Login Register Cart 0
Generic placeholder image

Current Bioinformatics

Editor-in-Chief

ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Short Communication

DILI-Stk: An Ensemble Model for the Prediction of Drug-induced Liver Injury of Drug Candidates

Author(s): Jingyu Lee, Myeong-Sang Yu and Dokyun Na*

Volume 17, Issue 3, 2022

Published on: 17 March, 2022

Page: [296 - 303] Pages: 8

DOI: 10.2174/1574893617666211228113939

Price: $65

Abstract

Background: Drug-induced Liver Injury (DILI) is a leading cause of drug failure, accounting for nearly 20% of drug withdrawal. Thus, there has been a great demand for in silico DILI prediction models for successful drug discovery. To date, various models have been developed for DILI prediction; however, building an accurate model for practical use in drug discovery remains challenging.

Methods: We constructed an ensemble model composed of three high-performance DILI prediction models to utilize the unique advantage of each machine learning algorithm.

Results: The ensemble model exhibited high predictive performance, with an area under the curve of 0.88, sensitivity of 0.83, specificity of 0.77, F1-score of 0.82, and accuracy of 0.80. When a test dataset collected from the literature was used to compare the performance of our model with publicly available DILI prediction models, our model achieved an accuracy of 0.77, sensitivity of 0.82, specificity of 0.72, and F1-score of 0.79, which were higher than those of the other DILI prediction models. As many published DILI prediction models are not available for public access, which hinders in silico drug discovery, we made our DILI prediction model publicly accessible (http://ssbio.cau.ac.kr/software/dili/).

Conclusion: We expect that our ensemble model may facilitate advancements in drug discovery by providing a highly predictive model and reducing the drug withdrawal rate.

Keywords: Drug discovery, hepatotoxicity, machine learning, quantitative structure-activity relationship model, drug-induced liver injury, xenobiotics metabolism, xenobiotic metabolism.

