Development of Prediction Models for Drug-Induced Cholestasis, Cirrhosis, Hepatitis, and Steatosis Based on Drug and Drug Metabolite Structures
Drug-induced liver injury (DILI) is one of the major reasons for termination of drug development. Due to the importance of predicting DILI in early phases of drug development, diverse in silico models have been developed to filter out DILI-causing candidates before clinical study. However, no comput...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-02-01
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| Series: | Frontiers in Pharmacology |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fphar.2020.00067/full |
| _version_ | 1818017919896911872 |
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| author | Hyun Kil Shin Myung-Gyun Kang Daeui Park Daeui Park Tamina Park Tamina Park Seokjoo Yoon Seokjoo Yoon |
| author_facet | Hyun Kil Shin Myung-Gyun Kang Daeui Park Daeui Park Tamina Park Tamina Park Seokjoo Yoon Seokjoo Yoon |
| author_sort | Hyun Kil Shin |
| collection | DOAJ |
| description | Drug-induced liver injury (DILI) is one of the major reasons for termination of drug development. Due to the importance of predicting DILI in early phases of drug development, diverse in silico models have been developed to filter out DILI-causing candidates before clinical study. However, no computational models have achieved sufficient prediction power for screening DILI in early phases because 1) drugs often cause liver injury through reactive metabolites, 2) different clinical outcomes of DILI have different mechanisms, and 3) the DILI label on drugs is not clearly defined. In this study, we developed binary classification models to predict drug-induced cholestasis, cirrhosis, hepatitis, and steatosis based on the structure of drugs and their metabolites. DILI-positive data was obtained from post-market reports of drugs and DILI-negative data from DILIrank, a database curated by the Food and Drug Administration (FDA). Support vector machine (SVM) and random forest (RF) were used in developing models with nine fingerprints and one 2D molecular descriptor calculated from drug (152 DILI-positives and 102 DILI-negatives) and drug metabolite (192 DILI-positives and 126 DILI-negatives) structures. Models were developed according to Organisation for Economic Co-operation and Development (OECD) guidelines for quantitative structure-activity relationship (QSAR) validation. Internal and external validation was performed with a randomization test in order to thoroughly examine model predictability and avoid random correlation between structural features and adverse outcomes. The applicability domain was defined with a leverage method for reliable prediction of new chemicals. The best models for each liver disease were selected based on external validation results from drugs (cholestasis: 70%, cirrhosis: 90%, hepatitis: 83%, and steatosis: 85%) and drug metabolites (cholestasis: 86%, cirrhosis: 88%, hepatitis: 86%, and steatosis: 83%) with applicability domain analysis. Compiled data sets were further exploited to derive privileged substructures that were more frequent in DILI-positive sets compared to DILI-negative sets and in drug metabolite structures compared to drug structures with a Morgan fingerprint level 2. |
| first_indexed | 2024-04-14T07:33:09Z |
| format | Article |
| id | doaj.art-99e6c26044fe4c3b9375b10149e825df |
| institution | Directory Open Access Journal |
| issn | 1663-9812 |
| language | English |
| last_indexed | 2024-04-14T07:33:09Z |
| publishDate | 2020-02-01 |
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| series | Frontiers in Pharmacology |
| spelling | doaj.art-99e6c26044fe4c3b9375b10149e825df2022-12-22T02:05:47ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122020-02-011110.3389/fphar.2020.00067496623Development of Prediction Models for Drug-Induced Cholestasis, Cirrhosis, Hepatitis, and Steatosis Based on Drug and Drug Metabolite StructuresHyun Kil Shin0Myung-Gyun Kang1Daeui Park2Daeui Park3Tamina Park4Tamina Park5Seokjoo Yoon6Seokjoo Yoon7Toxicoinformatics Group, Department of Predictive Toxicology, Korea Institute of Toxicology, Daejeon, South KoreaToxicoinformatics Group, Department of Predictive Toxicology, Korea Institute of Toxicology, Daejeon, South KoreaToxicoinformatics Group, Department of Predictive Toxicology, Korea Institute of Toxicology, Daejeon, South KoreaDepartment of Human and Environmental Toxicology, University of Science and