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Three-Level Hepatotoxicity Prediction System Based on Adverse Hepatic Effects
Lu Liu1; Jin-Wei Zhang1; Hui Wei1; Wen-Ling Ye1; Zhen-Ke Deng1; Lin Zhang2; Yan Cheng1; Defang Ouyang3; Qian Cao4; Dong-Sheng Cao1
2019-01
Source PublicationMOLECULAR PHARMACEUTICS
ISSN1543-8384
Volume16Issue:1Pages:393-408
Abstract

Hepatotoxicity is a major cause of drug withdrawal from the market. To reduce the drug attrition induced by hepatotoxicity, an accurate and efficient hepatotoxicity prediction system must be constructed. In the present study, we constructed a three-level hepatotoxicity prediction system based on different levels of adverse hepatic effects (AHEs) combined with machine learning, using (1) an end point, hepatotoxicity; (2) four hepatotoxicity severity degrees; and (3) specific AHEs. After collecting and curing 15873 compound-AHE pairs associated with 2017 compounds and 403 AHEs, we constructed 27 models with three end point levels with the random forest algorithm, and obtained accuracies ranging from 67.0 to 78.2% and the area under receiver operating characteristic curves (AUCs) of 0.715−0.875. The 27 models were fully integrated into a tiered hepatotoxicity prediction system. The existence of hepatotoxicity existence, severity degree, and potential AHEs for a given compound could be inferred simultaneously and systematically. Thus, the tiered hepatotoxicity prediction system allows researchers to have significant confidence in confirming compound hepatotoxicity, analyzing hepatotoxicity from multiple perspectives, obtaining warnings for the potential hepatotoxicity severity, and even rapidly selecting the proper in vitro experiments for hepatotoxicity verification. We also applied three external sets (11 drugs or candidates that failed in clinical trials or were withdrawn from the market, the PharmGKB (offsides) database, and an herbal hepatotoxicity data set) to test and validate the prediction ability of our system. Furthermore, the hepatotoxicity prediction system was adapted into a flow framework based on the Konstanz Information Miner, which was made available for researchers.

KeywordHepatotoxicity Adverse Hepatic Effects Toxicity Risk Assessment Sar Random Forest
DOI10.1021/acs.molpharmaceut.8b01048
Indexed BySCI
Language英语
WOS Research AreaResearch & Experimental Medicine ; Pharmacology & Pharmacy
WOS SubjectMedicine, Research & Experimental ; Pharmacology & Pharmacy
WOS IDWOS:000455288900035
PublisherAMER CHEMICAL SOC, 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
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Cited Times [WOS]:3   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionInstitute of Chinese Medical Sciences
Corresponding AuthorQian Cao; Dong-Sheng Cao
Affiliation1.Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, People’s Republic of China
2.Hunan Key Laboratory of Processed Food for Special Medical Purpose Central South University of Forestry and Technology, Changsha 410004, People’s Republic of China
3.State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
4.Beijing Rehabilitation Hospital Affiliated to Capital Medical University, Beijing 100001, People’s Republic of China
Recommended Citation
GB/T 7714
Lu Liu,Jin-Wei Zhang,Hui Wei,et al. Three-Level Hepatotoxicity Prediction System Based on Adverse Hepatic Effects[J]. MOLECULAR PHARMACEUTICS,2019,16(1):393-408.
APA Lu Liu.,Jin-Wei Zhang.,Hui Wei.,Wen-Ling Ye.,Zhen-Ke Deng.,...&Dong-Sheng Cao.(2019).Three-Level Hepatotoxicity Prediction System Based on Adverse Hepatic Effects.MOLECULAR PHARMACEUTICS,16(1),393-408.
MLA Lu Liu,et al."Three-Level Hepatotoxicity Prediction System Based on Adverse Hepatic Effects".MOLECULAR PHARMACEUTICS 16.1(2019):393-408.
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