Dataset Information

An In Silico Model for Predicting Drug-Induced Hepatotoxicity.

ABSTRACT: As one of the leading causes of drug failure in clinical trials, drug-induced liver injury (DILI) seriously impeded the development of new drugs. Assessing the DILI risk of drug candidates in advance has been considered as an effective strategy to decrease the rate of attrition in drug discovery. Recently, there have been continuous attempts in the prediction of DILI. However, it indeed remains a huge challenge to predict DILI successfully. There is an urgent need to develop a quantitative structure-activity relationship (QSAR) model for predicting DILI with satisfactory performance. In this work, we reported a high-quality QSAR model for predicting the DILI risk of xenobiotics by incorporating the use of eight effective classifiers and molecular descriptors provided by Marvin. In model development, a large-scale and diverse dataset consisting of 1254 compounds for DILI was built through a comprehensive literature retrieval. The optimal model was attained by an ensemble method, averaging the probabilities from eight classifiers, with accuracy (ACC) of 0.783, sensitivity (SE) of 0.818, specificity (SP) of 0.748, and area under the receiver operating characteristic curve (AUC) of 0.859. For further validation, three external test sets and a large negative dataset were utilized. Consequently, both the internal and external validation indicated that our model outperformed prior studies significantly. Data provided by the current study will also be a valuable source for modeling/data mining in the future.

SUBMITTER: He S

PROVIDER: S-EPMC6515336 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Publications

An In Silico Model for Predicting Drug-Induced Hepatotoxicity.

He Shuaibing S Ye Tianyuan T Wang Ruiying R Zhang Chenyang C Zhang Xuelian X Sun Guibo G Sun Xiaobo X

International journal of molecular sciences 20190417 8

As one of the leading causes of drug failure in clinical trials, drug-induced liver injury (DILI) seriously impeded the development of new drugs. Assessing the DILI risk of drug candidates in advance has been considered as an effective strategy to decrease the rate of attrition in drug discovery. Recently, there have been continuous attempts in the prediction of DILI. However, it indeed remains a huge challenge to predict DILI successfully. There is an urgent need to develop a quantitative struc ...[more]

PMID: 30999595

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Project description:Quantitative structure-activity relationship (QSAR) modeling and toxicogenomics are typically used independently as predictive tools in toxicology. In this study, we evaluated the power of several statistical models for predicting drug hepatotoxicity in rats using different descriptors of drug molecules, namely, their chemical descriptors and toxicogenomics profiles. The records were taken from the Toxicogenomics Project rat liver microarray database containing information on 127 drugs ( http://toxico.nibio.go.jp/datalist.html ). The model end point was hepatotoxicity in the rat following 28 days of continuous exposure, established by liver histopathology and serum chemistry. First, we developed multiple conventional QSAR classification models using a comprehensive set of chemical descriptors and several classification methods (k nearest neighbor, support vector machines, random forests, and distance weighted discrimination). With chemical descriptors alone, external predictivity (correct classification rate, CCR) from 5-fold external cross-validation was 61%. Next, the same classification methods were employed to build models using only toxicogenomics data (24 h after a single exposure) treated as biological descriptors. The optimized models used only 85 selected toxicogenomics descriptors and had CCR as high as 76%. Finally, hybrid models combining both chemical descriptors and transcripts were developed; their CCRs were between 68 and 77%. Although the accuracy of hybrid models did not exceed that of the models based on toxicogenomics data alone, the use of both chemical and biological descriptors enriched the interpretation of the models. In addition to finding 85 transcripts that were predictive and highly relevant to the mechanisms of drug-induced liver injury, chemical structural alerts for hepatotoxicity were identified. These results suggest that concurrent exploration of the chemical features and acute treatment-induced changes in transcript levels will both enrich the mechanistic understanding of subchronic liver injury and afford models capable of accurate prediction of hepatotoxicity from chemical structure and short-term assay results.

Project description:Many oncology drugs have been found to induce cardiotoxicity in a subset of patients, which significantly limits their clinical use and impedes the benefit of lifesaving anti-cancer treatments. Human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) carry donor-specific genetic information and have been proposed for explore the inter-individual difference in oncology drug-induced cardiotoxicity. Herein, we evaluated the inter- and intra- individual variability of iPSC-CM-related assays and presented a practical approach for using donor-specific iPSC-CMs to predict personalized doxorubicin (DOX)-induced cardiotoxicity (DIC) prior to chemotherapy. Our findings demonstrated that donor-specific iPSC-CMs exhibited greater line-to-line variability than the intra-individual variability in impedance cytotoxicity and transcriptome assays. The variable and dose-dependent cytotoxic responses of iPSC-CMs resembled those observed in clinical practice, and largely replicated the reported mechanisms of DIC. By categorizing iPSC-CMs into DOX-resistant and DOX-sensitive cell lines based on their phenotypic reactions to DOX, we found that the sensitivity of donor-specific iPSC-CMs to DOX may predict in vivo DIC risk. Furthermore, we assessed the limitations of the model for identification of potential genetic/molecular biomarker and pinpointed a differentially expressed gene, DND microRNA-mediated repression inhibitor 1 (DND1), between the DOX-resistant and DOX-sensitive iPSC-CMs. We also discussed the selection of DOX dose and exposure duration for inter-individual variability of DIC assessment. Our results support the utility of donor-specific iPSC-CMs in assessing inter-individual difference and enabling personalized cardiotoxicity prediction. Further studies will encompass a large panel of donor-specific iPSC-CMs to investigate the role of the DND1 and known DIC genetic variants, and to identify potential novel molecular and genetic biomarkers for predicting DOX and other oncology drug-induced cardiotoxicity.

Dataset Information

An In Silico Model for Predicting Drug-Induced Hepatotoxicity.

Publications

An In Silico Model for Predicting Drug-Induced Hepatotoxicity.

Similar Datasets

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