« Previous
Graphical Abstract

[1]
Bajželj B, Drgan V. Hepatotoxicity modeling using counter-propagation artificial neural networks: Handling an imbalanced classification problem. Molecules 2020; 25(3): 481.
[http://dx.doi.org/10.3390/molecules25030481 ] [PMID: 31979300]
[2]
Sturgill MG, Lambert G. Xenobiotic-induced hepatotoxicity: Mechanisms of liver injury and methods of monitoring hepatic function. Clin Chem 1997; 43(8): 1512-26.
[http://dx.doi.org/10.1093/clinchem/43.8.1512]
[3]
Walker PA, Ryder S, Lavado A, Dilworth C, Riley RJ. The evolution of strategies to minimise the risk of human drug-induced liver injury (DILI) in drug discovery and development. Arch Toxicol 2020; 94(8): 2559-85.
[http://dx.doi.org/10.1007/s00204-020-02763-w ] [PMID: 32372214]
[4]
Ionescu C, Caira MR, Eds. Drug metabolism: Current concepts. Netherlands: Springer 2006.
[5]
Benedetti MS, Whomsley R, Poggesi I, et al. Drug metabolism and pharmacokinetics. Drug Metab Pharmacokinet 2009; 41(3): 344-90.
[http://dx.doi.org/10.1080/10837450902891295 ] [PMID: 19601718]
[6]
Andrade RJ, Robles M, Fernández-Castañer A, López-Ortega S, López-Vega MC, Lucena MI. Assessment of drug-induced hepatotoxicity in clinical practice: A challenge for gastroenterologists. World J Gastroenterol 2007; 13(3): 329-40.
[http://dx.doi.org/10.3748/wjg.v13.i3.329 ] [PMID: 17230599]
[7]
Chen M, Borlak J, Tong W. Predicting idiosyncratic drug-induced liver injury: Some recent advances. Expert Rev Gastroenterol Hepatol 2014; 8(7): 721-3.
[http://dx.doi.org/10.1586/17474124.2014.922871 ] [PMID: 24857265]
[8]
Remmer H. The role of theliver in drug metabolism. Am J Med 1970; 49(5): 617-29.
[http://dx.doi.org/10.1016/S0002-9343(70)80129-2 ] [PMID: 4924589]
[9]
Gregus Z, Ed. Mechanisms of toxicity. New York: McGraw-Hill Professional 2008.
[10]
Przybylak KR, Cronin MT. In silico models for drug-induced liver injury--current status. Expert Opin Drug Metab Toxicol 2012; 8(2): 201-17.
[http://dx.doi.org/10.1517/17425255.2012.648613 ] [PMID: 22248266]
[11]
Schroeter TS, Schwaighofer A, Mika S, et al. Estimating the domain of applicability for machine learning QSAR models: A study on aque-ous solubility of drug discovery molecules. J Comput Aided Mol Des 2007; 21(12): 651-64.
[http://dx.doi.org/10.1007/s10822-007-9160-9 ] [PMID: 18060505]
[12]
Ponzoni I, Sebastián-Pérez V, Requena-Triguero C, et al. Hybridizing feature selection and feature learning approaches in QSAR modeling for drug discovery. Sci Rep 2017; 7(1): 2403.
[http://dx.doi.org/10.1038/s41598-017-02114-3 ] [PMID: 28546583]
[13]
Liu Y, Ed. Drug design by machine learning: Ensemble learning for QSAR modeling. Proceedings of the fourth International Conference on Machine Learning and Applications. 2005 Dec 15-17; LA, USA. Los Angeles: IEEE 2006.
[14]
Chen M, Hong H, Fang H, et al. Quantitative structure-activity relationship models for predicting drug-induced liver injury based on FDA-approved drug labeling annotation and using a large collection of drugs. Toxicol Sci 2013; 136(1): 242-9.
[http://dx.doi.org/10.1093/toxsci/kft189 ] [PMID: 23997115]
[15]
He S, Ye T, Wang R, et al. An in silico model for predicting drug-induced hepatotoxicity. Int J Mol Sci 2019; 20(8): 1897.
[http://dx.doi.org/10.3390/ijms20081897 ] [PMID: 30999595]
[16]
Ai H, Chen W, Zhang L, et al. Predicting drug-induced liver injury using ensemble learning methods and molecular fingerprints. Toxicol Sci 2018; 165(1): 100-7.
[http://dx.doi.org/10.1093/toxsci/kfy121 ] [PMID: 29788510]
[17]
Liu Z, Shi Q, Ding D, Kelly R, Fang H, Tong W. Translating clinical findings into knowledge in drug safety evaluation--drug induced liver injury prediction system (DILIps). PLOS Comput Biol 2011; 7(12): e1002310.
[http://dx.doi.org/10.1371/journal.pcbi.1002310 ] [PMID: 22194678]
[18]
Ancuceanu R, Hovanet MV, Anghel AI, et al. Computational models using multiple machine learning algorithms for predicting drug hepa-totoxicity with the DILIrank dataset. Int J Mol Sci 2020; 21(6): 2114.
[http://dx.doi.org/10.3390/ijms21062114 ] [PMID: 32204453]
[19]
Xiong G, Wu Z, Yi J, et al. ADMETlab 2.0: An integrated online platform for accurate and comprehensive predictions of ADMET proper-ties. Nucleic Acids Res 2021; 49(W1): W5-W14.
[http://dx.doi.org/10.1093/nar/gkab255 ] [PMID: 33893803]
[20]
