Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure
<h3>Background</h3><p dir="ltr">Drug-induced liver injury (DILI) is a major safety concern characterized by a complex and diverse pathogenesis. In order to identify DILI early in drug development, a better understanding of the injury and models with better predictivity ar...
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| مؤلفون آخرون: | , , , , , , |
| منشور في: |
2021
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| _version_ | 1864513516692570112 |
|---|---|
| author | Anika Liu (8411805) |
| author2 | Moritz Walter (10001555) Peter Wright (699130) Aleksandra Bartosik (10001558) Daniela Dolciami (1701346) Abdurrahman Elbasir (9977355) Hongbin Yang (2189392) Andreas Bender (192334) |
| author2_role | author author author author author author author |
| author_facet | Anika Liu (8411805) Moritz Walter (10001555) Peter Wright (699130) Aleksandra Bartosik (10001558) Daniela Dolciami (1701346) Abdurrahman Elbasir (9977355) Hongbin Yang (2189392) Andreas Bender (192334) |
| author_role | author |
| dc.creator.none.fl_str_mv | Anika Liu (8411805) Moritz Walter (10001555) Peter Wright (699130) Aleksandra Bartosik (10001558) Daniela Dolciami (1701346) Abdurrahman Elbasir (9977355) Hongbin Yang (2189392) Andreas Bender (192334) |
| dc.date.none.fl_str_mv | 2021-01-18T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1186/s13062-020-00285-0 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Prediction_and_mechanistic_analysis_of_drug-induced_liver_injury_DILI_based_on_chemical_structure/25771146 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Chemical sciences Medicinal and biomolecular chemistry Drug-induced liver injury (DILI) Mechanistic models Structural alerts Protein target |
| dc.title.none.fl_str_mv | Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Background</h3><p dir="ltr">Drug-induced liver injury (DILI) is a major safety concern characterized by a complex and diverse pathogenesis. In order to identify DILI early in drug development, a better understanding of the injury and models with better predictivity are urgently needed. One approach in this regard are in silico models which aim at predicting the risk of DILI based on the compound structure. However, these models do not yet show sufficient predictive performance or interpretability to be useful for decision making by themselves, the former partially stemming from the underlying problem of labeling the in vivo DILI risk of compounds in a meaningful way for generating machine learning models.</p><h3>Results</h3><p dir="ltr">As part of the Critical Assessment of Massive Data Analysis (CAMDA) “CMap Drug Safety Challenge” 2019 (http://camda2019.bioinf.jku.at), chemical structure-based models were generated using the binarized DILIrank annotations. Support Vector Machine (SVM) and Random Forest (RF) classifiers showed comparable performance to previously published models with a mean balanced accuracy over models generated using 5-fold LOCO-CV inside a 10-fold training scheme of 0.759 ± 0.027 when predicting an external test set. In the models which used predicted protein targets as compound descriptors, we identified the most information-rich proteins which agreed with the mechanisms of action and toxicity of nonsteroidal anti-inflammatory drugs (NSAIDs), one of the most important drug classes causing DILI, stress response via TP53 and biotransformation. In addition, we identified multiple proteins involved in xenobiotic metabolism which could be novel DILI-related off-targets, such as CLK1 and DYRK2. Moreover, we derived potential structural alerts for DILI with high precision, including furan and hydrazine derivatives; however, all derived alerts were present in approved drugs and were over specific indicating the need to consider quantitative variables such as dose.</p><h3>Conclusion</h3><p dir="ltr">Using chemical structure-based descriptors such as structural fingerprints and predicted protein targets, DILI prediction models were built with a predictive performance comparable to previous literature. In addition, we derived insights on proteins and pathways statistically (and potentially causally) linked to DILI from these models and inferred new structural alerts related to this adverse endpoint.</p><h2>Other Information</h2><p dir="ltr">Published in: Biology Direct<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1186/s13062-020-00285-0" target="_blank">https://dx.doi.org/10.1186/s13062-020-00285-0</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_15d2f2bf1a58ab716778a13dd589ad69 |
| identifier_str_mv | 10.1186/s13062-020-00285-0 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25771146 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structureAnika Liu (8411805)Moritz Walter (10001555)Peter Wright (699130)Aleksandra Bartosik (10001558)Daniela Dolciami (1701346)Abdurrahman Elbasir (9977355)Hongbin Yang (2189392)Andreas Bender (192334)Chemical sciencesMedicinal and biomolecular chemistryDrug-induced liver injury (DILI)Mechanistic modelsStructural alertsProtein target<h3>Background</h3><p dir="ltr">Drug-induced liver injury (DILI) is a major safety concern characterized by a complex and diverse pathogenesis. In order to identify DILI early in drug development, a better understanding of the injury and models with better predictivity are urgently needed. One approach in this regard are in silico models which aim at predicting the risk of DILI based on the compound structure. However, these models do not yet show sufficient predictive performance or interpretability to be useful for decision making by themselves, the former partially stemming from the underlying problem of labeling the in vivo DILI risk of compounds in a meaningful way for generating machine learning models.</p><h3>Results</h3><p dir="ltr">As part of the Critical Assessment of Massive Data Analysis (CAMDA) “CMap Drug Safety Challenge” 2019 (http://camda2019.bioinf.jku.at), chemical structure-based models were generated using the binarized DILIrank annotations. Support Vector Machine (SVM) and Random Forest (RF) classifiers showed comparable performance to previously published models with a mean balanced accuracy over models generated using 5-fold LOCO-CV inside a 10-fold training scheme of 0.759 ± 0.027 when predicting an external test set. In the models which used predicted protein targets as compound descriptors, we identified the most information-rich proteins which agreed with the mechanisms of action and toxicity of nonsteroidal anti-inflammatory drugs (NSAIDs), one of the most important drug classes causing DILI, stress response via TP53 and biotransformation. In addition, we identified multiple proteins involved in xenobiotic metabolism which could be novel DILI-related off-targets, such as CLK1 and DYRK2. Moreover, we derived potential structural alerts for DILI with high precision, including furan and hydrazine derivatives; however, all derived alerts were present in approved drugs and were over specific indicating the need to consider quantitative variables such as dose.</p><h3>Conclusion</h3><p dir="ltr">Using chemical structure-based descriptors such as structural fingerprints and predicted protein targets, DILI prediction models were built with a predictive performance comparable to previous literature. In addition, we derived insights on proteins and pathways statistically (and potentially causally) linked to DILI from these models and inferred new structural alerts related to this adverse endpoint.</p><h2>Other Information</h2><p dir="ltr">Published in: Biology Direct<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1186/s13062-020-00285-0" target="_blank">https://dx.doi.org/10.1186/s13062-020-00285-0</a></p>2021-01-18T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1186/s13062-020-00285-0https://figshare.com/articles/journal_contribution/Prediction_and_mechanistic_analysis_of_drug-induced_liver_injury_DILI_based_on_chemical_structure/25771146CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/257711462021-01-18T03:00:00Z |
| spellingShingle | Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure Anika Liu (8411805) Chemical sciences Medicinal and biomolecular chemistry Drug-induced liver injury (DILI) Mechanistic models Structural alerts Protein target |
| status_str | publishedVersion |
| title | Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure |
| title_full | Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure |
| title_fullStr | Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure |
| title_full_unstemmed | Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure |
| title_short | Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure |
| title_sort | Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure |
| topic | Chemical sciences Medicinal and biomolecular chemistry Drug-induced liver injury (DILI) Mechanistic models Structural alerts Protein target |