Distinguishing characteristics of exposure to biguanide and sulfonylurea anti-diabetic medications in the United States

Drug-induced hypoglycemia: a disproportionality analysis of the FAERS database

BACKGROUND

Hypoglycemia is an adverse event (AE) that cannot be ignored in clinical practice. This study aimed to identify the most common and top drugs associated with the risk of hypoglycemia based on the United States Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database.

RESEARCH DESIGN AND METHODS

We used OpenVigil 2.1 pharmacovigilance analytics platform to query FAERS database and data from 2004 to 2023 were retrieved. The Medical Dictionary for Regulatory Activities (MedDRA) was used to identify hypoglycemia cases, and DrugBank database was used to determine drug generic names.

RESULTS

A total of 11,155,106 AEs reports were identified, of which 28,443 (0.25%) were related to hypoglycemia. Metformin (6926 cases) was associated with most cases of hypoglycemia. According to the disproportionality analysis, the top five drugs with the highest ROR and PRR were penamecillin, nikethamide, sotagliflozin, norethandrolone, glimepiride/pioglitazone. Nineteen of the top 50 drugs did not have hypoglycemia indicated in the package insert.

CONCLUSIONS

By analyzing the FAERS database, we listed drugs with a strong hypoglycemic signal for which the label does not provide a reminder. Notably, the potential hypoglycemia risks are of great importance and should be closely monitored in medical practice.

Utility of artificial intelligence to identify antihyperglycemic agents poisoning in the USA: introducing a practical web application using National Poison Data System (NPDS)

Clinical effects of antihyperglycemic agents poisoning may overlap each other. So, distinguishing exposure to these pharmaceutical drugs may take work. This study examined the application of machine learning techniques in identifying antihyperglycemic agent exposure using the national poisoning database in the USA. In this study, the data of single exposure due to Biguanides and Sulfonylureas (n=6183) was requested from the National Poison Data System (NPDS) for 2014-2018. We have tried five machine learning models (random forest classifier, k-nearest neighbors, Xgboost classifier, logistic regression, neural network Keras). For the multiclass classification modeling, we have divided the dataset into two parts: train (75%) and test (25%). The performance metrics used were accuracy, specificity, precision, recall, and F1-score. The algorithms used to get the classification results of different models to diagnose antihyperglycemic agents were very accurate. The accuracy of our model in determining these two antihyperglycemic agents was 91-93%. The precision-recall curve showed average precision of 0.91, 0.97, 0.97, and 0.98 for k-nearest neighbors, logistic regression, random forest, and XGB, respectively. The logistic regression, random forest, and XGB had the highest AUC (AUC=0.97) among both biguanides and sulfonylureas groups. The negative predictive values (NPV) for all the models were between 89 and 93%. We introduced a practical web application to help physicians distinguish between these agents. Despite variations in accuracy among the different types of algorithms used, all of them could accurately determine the specific exposure to biguanides and sulfonylureas retrospectively. Machine learning can distinguish antihyperglycemic agents, which may be useful for physicians without any background in medical toxicology. Besides, Our suggested ML-based Web application might help physicians in their diagnosis.

Deep learning neural network derivation and testing to distinguish acute poisonings

INTRODUCTION

Acute poisoning is a significant global health burden, and the causative agent is often unclear. The primary aim of this pilot study was to develop a deep learning algorithm that predicts the most probable agent a poisoned patient was exposed to from a pre-specified list of drugs.

RESEARCH DESIGN & METHODS

Data were queried from the National Poison Data System (NPDS) from 2014 through 2018 for eight single-agent poisonings (acetaminophen, diphenhydramine, aspirin, calcium channel blockers, sulfonylureas, benzodiazepines, bupropion, and lithium). Two Deep Neural Networks (PyTorch and Keras) designed for multi-class classification tasks were applied.

RESULTS

There were 201,031 single-agent poisonings included in the analysis. For distinguishing among selected poisonings, PyTorch model had specificity of 97%, accuracy of 83%, precision of 83%, recall of 83%, and a F1-score of 82%. Keras had specificity of 98%, accuracy of 83%, precision of 84%, recall of 83%, and a F1-score of 83%. The best performance was achieved in the diagnosis of single-agent poisoning in diagnosing poisoning by lithium, sulfonylureas, diphenhydramine, calcium channel blockers, then acetaminophen, in PyTorch (F1-score = 99%, 94%, 85%, 83%, and 82%, respectively) and Keras (F1-score = 99%, 94%, 86%, 82%, and 82%, respectively).

CONCLUSION

Deep neural networks can potentially help in distinguishing the causative agent of acute poisoning. This study used a small list of drugs, with polysubstance ingestions excluded.Reproducible source code and results can be obtained at https://github.com/ashiskb/npds-workspace.git.

Sanghuangporus vaninii mixture ameliorated type 2 diabetes mellitus and altered intestinal microbiota in mice

Sanghuangporus vaninii mixture ameliorated type 2 diabetes mellitus through improving body weight, fasting blood glucose, insulin-related indicators, lipid indexes, inflammatory factors, histological pathology, and intestinal microbiota.