who has published guidelines defining criteria for qualifying preclinical models. An economic analysis was also performed to measure the value Liver-Chips could offer if they were broadly adopted in supporting toxicity-related decisions as part of preclinical development workflows. RESULTS: Here, we show that the Liver-Chip met the qualification guidelines across a blinded set of 27 known hepatotoxic and non-toxic drugs with a sensitivity of 87% and a specificity of 100%. We also show that this level of performance could generate over $3 billion annually for the pharmaceutical industry through increased small-molecule R&D productivity. CONCLUSIONS: The results of this study show how incorporating predictive Organ-Chips into drug development workflows could substantially improve drug discovery and development, allowing manufacturers to bring safer, more effective medicines to market in less time and at lower costs. Nature Publishing Group UK 2022-12-06 /pmc/articles/PMC9727064/ /pubmed/36473994 http://dx.doi.org/10.1038/s43856-022-00209-1 Text en © The Author(s) 2022, corrected publication 2023 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 | Article Ewart, Lorna Apostolou, Athanasia Briggs, Skyler A. Carman, Christopher V. Chaff, Jake T. Heng, Anthony R. Jadalannagari, Sushma Janardhanan, Jeshina Jang, Kyung-Jin Joshipura, Sannidhi R. Kadam, Mahika M. Kanellias, Marianne Kujala, Ville J. Kulkarni, Gauri Le, Christopher Y. Lucchesi, Carolina Manatakis, Dimitris V. Maniar, Kairav K. Quinn, Meaghan E. Ravan, Joseph S. Rizos, Ann Catherine Sauld, John F. K. Sliz, Josiah D. Tien-Street, William Trinidad, Dennis Ramos Velez, James Wendell, Max Irrechukwu, Onyi Mahalingaiah, Prathap Kumar Ingber, Donald E. Scannell, Jack W. Levner, Daniel Performance assessment and economic analysis of a human Liver-Chip for predictive toxicology |
title | Performance assessment and economic analysis of a human Liver-Chip for predictive toxicology |
title_full | Performance assessment and economic analysis of a human Liver-Chip for predictive toxicology |
title_fullStr | Performance assessment and economic analysis of a human Liver-Chip for predictive toxicology |
title_full_unstemmed | Performance assessment and economic analysis of a human Liver-Chip for predictive toxicology |
title_short | Performance assessment and economic analysis of a human Liver-Chip for predictive toxicology |
title_sort | performance assessment and economic analysis of a human liver-chip for predictive toxicology |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9727064/ https://www.ncbi.nlm.nih.gov/pubmed/36473994 http://dx.doi.org/10.1038/s43856-022-00209-1 |
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