Three case reports: Temporal association between tyrosine-kinase inhibitor-induced hepatitis and immune checkpoint inhibitors in renal cell carcinoma

Liver Pathology Related to Onco-Therapeutic Agents

Oncotherapeutic agents can cause a wide range of liver injuries from elevated liver functions tests to fulminant liver failure. In this review, we emphasize a newer generation of drugs including immune checkpoint inhibitors, protein kinase inhibitors, monoclonal antibodies, and hormonal therapy. A few conventional chemotherapy agents are also discussed.

The safety profile of EGFR/ALK-TKIs administered immediately before or after ICIs in advanced NSCLC

BACKGROUND

As more therapeutic targets are being discovered in advanced non-small cell lung cancer (NSCLC), it is pivotal for clinicians to correctly sequence immune checkpoint inhibitors (ICIs) and tyrosine kinase inhibitors (TKIs) for delivery of safe and effective treatment. Our present study aimed to assess the safety profile of sequential treatment of TKIs and ICIs in advanced NSCLC.

METHODS

We retrospectively analyzed the data of 64 patients who underwent sequential treatment of EGFR/ALK-TKIs and ICIs, including all the EGFR/ALK-TKIs and ICIs approved by National Medical Products Administration (NMPA) in China.

RESULTS

The decrease in hemoglobin was the most common adverse event (54.5 % and 44.4 %) for all patients. For TKIs post-treatment with ICIs group, the incidence rate of decrease in white blood cells was 32.7 %. Liver toxicity was also common for this sequential therapy: treatment-related elevation in ALT (30.9 %) and AST (25.5 %). In addition, grade 3 or higher skin toxicity occurred in 2 patients, and grade 3 or higher neuritis was observed in 1 patient. Interstitial pneumonia was also observed in 1 patient. For patients within the group of TKIs pre-treatment with ICIs, the most common adverse event was hepatic toxicity, the elevation in ALT and AST was 33.3 % and 22.2 % respectively. It was worth noting that the incidence rate of grade 3 or higher elevation in ALT and AST was 22.2 %. Other adverse events such as blood toxicity, skin rash, and diarrhea were also observed in this sequential treatment, but most of which was slight.

CONCLUSION

Although the adverse event did not significantly increase in the sequential treatment pattern of our study, careful consideration should be given to the possibility of an increased risk of some adverse event when TKIs were pre/post-treated with ICIs.

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.

Pharmacogenetics in the treatment of gastrointestinal stromal tumors - an updated review

INTRODUCTION

Gastrointestinal stromal tumors (GIST) are the best example of a targeted therapy in solid tumors. The introduction of tyrosine kinase inhibitors (TKIs) deeply improved the prognosis of this tumor. However, a degree of inter-patient variability is still reported in response rates and pharmacogenetics may play an important role in the final clinical outcome.

AREAS COVERED

In this review, the authors provide an updated overview of the pharmacogenetic literature analyzing the role of polymorphisms in both GIST treatment efficacy and toxicity.

EXPERT OPINION

Besides the primary role of somatic DNA in dictating the clinical response to TKIs, several polymorphisms influencing their pharmacokinetics and pharmacodynamics have been identified as being potentially involved. In the last 10 years, many potential biomarkers have been proposed to predict clinical response and toxicity after TKI administration. However, the evidence is still too limited to promote a clinical translation. To date, the somatic mutational status represents the main player in clinical response to TKIs in GIST treatment; however, pharmacogenetics could still explain the degree of inter-patient variability observed in GIST patients. A combination of different theoretical approaches, experimental model systems, and statistical methods is clearly needed, in order to translate pharmacogenetics to clinical practice in the near future.