Acute Portal and Superior Mesenteric Vein Thrombosis with Topical Testosterone Therapy: An Adverse Drug Event Case Report

This is a case report of a 55-year-old Caucasian male prescribed topical testosterone therapy for 12 months prior to admission, when he was diagnosed with acute thrombosis in the portal vein (PVT) and superior mesenteric vein (SMV). The patient had a negative thrombophilia workup, including Factor V Leiden, Prothrombin G20210A, and JAK2 V617F mutations. There were no other pertinent laboratory markers that raised concern for the cause of thrombus. No strong familial history of venous thromboembolism (VTE) was reported during the patient’s initial workup. With this in mind, the patient’s use of topical testosterone therapy was considered the most likely risk factor for the PVT and SMV thrombus. During hospitalization, the patient was initiated on therapeutic anticoagulation with a heparin drip and discharged to home on apixaban for 3 months with extended therapy to be determined by outpatient hematologist. With no other identified VTE risk factors, probability that this patient’s VTE was attributed to testosterone was evaluated using the Naranjo scale with a calculated score of 6, which classifies the adverse reaction as “likely.” Clinicians should be aware of the possibility that topical testosterone therapy may be a risk factor for venous thrombosis in unusual sites.

Analysis of the Full-Size Russian Corpus of Internet Drug Reviews with Complex NER Labeling Using Deep Learning Neural Networks and Language Models

The paper presents the full-size Russian corpus of Internet users’ reviews on medicines with complex named entity recognition (NER) labeling of pharmaceutically relevant entities. We evaluate the accuracy levels reached on this corpus by a set of advanced deep learning neural networks for extracting mentions of these entities. The corpus markup includes mentions of the following entities: medication (33,005 mentions), adverse drug reaction (1778), disease (17,403), and note (4490). Two of them—medication and disease—include a set of attributes. A part of the corpus has a coreference annotation with 1560 coreference chains in 300 documents. A multi-label model based on a language model and a set of features has been developed for recognizing entities of the presented corpus. We analyze how the choice of different model components affects the entity recognition accuracy. Those components include methods for vector representation of words, types of language models pre-trained for the Russian language, ways of text normalization, and other pre-processing methods. The sufficient size of our corpus allows us to study the effects of particularities of annotation and entity balancing. We compare our corpus to existing ones by the occurrences of entities of different types and show that balancing the corpus by the number of texts with and without adverse drug event (ADR) mentions improves the ADR recognition accuracy with no notable decline in the accuracy of detecting entities of other types. As a result, the state of the art for the pharmacological entity extraction task for the Russian language is established on a full-size labeled corpus. For the ADR entity type, the accuracy achieved is 61.1% by the F1-exact metric, which is on par with the accuracy level for other language corpora with similar characteristics and ADR representativeness. The accuracy of the coreference relation extraction evaluated on our corpus is 71%, which is higher than the results achieved on the other Russian-language corpora.

Combining a Pharmacological Network Model with a Bayesian Signal Detection Algorithm to Improve the Detection of Adverse Drug Events

Introduction: Improving adverse drug event (ADE) detection is important for post-marketing drug safety surveillance. Existing statistical approaches can be further optimized owing to their high efficiency and low cost.Objective: The objective of this study was to evaluate the proposed approach for use in pharmacovigilance, the early detection of potential ADEs, and the improvement of drug safety.Methods: We developed a novel integrated approach, the Bayesian signal detection algorithm, based on the pharmacological network model (ICPNM) using the FDA Adverse Event Reporting System (FAERS) data published from 2004 to 2009 and from 2014 to 2019Q2, PubChem, and DrugBank database. First, we used a pharmacological network model to generate the probabilities for drug-ADE associations, which comprised the proper prior information component (IC). We then defined the probability of the propensity score adjustment based on a logistic regression model to control for the confounding bias. Finally, we chose the Side Effect Resource (SIDER) and the Observational Medical Outcomes Partnership (OMOP) data to evaluate the detection performance and robustness of the ICPNM compared with the statistical approaches [disproportionality analysis (DPA)] by using the area under the receiver operator characteristics curve (AUC) and Youden’s index.Results: Of the statistical approaches implemented, the ICPNM showed the best performance (AUC, 0.8291; Youden’s index, 0.5836). Meanwhile, the AUCs of the IC, EBGM, ROR, and PRR were 0.7343, 0.7231, 0.6828, and 0.6721, respectively.Conclusion: The proposed ICPNM combined the strengths of the pharmacological network model and the Bayesian signal detection algorithm and performed better in detecting true drug-ADE associations. It also detected newer ADE signals than a DPA and may be complementary to the existing statistical approaches.

Pharmaceutical Care Network Europe (PCNE) Drug-Related Problems Classification Version 9.1: First Implementation in Sudan

Background: Clinical pharmacy services are an emerging specialty in Sudan. Many tools exist to document drug-related problems (DRP), such as the Pharmaceutical Care Network Europe (PCNE) classification. However, none has been attempted and published in Sudan. Objectives: The study aimed to identify the DRP and its characteristics in real hospital setting using non-modified version of PCNE. Method: Prospective study of clinical pharmacists' interventions during the routine care work of reviewing patients over a period from December 2020 to February 2021 at the wards of National Cancer Institute, University of Gezira, Sudan. Main outcome measure Using non-modified PCNE version 9.1 to identify the number, types, causes of the DRP, clinical pharmacists' interventions, acceptance, and outcomes. Results: Five minutes (range, 3-15 minutes) was the median time spent for evaluation and intervention by the clinical pharmacists, a total of 51 DRP were discovered among 40 patients with an average of 1.3 DRP per patient, an adverse drug event (possibly) occurring (29.4%) was the main problem, no or incomplete drug treatment (27.5%) was the main causes, above one-third of the clinical pharmacists' interventions were proposed to the prescriber, these interventions were accepted in 96% and fully implemented among 72.5% of the cases. At the end of the process, the majority of DRP (72.5%) were totally solved. Conclusion: Non-modified PCNE version 9.1 provides a suitable tool for the DRP process for Sudanese clinical pharmacists during routine work in the oncology setting. It hence can be considered as an optimal tool for further quality and policymaking.

