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Chemistry-Based Modeling on Phenotype-Based Drug-Induced Liver Injury Annotation: From Public to Proprietary Data
[Image: see text] Drug-induced liver injury (DILI) is an important safety concern and a major reason to remove a drug from the market. Advancements in recent machine learning methods have led to a wide range of in silico models for DILI predictive methods based on molecule chemical structures (finge...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10445287/ https://www.ncbi.nlm.nih.gov/pubmed/37556769 http://dx.doi.org/10.1021/acs.chemrestox.2c00378 |
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author | Moein, Mohammad Heinonen, Markus Mesens, Natalie Chamanza, Ronnie Amuzie, Chidozie Will, Yvonne Ceulemans, Hugo Kaski, Samuel Herman, Dorota |
author_facet | Moein, Mohammad Heinonen, Markus Mesens, Natalie Chamanza, Ronnie Amuzie, Chidozie Will, Yvonne Ceulemans, Hugo Kaski, Samuel Herman, Dorota |
author_sort | Moein, Mohammad |
collection | PubMed |
description | [Image: see text] Drug-induced liver injury (DILI) is an important safety concern and a major reason to remove a drug from the market. Advancements in recent machine learning methods have led to a wide range of in silico models for DILI predictive methods based on molecule chemical structures (fingerprints). Existing publicly available DILI data sets used for model building are based on the interpretation of drug labels or patient case reports, resulting in a typical binary clinical DILI annotation. We developed a novel phenotype-based annotation to process hepatotoxicity information extracted from repeated dose in vivo preclinical toxicology studies using INHAND annotation to provide a more informative and reliable data set for machine learning algorithms. This work resulted in a data set of 430 unique compounds covering diverse liver pathology findings which were utilized to develop multiple DILI prediction models trained on the publicly available data (TG-GATEs) using the compound’s fingerprint. We demonstrate that the TG-GATEs compounds DILI labels can be predicted well and how the differences between TG-GATEs and the external test compounds (Johnson & Johnson) impact the model generalization performance. |
format | Online Article Text |
id | pubmed-10445287 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-104452872023-08-24 Chemistry-Based Modeling on Phenotype-Based Drug-Induced Liver Injury Annotation: From Public to Proprietary Data Moein, Mohammad Heinonen, Markus Mesens, Natalie Chamanza, Ronnie Amuzie, Chidozie Will, Yvonne Ceulemans, Hugo Kaski, Samuel Herman, Dorota Chem Res Toxicol [Image: see text] Drug-induced liver injury (DILI) is an important safety concern and a major reason to remove a drug from the market. Advancements in recent machine learning methods have led to a wide range of in silico models for DILI predictive methods based on molecule chemical structures (fingerprints). Existing publicly available DILI data sets used for model building are based on the interpretation of drug labels or patient case reports, resulting in a typical binary clinical DILI annotation. We developed a novel phenotype-based annotation to process hepatotoxicity information extracted from repeated dose in vivo preclinical toxicology studies using INHAND annotation to provide a more informative and reliable data set for machine learning algorithms. This work resulted in a data set of 430 unique compounds covering diverse liver pathology findings which were utilized to develop multiple DILI prediction models trained on the publicly available data (TG-GATEs) using the compound’s fingerprint. We demonstrate that the TG-GATEs compounds DILI labels can be predicted well and how the differences between TG-GATEs and the external test compounds (Johnson & Johnson) impact the model generalization performance. American Chemical Society 2023-08-09 /pmc/articles/PMC10445287/ /pubmed/37556769 http://dx.doi.org/10.1021/acs.chemrestox.2c00378 Text en © 2023 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by/4.0/Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Moein, Mohammad Heinonen, Markus Mesens, Natalie Chamanza, Ronnie Amuzie, Chidozie Will, Yvonne Ceulemans, Hugo Kaski, Samuel Herman, Dorota Chemistry-Based Modeling on Phenotype-Based Drug-Induced Liver Injury Annotation: From Public to Proprietary Data |
title | Chemistry-Based
Modeling on Phenotype-Based Drug-Induced
Liver Injury Annotation: From Public to Proprietary Data |
title_full | Chemistry-Based
Modeling on Phenotype-Based Drug-Induced
Liver Injury Annotation: From Public to Proprietary Data |
title_fullStr | Chemistry-Based
Modeling on Phenotype-Based Drug-Induced
Liver Injury Annotation: From Public to Proprietary Data |
title_full_unstemmed | Chemistry-Based
Modeling on Phenotype-Based Drug-Induced
Liver Injury Annotation: From Public to Proprietary Data |
title_short | Chemistry-Based
Modeling on Phenotype-Based Drug-Induced
Liver Injury Annotation: From Public to Proprietary Data |
title_sort | chemistry-based
modeling on phenotype-based drug-induced
liver injury annotation: from public to proprietary data |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10445287/ https://www.ncbi.nlm.nih.gov/pubmed/37556769 http://dx.doi.org/10.1021/acs.chemrestox.2c00378 |
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