Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure
نویسندگان
چکیده
Abstract Background Drug-induced liver injury (DILI) is a major safety concern characterized by complex and diverse pathogenesis. In order to identify DILI early in drug development, better understanding of the models with predictivity are urgently needed. One approach this regard silico which aim at predicting risk based on compound structure. However, these do not yet show sufficient predictive performance or interpretability be useful for decision making themselves, former partially stemming from underlying problem labeling vivo compounds meaningful way generating machine learning models. Results As part Critical Assessment Massive Data Analysis (CAMDA) “CMap Drug Safety Challenge” 2019 ( http://camda2019.bioinf.jku.at ), chemical structure-based were generated using binarized DILIrank annotations. Support Vector Machine (SVM) Random Forest (RF) classifiers showed comparable previously published mean balanced accuracy over 5-fold LOCO-CV inside 10-fold training scheme 0.759 ± 0.027 when an external test set. used predicted protein targets as descriptors, we identified most information-rich proteins agreed mechanisms action toxicity nonsteroidal anti-inflammatory drugs (NSAIDs), one important classes causing DILI, stress response via TP53 biotransformation. addition, multiple involved xenobiotic metabolism could novel DILI-related off-targets, such CLK1 DYRK2. Moreover, derived potential structural alerts high precision, including furan hydrazine derivatives; however, all present approved specific indicating need consider quantitative variables dose. Conclusion Using descriptors fingerprints targets, prediction built previous literature. insights pathways statistically (and potentially causally) linked inferred new related adverse endpoint.
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ژورنال
عنوان ژورنال: Biology Direct
سال: 2021
ISSN: ['1745-6150']
DOI: https://doi.org/10.1186/s13062-020-00285-0