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Chemical and Drug Induced Liver Injury

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Prediction of Drug-Induced Liver Toxicity Using SVM and Optimal Descriptor Sets.

International journal of molecular sciences
Drug-induced liver toxicity is one of the significant safety challenges for the patient's health and the pharmaceutical industry. It causes termination of drug candidates in clinical trials and also the retractions of approved drugs from the market. ...

Derivation, characterisation and analysis of an adverse outcome pathway network for human hepatotoxicity.

Toxicology
Adverse outcome pathways (AOPs) and their networks are important tools for the development of mechanistically based non-animal testing approaches, such as in vitro and/or in silico assays, to assess toxicity induced by chemicals. In the present study...

Machine Learning Approaches to Predict Hepatotoxicity Risk in Patients Receiving Nilotinib.

Molecules (Basel, Switzerland)
Although nilotinib hepatotoxicity can cause severe clinical conditions and may alter treatment plans, risk factors affecting nilotinib-induced hepatotoxicity have not been investigated. This study aimed to elucidate the factors affecting nilotinib-i...

An artificial intelligence algorithm for analyzing acetaminophen-associated toxic hepatitis.

Human & experimental toxicology
INTRODUCTION: Very little artificial intelligence (AI) work has been performed to investigate acetaminophen-associated hepatotoxicity. The objective of this study was to develop an AI algorithm for analyzing weighted features for toxic hepatitis afte...

Application of Supervised SOM Algorithms in Predicting the Hepatotoxic Potential of Drugs.

International journal of molecular sciences
The hepatotoxic potential of drugs is one of the main reasons why a number of drugs never reach the market or have to be withdrawn from the market. Therefore, the evaluation of the hepatotoxic potential of drugs is an important part of the drug devel...

Drug properties and host factors contribute to biochemical presentation of drug-induced liver injury: a prediction model from a machine learning approach.

Archives of toxicology
Drug-induced liver injury (DILI) presentation varies biochemically and histologically. Certain drugs present quite consistent injury patterns, i.e., DILI signatures. In contrast, others are manifested as broader types of liver injury. The variety of ...

Trade-off Predictivity and Explainability for Machine-Learning Powered Predictive Toxicology: An in-Depth Investigation with Tox21 Data Sets.

Chemical research in toxicology
Selecting a model in predictive toxicology often involves a trade-off between prediction performance and explainability: should we sacrifice the model performance to gain explainability or vice versa. Here we present a comprehensive study to assess a...

Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure.

Biology direct
BACKGROUND: Drug-induced liver injury (DILI) is a major safety concern characterized by a complex and diverse pathogenesis. In order to identify DILI early in drug development, a better understanding of the injury and models with better predictivity ...

An ensemble learning approach for modeling the systems biology of drug-induced injury.

Biology direct
BACKGROUND: Drug-induced liver injury (DILI) is an adverse reaction caused by the intake of drugs of common use that produces liver damage. The impact of DILI is estimated to affect around 20 in 100,000 inhabitants worldwide each year. Despite being ...