Three-Level Hepatotoxicity Prediction System Based on Adverse Hepatic Effects

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 15 873 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(11 drugs or candidates that failed in clinical trials or were withdrawn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.