Comprehensive Analysis of Bortezomib-Induced Adverse Events Using the Japanese Real-World Database

Background: Bortezomib is used as first-line therapy for multiple myeloma. Observational studies based on the FDA Adverse Event Reporting System (FAERS) database analysis and systematic reviews indicate that the incidence of peripheral neuropathy and tumor lysis syndrome (TLS) tends to be higher with bortezomib than that of other drugs. In a comprehensive analysis assessing drugs that cause peripheral neuropathy in Japanese patients, the incidence of bortezomib-induced adverse events (AEs) was reportedly high. However, a comprehensive assessment of bortezomib is lacking. Objectives: The purpose of this study was to determine the frequency of bortezomib AEs in Japanese patients and to determine the incidence, time to onset, and post hoc outcomes of unique AEs using the Japanese Adverse Drug Event Report (JADER) database. Method: To investigate the association between bortezomib and AEs, we analyzed the JADER database, which contains spontaneous AE reports submitted to the Pharmaceuticals and Medical Devices Agency from April 2004 to December 2020. Criteria indicating the presence of an AE signal were met when the following requirements were fulfilled: proportional reporting ratios (PRR) ≥ 2 and χ2 ≥ 4. Time to onset and post-event outcomes were analyzed for characteristic AEs. Results: Among 26 extracted AEs, 13 presented AE signals. The post-exposure outcomes of 12 AEs showed fatal outcomes at rates exceeding 10%, including cardiac failure (30%), lung disorder (24%), pneumonia (18%), and TLS (10%). Furthermore, a histogram of time to onset revealed that the 12 AEs were concentrated from the beginning to approximately one month after bortezomib administration. The median onset times for cardiac failure, lung disorder, pneumonia, and TLS were 28, 13, 42, and 5 days, respectively. Conclusions: Cardiac failure, lung disorder, pneumonia, and TLS had a higher rate of fatal clinical outcomes after onset than other AEs. These AEs exhibited a greater onset tendency in the early post-dose period. This study suggests that there is a need to monitor signs of cardiac failure, lung disorder, pneumonia, and TLS, potentially resulting in serious outcomes.

Exploring the Mechanisms Underlying Drug-Induced Fractures Using the Japanese Adverse Drug Event Reporting Database

Fractures occur when bones become fragile and are subjected to external forces as occurring during falls. The use of drugs that increase bone fragility or fall risk increases the risk of fracture. This study investigates drug-induced fractures reported in the Japanese Adverse Drug Event Report (JADER) database in patients using 4892 drugs. Atypical femur fracture was the most frequently reported fracture, and 58 other fractures were also reported. Using Volcano plots and multiple logistic regression analysis, we identified the risk factors for drug-induced fractures as being female, of older age, higher body mass index, and using one of 90 drugs. The drug groups significantly associated with drug-induced fractures included bone resorption inhibitors, antiviral drugs, dopaminergic drugs, corticosteroids, and sleep sedatives. Principal component analysis was used to examine the relationship between the use of specific drugs and the site of drug-induced fracture. Bone resorption inhibitors and corticosteroids were associated with atypical femur fractures, jaw fractures, and ulna fractures through an osteoclast-mediated process. Other drugs were found to increase fracture risk via non-osteoclast-mediated mechanisms. These findings suggest that many drugs can result in drug-induced fractures through a variety of mechanisms.

Analysis of anticholinergic adverse effects using two large databases: The US Food and Drug Administration Adverse Event Reporting System database and the Japanese Adverse Drug Event Report database

Introduction Anticholinergic adverse effects (AEs) are a problem for elderly people. This study aimed to answer the following questions. First, is an analysis of anticholinergic AEs using spontaneous adverse drug event databases possible? Second, what is the main drug suspected of inducing anticholinergic AEs in the databases? Third, do database differences yield different results? Methods We used two databases: the US Food and Drug Administration Adverse Event Reporting System database (FAERS) and the Japanese Adverse Drug Event Report database (JADER) recorded from 2004 to 2020. We defined three types of anticholinergic AEs: central nervous system (CNS) AEs, peripheral nervous system (PNS) AEs, and a combination of these AEs. We counted the number of cases and evaluated the ratio of drug–anticholinergic AE pairs between FAERS and JADER. We computed reporting odds ratios (RORs) and assessed the drugs using Beers Criteria®. Results Constipation was the most reported AE in FAERS. The ratio of drug–anticholinergic AE pairs was statistically significantly larger in FAERS than JADER. Overactive bladder agents were suspected drugs common to both databases. Other drugs differed between the two databases. CNS AEs were associated with antidementia drugs in FAERS and opioids in JADER. In the assessment using Beers Criteria®, signals were detected for almost all drugs. Between the two databases, a significantly higher positive correlation was observed for PNS AEs (correlation coefficient 0.85, P = 0.0001). The ROR was significantly greater in JADER. Conclusions There are many methods to investigate AEs. This study shows that the analysis of anticholinergic AEs using spontaneous adverse drug event databases is possible. From this analysis, various suspected drugs were detected. In particular, FAERS had many cases. The differences in the results between the two databases may reflect differences in the reporting countries. Further study of the relationship between drugs and CNS AEs should be conducted.