Severe cholestasis due to azathioprine in Behcet's disease

Severe azathioprine-induced liver injury 22 months after initiation of treatment

Drug-induced liver injury (DILI) is the leading cause of acute liver failure in high-income countries. Acute cholestasis is one of the most common forms of hepatotoxicity induced by azathioprine. It usually begins during the first year of treatment, with most cases reported during the first month. We describe an uncommon case of DILI that occurred after 22 months of drug administration. A woman in her 50s was hospitalised because of jaundice and asthenia. She had been treated with azathioprine for myasthenia gravis during the last 2 years. Acute cholestatic injury was diagnosed. After ruling out most common causes of cholestasis, azathioprine was withdrawn and subsequent histological findings in liver biopsy were consistent with drug-induced cholestatic liver damage. After discontinuation of azathioprine, biochemical parameters progressively normalised and remarkable clinical improvement was achieved. With this report, we suggest that azathioprine should be considered among the causes of liver injury, despite long treatment duration.

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.