Banerjee P, Eckert AO, Schrey AK, Preissner R. ProTox-II: A webserver for the prediction of toxicity of chemicals. Nucleic Acids Res 2018; 46(W1): W257-63.
[http://dx.doi.org/10.1093/nar/gky318 ] [PMID: 29718510]
[21]
Liew CY, Lim YC, Yap CW. Mixed learning algorithms and features ensemble in hepatotoxicity prediction. J Comput Aided Mol Des 2011; 25(9): 855-71.
[http://dx.doi.org/10.1007/s10822-011-9468-3 ] [PMID: 21898162]
[22]
Zhu X, Kruhlak NL. Construction and analysis of a human hepatotoxicity database suitable for QSAR modeling using post-market safety data. Toxicology 2014; 321: 62-72.
[http://dx.doi.org/10.1016/j.tox.2014.03.009 ] [PMID: 24721472]
[23]
Center for Drug Evaluation and Research (U.S.). Orange book: Approved drug products with therapeutic equivalence evaluations. US Food Drug Adm 2013. Available from: 2013.https://www.accessdata.fda.gov/scripts/cder/ob/index.cfm
[24]
Xia J, Wright J, Adams CE. Five large Chinese biomedical bibliographic databases: Accessibility and coverage. Health Info Libr J 2008; 25(1): 55-61.
[http://dx.doi.org/10.1111/j.1471-1842.2007.00734.x]
[25]
Mauri A, Consonni V, Pavan M, Todeschini R. Dragon software: An easy approach to molecular descriptor calculations. Match (Mulh) 2007; 56(2): 237-48.
[26]
Eesa AS, Kh Arabo W. A normalization methods for backpropagation: A comparative study. Sci J Univ Zakho 2017; 5(4): 319-23.
[http://dx.doi.org/10.25271/2017.5.4.381]
[27]
Ranjan GSK, Kumar Verma A, Radhika S, Eds. K-nearest neighbors and grid search CV based real time fault monitoring system for indus-tries. Proceedings of the 2019 IEEE 5th International Conference for Convergence in Technology. 2019 March 29-31; Bombay, India. 2020.
[28]
Cao C, Wang Z. IMCStacking: Cost-sensitive stacking learning with feature inverse mapping for imbalanced problems. Knowl Base Syst 2018; 150: 27-37.
[http://dx.doi.org/10.1016/j.knosys.2018.02.031]
[29]
El-Rashidy N, El-Sappagh S, Abuhmed T, Abdelrazek S, El-Bakry HM. Intensive care unit mortality prediction: An improved patientspecific stacking ensemble model. IEEE Access 2020; 8: 133541-64.
[http://dx.doi.org/10.1109/ACCESS.2020.3010556]
[30]
Cockroft NT, Cheng X, Fuchs JR. STarFish: A stacked ensemble target fishing approach and its application to natural products. J Chem Inf Model 2019; 59(11): 4906-20.
[http://dx.doi.org/10.1021/acs.jcim.9b00489 ] [PMID: 31589422]
[31]
He H, Zhang W, Zhang S. A novel ensemble method for credit scoring: Adaption of different imbalance ratios. Expert Syst Appl 2018; 98: 105-17.
[http://dx.doi.org/10.1016/j.eswa.2018.01.012]
[32]
Williams ML, James WP, Rose MT. Variable segmentation and ensemble classifiers for predicting dairy cow behaviour. Biosyst Eng 2019; 178: 156-67.
[http://dx.doi.org/10.1016/j.biosystemseng.2018.11.011]
[33]
Layeghian Javan S, Sepehri MM, Layeghian Javan M, Khatibi T. An intelligent warning model for early prediction of cardiac arrest in sep-sis patients. Comput Methods Programs Biomed 2019; 178: 47-58.
[http://dx.doi.org/10.1016/j.cmpb.2019.06.010 ] [PMID: 31416562]
[34]
Kaplowitz N, Deleve LD. Drug-Induced Liver Disease. New York 2003.
[35]
Williams M. An encyclopedia of chemicals, drugs, and biologicals. NJ, USA: Merck & Co, Inc. 1989.
[36]
Kotsampasakou E, Montanari F, Ecker GF. Predicting drug-induced liver injury: The importance of data curation. Toxicology 2017; 389: 139-45.
[http://dx.doi.org/10.1016/j.tox.2017.06.003 ] [PMID: 28652195]
[37]
Wang Y, Xiao Q, Chen P, Wang B. In silico prediction of drug-induced liver injury based on ensemble classifier method. Int J Mol Sci 2019; 20(17): 4106.
[http://dx.doi.org/10.3390/ijms20174106 ] [PMID: 31443562]
[38]
Karlos S, Kostopoulos G, Kotsiantis S. A soft-voting ensemble based co-training scheme using static selection for binary classification problems. Algorithms 2020; 13(1): 26.
[http://dx.doi.org/10.3390/a13010026]
[39]
Le NQK, Do DT, Hung TNK, Lam LHT, Huynh TT, Nguyen NTK. A computational framework based on ensemble deep neural networks for essential genes identification. Int J Mol Sci 2020; 21(23): 9070.
[http://dx.doi.org/10.3390/ijms21239070 ] [PMID: 33260643]
[40]
Teschke R, Uetrecht J. Mechanism of idiosyncratic drug induced liver injury (DILI): Unresolved basic issues. Ann Transl Med 2021; 9(8): 730.
[http://dx.doi.org/10.21037/atm-2020-ubih-05 ] [PMID: 33987428]

Rights & Permissions Print Cite
Article Metrics
38
1
© 2024 Bentham Science Publishers | Privacy Policy