Detection of Anti-mitochondrial Antibodies Accompanied by Drug-induced Hepatic Injury due to Atorvastatin

Statin-induced Liver Injury Patterns: A Clinical Review

Since their introduction in 1987, hydroxymethyl glutaryl coenzyme A reductase (HMG-CoA) inhibitors, more commonly known as statins, have become some of the most widely prescribed medications in the world. Though generally considered to be safe and well tolerated, statins have been associated with several side effects including mild liver dysfunction manifested by increases in aminotransferases. Rarely, statins have been noted to induce more serious hepatic injury, including liver injury with autoimmune features. Current literature supports statin induced liver injury presenting in either hepatocellular or cholestatic patterns, though with the former being the prevailing pattern of injury. Fortunately, severe liver injury is uncommon with statin use and is generally reversible without any intervention other than offending statin cessation. When evaluating cases of suspected statin-induced liver injury, a complete medical history, laboratory tests including a complete metabolic panel, autoimmune markers, and viral panel, as well as hepatic imaging, are crucial for a complete causality analysis with validated tools such as Roussel Uclaf Causality Assessment Method. The aim of this review is to review the current evidence for statin-induced liver injury and cholestasis.

A Knowledge Graph Embedding Based Approach to Predict the Adverse Drug Reactions Using a Deep Neural Network

Recently Artificial Intelligence(AI) has not only been used to diagnose the disease but also to cure the disease. Researchers started using AI for drug discovery. Predicting the Adverse Drug Reactions(ADRs) caused by the drug in the manufacturing stage or in the clinical trial stage is a very important problem in drug discovery. ADRs have become a major concern resulting in injuries and also becoming fatal sometimes. Drug safety has gained much importance over the years propelling to the forefront investigation of predicting the ADRs. Although prior studies have queried diverse approaches to predict ADRs, very few were found to be effective. Also, the problem of having fewer reports makes the prediction of ADRs more difficult. To tackle this problem effectively, a novel method has been proposed in this paper. The proposed method is based on Knowledge Graph(KG) embedding. Using the KG embedding, we designed and trained a custom-made Deep Neural Network(DNN) called KGDNN(Knowledge Graph DNN) for predicting the ADRs. A KG has been constructed with 6 types of entities: drugs, ADRs, target proteins, indications, pathways, and genes. Using the Node2Vec algorithm, each node has been embedded into a feature space. Using those embeddings, the ADRs are classified by the KGDNN model. The proposed method has obtained an AUROC score of 0.917 and significantly outperformed the existing methods. Two case studies on drugs causing liver injury and COVID-19 recommended drugs have been performed to illustrate the model efficacy.

Prediction of adverse drug reactions based on knowledge graph embedding

BACKGROUND

Adverse drug reactions (ADRs) are an important concern in the medication process and can pose a substantial economic burden for patients and hospitals. Because of the limitations of clinical trials, it is difficult to identify all possible ADRs of a drug before it is marketed. We developed a new model based on data mining technology to predict potential ADRs based on available drug data.

METHOD

Based on the Word2Vec model in Nature Language Processing, we propose a new knowledge graph embedding method that embeds drugs and ADRs into their respective vectors and builds a logistic regression classification model to predict whether a given drug will have ADRs.

RESULT

First, a new knowledge graph embedding method was proposed, and comparison with similar studies showed that our model not only had high prediction accuracy but also was simpler in model structure. In our experiments, the AUC of the classification model reached a maximum of 0.87, and the mean AUC was 0.863.

CONCLUSION

In this paper, we introduce a new method to embed knowledge graph to vectorize drugs and ADRs, then use a logistic regression classification model to predict whether there is a causal relationship between them. The experiment showed that the use of knowledge graph embedding can effectively encode drugs and ADRs. And the proposed ADRs prediction system is also very effective.