Provenance-Centered Dataset of Drug-Drug Interactions

Over the years several studies have demonstrated the ability to identify potential drug-drug interactions via data mining from the literature (MEDLINE), electronic health records, public databases (Drugbank), etc. While each one of these approaches is properly statistically validated, they do not take into consideration the overlap between them as one of their decision making variables. In this paper we present LInked Drug-Drug Interactions (LIDDI), a public nanopublication-based RDF dataset with trusty URIs that encompasses some of the most cited prediction methods and sources to provide researchers a resource for leveraging the work of others into their prediction methods. As one of the main issues to overcome the usage of external resources is their mappings between drug names and identifiers used, we also provide the set of mappings we curated to be able to compare the multiple sources we aggregate in our dataset.Comment: In Proceedings of the 14th International Semantic Web Conference (ISWC) 201

Drug interactions and adverse events in elderly heart disease patients

Chronic and multiple diseases are more prevalent in elderly individuals and, epidemiological highlight can be given to cardiovascular conditions requiring multi-drug therapies, which favor the occurrence of drug interactions. This study aims to analyze potential drug interactions and correlate them with adverse events in elderly heart-disease patients in a hospital setting. This is a prospective description of the analysis of medical prescriptions and records of 80 patients, with data collection performed by using validated instruments during a seven-month period. The drug interactions found were indicated by scientifically recognized databases and subsequently treated statistically with adequate software. 1841 potential interactions between drugs were detected, of which 74.1% did not show any therapeutic benefits, with antithrombotic and analgesic drugs accounting for the worst results. Thenumber of potential interactions was proportional to the occurrence of adverse events, classified at 87.3% as moderate to severe. It is concluded from such results that there is a proportionality between the occurrence of potential drug interactions and the detection of adverse events, with therapeutic management being of great importance for safety, quality and affordability of the treatment

GNTeam at 2018 n2c2:Feature-augmented BiLSTM-CRF for drug-related entity recognition in hospital discharge summaries

Monitoring the administration of drugs and adverse drug reactions are key parts of pharmacovigilance. In this paper, we explore the extraction of drug mentions and drug-related information (reason for taking a drug, route, frequency, dosage, strength, form, duration, and adverse events) from hospital discharge summaries through deep learning that relies on various representations for clinical named entity recognition. This work was officially part of the 2018 n2c2 shared task, and we use the data supplied as part of the task. We developed two deep learning architecture based on recurrent neural networks and pre-trained language models. We also explore the effect of augmenting word representations with semantic features for clinical named entity recognition. Our feature-augmented BiLSTM-CRF model performed with F1-score of 92.67% and ranked 4th for entity extraction sub-task among submitted systems to n2c2 challenge. The recurrent neural networks that use the pre-trained domain-specific word embeddings and a CRF layer for label optimization perform drug, adverse event and related entities extraction with micro-averaged F1-score of over 91%. The augmentation of word vectors with semantic features extracted using available clinical NLP toolkits can further improve the performance. Word embeddings that are pre-trained on a large unannotated corpus of relevant documents and further fine-tuned to the task perform rather well. However, the augmentation of word embeddings with semantic features can help improve the performance (primarily by boosting precision) of drug-related named entity recognition from electronic health records

BIG DATA ANALYTICS IN PHARMACOVIGILANCE - A GLOBAL TREND

Big data analysis has enhanced its demand nowadays in various sectors of health-care including pharmacovigilance. The exact definition of big data is not known to many people though it is routinely used by them. Big data refer to immense and voluminous computerized medical information which are obtained from electronic health records, administrative data, registries related to disease, drug monitoring, etc. This data are usually collected from doctors and pharmacists in a health-care facility. Analysis of big data in pharmacovigilance is useful for early raising of safety alerts, line listing them for signal detection of drugs and vaccines, and also for their validation. The present paper is intended to discuss big data analytics in pharmacovigilance focusing on global prospect and domestic country-India

Predicting drug–drug interactions through drug structural similarities and interaction networks incorporating pharmacokinetics and pharmacodynamics knowledge

Additional file 1. Table S1. Average structural similarity scores for the DDI/non–DDI pairs in the network of each De. Table S2-1. Top 10 predicted drugs with DDIs for warfarin. Table S2-2. Top 10 predicted drugs with DDIs for simvastatin. Table S3. Four-fold cross-validation test results. Text S1. Drugs that show DDI (DrugBank ID). Figure S1. Illustration of construction of training and test set for 4-fold cross validation. Figure S2. ROC curves using the models with score set 1 in a 4-fold validation

Progressive multifocal leukoencephalopathy reports in rheumatoid arthritis concerning different treatment patterns-an exploratory assessment using the food and drug administration adverse event reporting system

Introduction: Progressive multifocal leukoencephalopathy (PML) is a rare but potentially life-threatening brain infection caused by the John Cunningham virus. PML is a known adverse effect associated with molecular-targeted drugs and immunosuppressive agents. Recent concerns have emerged regarding the link between methotrexate (MTX) and PML. However, limited information exists on the influence of concomitant drug use in rheumatoid arthritis (RA) treatment, where various medications are often used together.Methods: To explore treatment patterns and patient background that affect PML reporting in RA, we analyzed data on RA cases from the Food and Drug Administration Adverse Event Reporting System (FAERS; JAPIC AERS) database between 1997 and 2019.Results and Discussion: Our analysis revealed significantly elevated crude and adjusted reporting odds ratios (aROR) for MTX, rituximab (RIT), azathioprine, and cyclophosphamide. When considering treatment patterns, the concomitant use of MTX and RIT showed a higher aROR than using MTX or RIT alone. Additional TNF-α inhibitors or glucocorticoids did not increase PML reports. Moreover, male sex and older age were associated with increased PML reports. While limitations are inherent in studies using spontaneous reporting data, our exploratory assessment suggests an association between PML and the combination of MTX and RIT and a higher risk in men and older patients. These findings help enhance our understanding of PML risk factors in the context of RA treatment