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Performance of preclinical models in predicting drug-induced liver injury in humans: a systematic review
Drug-induced liver injury (DILI) causes one in three market withdrawals due to adverse drug reactions, causing preventable human suffering and massive financial loss. We applied evidence-based methods to investigate the role of preclinical studies in predicting human DILI using two anti-diabetic dru...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7973584/ https://www.ncbi.nlm.nih.gov/pubmed/33737635 http://dx.doi.org/10.1038/s41598-021-85708-2 |
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author | Dirven, Hubert Vist, Gunn E. Bandhakavi, Sricharan Mehta, Jyotsna Fitch, Seneca E. Pound, Pandora Ram, Rebecca Kincaid, Breanne Leenaars, Cathalijn H. C. Chen, Minjun Wright, Robert A. Tsaioun, Katya |
author_facet | Dirven, Hubert Vist, Gunn E. Bandhakavi, Sricharan Mehta, Jyotsna Fitch, Seneca E. Pound, Pandora Ram, Rebecca Kincaid, Breanne Leenaars, Cathalijn H. C. Chen, Minjun Wright, Robert A. Tsaioun, Katya |
author_sort | Dirven, Hubert |
collection | PubMed |
description | Drug-induced liver injury (DILI) causes one in three market withdrawals due to adverse drug reactions, causing preventable human suffering and massive financial loss. We applied evidence-based methods to investigate the role of preclinical studies in predicting human DILI using two anti-diabetic drugs from the same class, but with different toxicological profiles: troglitazone (withdrawn from US market due to DILI) and rosiglitazone (remains on US market). Evidence Stream 1: A systematic literature review of in vivo studies on rosiglitazone or troglitazone was conducted (PROSPERO registration CRD42018112353). Evidence Stream 2: in vitro data on troglitazone and rosiglitazone were retrieved from the US EPA ToxCast database. Evidence Stream 3: troglitazone- and rosiglitazone-related DILI cases were retrieved from WHO Vigibase. All three evidence stream analyses were conducted according to evidence-based methodologies and performed according to pre-registered protocols. Evidence Stream 1: 9288 references were identified, with 42 studies included in analysis. No reported biomarker for either drug indicated a strong hazard signal in either preclinical animal or human studies. All included studies had substantial limitations, resulting in “low” or “very low” certainty in findings. Evidence Stream 2: Troglitazone was active in twice as many in vitro assays (129) as rosiglitazone (60), indicating a strong signal for more off-target effects. Evidence Stream 3: We observed a fivefold difference in both all adverse events and liver-related adverse events reported, and an eightfold difference in fatalities for troglitazone, compared to rosiglitazone. In summary, published animal and human trials failed to predict troglitazone’s potential to cause severe liver injury in a wider patient population, while in vitro data showed marked differences in the two drugs’ off-target activities, offering a new paradigm for reducing drug attrition in late development and in the market. This investigation concludes that death and disability due to adverse drug reactions may be prevented if mechanistic information is deployed at early stages of drug development by pharmaceutical companies and is considered by regulators as a part of regulatory submissions. |
format | Online Article Text |
id | pubmed-7973584 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-79735842021-03-19 Performance of preclinical models in predicting drug-induced liver injury in humans: a systematic review Dirven, Hubert Vist, Gunn E. Bandhakavi, Sricharan Mehta, Jyotsna Fitch, Seneca E. Pound, Pandora Ram, Rebecca Kincaid, Breanne Leenaars, Cathalijn H. C. Chen, Minjun Wright, Robert A. Tsaioun, Katya Sci Rep Article Drug-induced liver injury (DILI) causes one in three market withdrawals due to adverse drug reactions, causing preventable human suffering and massive financial loss. We applied evidence-based methods to investigate the role of preclinical studies in predicting human DILI using two anti-diabetic drugs from the same class, but with different toxicological profiles: troglitazone (withdrawn from US market due to DILI) and rosiglitazone (remains on US market). Evidence Stream 1: A systematic literature review of in vivo studies on rosiglitazone or troglitazone was conducted (PROSPERO registration CRD42018112353). Evidence Stream 2: in vitro data on troglitazone and rosiglitazone were retrieved from the US EPA ToxCast database. Evidence Stream 3: troglitazone- and rosiglitazone-related DILI cases were retrieved from WHO Vigibase. All three evidence stream analyses were conducted according to evidence-based methodologies and performed according to pre-registered protocols. Evidence Stream 1: 9288 references were identified, with 42 studies included in analysis. No reported biomarker for either drug indicated a strong hazard signal in either preclinical animal or human studies. All included studies had substantial limitations, resulting in “low” or “very low” certainty in findings. Evidence Stream 2: Troglitazone was active in twice as many in vitro assays (129) as rosiglitazone (60), indicating a strong signal for more off-target effects. Evidence Stream 3: We observed a fivefold difference in both all adverse events and liver-related adverse events reported, and an eightfold difference in fatalities for troglitazone, compared to rosiglitazone. In summary, published animal and human trials failed to predict troglitazone’s potential to cause severe liver injury in a wider patient population, while in vitro data showed marked differences in the two drugs’ off-target activities, offering a new paradigm for reducing drug attrition in late development and in the market. This investigation concludes that death and disability due to adverse drug reactions may be prevented if mechanistic information is deployed at early stages of drug development by pharmaceutical companies and is considered by regulators as a part of regulatory submissions. Nature Publishing Group UK 2021-03-18 /pmc/articles/PMC7973584/ /pubmed/33737635 http://dx.doi.org/10.1038/s41598-021-85708-2 Text en © The Author(s) 2021 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Dirven, Hubert Vist, Gunn E. Bandhakavi, Sricharan Mehta, Jyotsna Fitch, Seneca E. Pound, Pandora Ram, Rebecca Kincaid, Breanne Leenaars, Cathalijn H. C. Chen, Minjun Wright, Robert A. Tsaioun, Katya Performance of preclinical models in predicting drug-induced liver injury in humans: a systematic review |
title | Performance of preclinical models in predicting drug-induced liver injury in humans: a systematic review |
title_full | Performance of preclinical models in predicting drug-induced liver injury in humans: a systematic review |
title_fullStr | Performance of preclinical models in predicting drug-induced liver injury in humans: a systematic review |
title_full_unstemmed | Performance of preclinical models in predicting drug-induced liver injury in humans: a systematic review |
title_short | Performance of preclinical models in predicting drug-induced liver injury in humans: a systematic review |
title_sort | performance of preclinical models in predicting drug-induced liver injury in humans: a systematic review |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7973584/ https://www.ncbi.nlm.nih.gov/pubmed/33737635 http://dx.doi.org/10.1038/s41598-021-85708-2 |
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