Technology, Daejeon, South KoreaToxicoinformatics Group, Department of Predictive Toxicology, Korea Institute of Toxicology, Daejeon, South KoreaDepartment of Human and Environmental Toxicology, University of Science and Technology, Daejeon, South KoreaToxicoinformatics Group, Department of Predictive Toxicology, Korea Institute of Toxicology, Daejeon, South KoreaDepartment of Human and Environmental Toxicology, University of Science and Technology, Daejeon, South KoreaDrug-induced liver injury (DILI) is one of the major reasons for termination of drug development. Due to the importance of predicting DILI in early phases of drug development, diverse in silico models have been developed to filter out DILI-causing candidates before clinical study. However, no computational models have achieved sufficient prediction power for screening DILI in early phases because 1) drugs often cause liver injury through reactive metabolites, 2) different clinical outcomes of DILI have different mechanisms, and 3) the DILI label on drugs is not clearly defined. In this study, we developed binary classification models to predict drug-induced cholestasis, cirrhosis, hepatitis, and steatosis based on the structure of drugs and their metabolites. DILI-positive data was obtained from post-market reports of drugs and DILI-negative data from DILIrank, a database curated by the Food and Drug Administration (FDA). Support vector machine (SVM) and random forest (RF) were used in developing models with nine fingerprints and one 2D molecular descriptor calculated from drug (152 DILI-positives and 102 DILI-negatives) and drug metabolite (192 DILI-positives and 126 DILI-negatives) structures. Models were developed according to Organisation for Economic Co-operation and Development (OECD) guidelines for quantitative structure-activity relationship (QSAR) validation. Internal and external validation was performed with a randomization test in order to thoroughly examine model predictability and avoid random correlation between structural features and adverse outcomes. The applicability domain was defined with a leverage method for reliable prediction of new chemicals. The best models for each liver disease were selected based on external validation results from drugs (cholestasis: 70%, cirrhosis: 90%, hepatitis: 83%, and steatosis: 85%) and drug metabolites (cholestasis: 86%, cirrhosis: 88%, hepatitis: 86%, and steatosis: 83%) with applicability domain analysis. Compiled data sets were further exploited to derive privileged substructures that were more frequent in DILI-positive sets compared to DILI-negative sets and in drug metabolite structures compared to drug structures with a Morgan fingerprint level 2.https://www.frontiersin.org/article/10.3389/fphar.2020.00067/fulldrug-induced liver injurystructure-activity relationshipstructural alertscomputational toxicologydrug metabolism |
| spellingShingle | Hyun Kil Shin Myung-Gyun Kang Daeui Park Daeui Park Tamina Park Tamina Park Seokjoo Yoon Seokjoo Yoon Development of Prediction Models for Drug-Induced Cholestasis, Cirrhosis, Hepatitis, and Steatosis Based on Drug and Drug Metabolite Structures Frontiers in Pharmacology drug-induced liver injury structure-activity relationship structural alerts computational toxicology drug metabolism |
| title | Development of Prediction Models for Drug-Induced Cholestasis, Cirrhosis, Hepatitis, and Steatosis Based on Drug and Drug Metabolite Structures |
| title_full | Development of Prediction Models for Drug-Induced Cholestasis, Cirrhosis, Hepatitis, and Steatosis Based on Drug and Drug Metabolite Structures |
| title_fullStr | Development of Prediction Models for Drug-Induced Cholestasis, Cirrhosis, Hepatitis, and Steatosis Based on Drug and Drug Metabolite Structures |
| title_full_unstemmed | Development of Prediction Models for Drug-Induced Cholestasis, Cirrhosis, Hepatitis, and Steatosis Based on Drug and Drug Metabolite Structures |
| title_short | Development of Prediction Models for Drug-Induced Cholestasis, Cirrhosis, Hepatitis, and Steatosis Based on Drug and Drug Metabolite Structures |
| title_sort | development of prediction models for drug induced cholestasis cirrhosis hepatitis and steatosis based on drug and drug metabolite structures |
| topic | drug-induced liver injury structure-activity relationship structural alerts computational toxicology drug metabolism |
| url | https://www.frontiersin.org/article/10.3389/fphar.2020.00067/full |
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