Dataset Information

Integrating Drug's Mode of Action into Quantitative Structure-Activity Relationships for Improved Prediction of Drug-Induced Liver Injury.

ABSTRACT: Drug-induced liver injury (DILI) is complex in mechanism. Different drugs could undergo different mechanisms but result in the same DILI type, while the same drug could lead to different DILI types via different mechanisms. Therefore, predicting a drug's potential for DILI should take its underlying mechanisms into consideration. To achieve that, we constructed a novel approach by incorporating the drug's Mode of Action (MOA) into Quantitative Structure-Activity Relationship (QSAR) modeling. This MOA-DILI approach was examined using a data set of 333 drugs. The drugs were first grouped according to their MOA profiles (positive or negative in each MOA) based on the Tox21 qHTS assays. QSAR models for individual MOA assays were developed and subsequently combined to obtain the MOA-DILI model. A hold-out testing strategy (222 drugs for training and 111 drugs as a test set) was employed, which yielded a predictive accuracy of 0.711. The MOA-DILI model was directly compared with the standard QSAR approach using the same hold-out strategy, and the QSAR model yielded an accuracy of 0.662. To minimize the random chance in splitting training/test sets, the hold-out testing process was repeated 1000 times, and the observed difference in prediction accuracy between MOA-DILI and QSARs was statistically significant (P value <0.0001). Out of 17 MOAs used, four assays (i.e., antioxidant response elements, PPAR-gamma, estrogen receptor, and thyroid receptor assays) contributed most to the improved prediction of the MOA-DILI model over QSARs. In conclusion, the MOA-DILI approach has the potential to significantly improve predictive outcomes and to reveal complex relationships between MOAs and DILI, all of which would be helpful in developing DILI predictive models in drug screening and for risk assessment of industrial chemicals.

SUBMITTER: Wu L

PROVIDER: S-EPMC6233892 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Integrating Drug's Mode of Action into Quantitative Structure-Activity Relationships for Improved Prediction of Drug-Induced Liver Injury.

Wu Leihong L Liu Zhichao Z Auerbach Scott S Huang Ruili R Chen Minjun M McEuen Kristin K Xu Joshua J Fang Hong H Tong Weida W

Journal of chemical information and modeling 20170410 4

Drug-induced liver injury (DILI) is complex in mechanism. Different drugs could undergo different mechanisms but result in the same DILI type, while the same drug could lead to different DILI types via different mechanisms. Therefore, predicting a drug's potential for DILI should take its underlying mechanisms into consideration. To achieve that, we constructed a novel approach by incorporating the drug's Mode of Action (MOA) into Quantitative Structure-Activity Relationship (QSAR) modeling. Thi ...[more]

PMID: 28350954

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Project description:Drug-induced liver injury (DILI) has been a significant challenge in drug discovery, often leading to clinical trial failures and necessitating drug withdrawals. Over the last decade, the existing suite of in vitro proxy-DILI assays has generally improved at identifying compounds with hepatotoxicity. However, there is considerable interest in enhancing the in silico prediction of DILI because it allows for evaluating large sets of compounds more quickly and cost-effectively, particularly in the early stages of projects. In this study, we aim to study ML models for DILI prediction that first predict nine proxy-DILI labels and then use them as features in addition to chemical structural features to predict DILI. The features include in vitro (e.g., mitochondrial toxicity, bile salt export pump inhibition) data, in vivo (e.g., preclinical rat hepatotoxicity studies) data, pharmacokinetic parameters of maximum concentration, structural fingerprints, and physicochemical parameters. We trained DILI-prediction models on 888 compounds from the DILI data set (composed of DILIst and DILIrank) and tested them on a held-out external test set of 223 compounds from the DILI data set. The best model, DILIPredictor, attained an AUC-PR of 0.79. This model enabled the detection of the top 25 toxic compounds (2.68 LR+, positive likelihood ratio) compared to models using only structural features (1.65 LR+ score). Using feature interpretation from DILIPredictor, we identified the chemical substructures causing DILI and differentiated cases of DILI caused by compounds in animals but not in humans. For example, DILIPredictor correctly recognized 2-butoxyethanol as nontoxic in humans despite its hepatotoxicity in mice models. Overall, the DILIPredictor model improves the detection of compounds causing DILI with an improved differentiation between animal and human sensitivity and the potential for mechanism evaluation. DILIPredictor required only chemical structures as input for prediction and is publicly available at https://broad.io/DILIPredictor for use via web interface and with all code available for download.

Project description:Huang Qin Decoction (HQD), is used for the treatment of diabetic liver injury (DLI) and in this study, its mechanisms were evaluated by metabonomics and system pharmacology. To study the anti-DLI effects of HQD. The 48 male db/db mice were fed adaptively for one week, and a random blood glucose test was performed twice. The db/db mice with a blood glucose level of more than 11.1mol/l were separated into four groups: the model group, the active control group, the high-dose HQD group the low-dose HQD group, the control group consisted of db/m mice. Using the UHPLC/Q-TOF-MS metabolomics approach, 18 metabolites were found to be profoundly altered in the model group, and the levels of these biomarkers were significantly recovered after treatment with HQD. 8 signaling pathways related to HQD, including the Sphingolipid metabolism, Taurine and hypotaurine metabolism, Phenylalanine metabolism, Glutathione metabolism and Glycerophospholipid metabolism, etc. were explored. In addition, the system pharmacology paradigm revealed that HQD contains 141 active ingredients and is related to 265 genes, and 1404 disease genes are related to DLI. The construction of the HQD composition-target-DLI network identified a total of 161 intersection genes. We identified 10 key genes, which is partially compatible with the results of metabolomics. The integrated approach metabolomics and network pharmacology revealed that additional detailed investigation focused on five major targets, including CAT, PTGS2, MAPK3, AKT1, and MAPK8, and their essential metabolites (sphinganine, sphingosine, Glutahione, Oxidized gutahione, Dihydrolipoamide) and pathway (glycerol phospholipid metabolism and tryptophan metabolism). The significant affinity of the primary target for the HQD was confirmed by molecular docking. The results demonstrate that the combination of metabolomics and network pharmacology could be used to reflect the effects of HQD on the biological network and metabolic state of DLI and to evaluate the drug efficacy of HQD and its related mechanisms.

Dataset Information

Integrating Drug's Mode of Action into Quantitative Structure-Activity Relationships for Improved Prediction of Drug-Induced Liver Injury.

Publications

Integrating Drug's Mode of Action into Quantitative Structure-Activity Relationships for Improved Prediction of Drug-Induced Liver Injury.

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