The Association between Molecular Initiating Events and Drug-Induced Hiccups.

Hiccups can significantly reduce the quality of life of patients and can occur as a drug side effect. Previous reports have revealed sex-specific differences in the incidence of drug-induced hiccups. However, the pathogenesis of drug-induced hiccups remains unknown, and there is limited evidence on its treatment or prevention. This study examined molecular initiating events (MIEs), which are the starting point of adverse events, to investigate the drug-induced pathways of hiccups. We extracted drugs suspected to cause hiccups using the FDA Adverse Event Reporting System, a large database on adverse drug reactions. Information on drugs suspected to be associated with hiccups was extracted from the overall population and sex-specific subgroups were divided. In each data table, the predicted activity values of nuclear receptors and stress response pathways for each drug were calculated using the Toxicity Predictor, a machine-learning model. Transforming growth factor-beta and antioxidant response elements were considered an independent factor for hiccups in the male and female subgroups, respectively. This report first examined one of the mechanisms of drug-induced hiccups and identified MIEs associated with drug-induced hiccups. The use of an adverse event database and the machine-learning model, Toxicity Predictor, may be useful for generating hypotheses for other adverse effects with unknown mechanisms.

Progress in Predicting Ames Test Outcomes from Chemical Structures: An In-Depth Re-Evaluation of Models from the 1st and 2nd Ames/QSAR International Challenge Projects.

The Ames/quantitative structure-activity relationship (QSAR) International Challenge Projects, held during 2014-2017 and 2020-2022, evaluated the performance of various predictive models. Despite the significant insights gained, the rules allowing participants to select prediction targets introduced ambiguity in model performance evaluation. This reanalysis identified the highest-performing prediction model, assuming a 100% coverage rate (COV) for all prediction target compounds and an estimated performance variation due to changes in COV. All models from both projects were evaluated using balance accuracy (BA), the Matthews correlation coefficient (MCC), the F1 score (F1), and the first principal component (PC1). After normalizing the COV, a correlation analysis with these indicators was conducted, and the evaluation index for all prediction models in terms of the COV was estimated. In total, using 109 models, the model with the highest estimated BA (76.9) at 100% COV was MMI-VOTE1, as reported by Meiji Pharmaceutical University (MPU). The best models for MCC, F1, and PC1 were all MMI-STK1, also reported by MPU. All the models reported by MPU ranked in the top four. MMI-STK1 was estimated to have F1 scores of 59.2, 61.5, and 63.1 at COV levels of 90%, 60%, and 30%, respectively. These findings highlight the current state and potential of the Ames prediction technology.

Efficiency of pharmaceutical toxicity prediction in computational toxicology.

The adverse effects and toxicity of chemical substances pose substantial challenges in drug discovery and environmental science. Their management, most especially in the early development stage, is crucial in preventing costly failures in clinical trials. Predictive methodologies, such as computational toxicology, offer an effective means of managing risks, particularly for new compounds with insufficient post-marketing surveillance and those lacking information on adverse effects. Computational approaches have become increasingly important in environmental science, in which the sheer number and diversity of chemicals present similar challenges to toxicity control. Traditional animal-based evaluation methods are resource intensive, time consuming, and ethically problematic, making them unsuitable for use in assessing the vast compound range. It is an urgent task for the academic community to minimize the risks associated with drug discovery and environmental exposure. This study focuses on systems used to predict toxicity from chemical structure information and outlines the prediction accuracy and systems developed in Japan.

Disproportionality analysis of stomatitis associated with anticancer drugs using Japanese Adverse Drug Event Reporting Database.

INTRODUCTION: Anticancer drug-induced stomatitis can affect a patient's quality of life and the continuation of drug treatment. Although there have been reports of the occurrence of stomatitis associated with anticancer agents in clinical trials, few Japanese participants have been enrolled in clinical trials and have not been sufficiently investigated. In addition, there has been little attention on research on anticancer drugs associated with stomatitis by patient stratification with different carcinogenic sites. Therefore, the aim of this study was to determine the disproportionality associated with stomatitis for various types of anticancer drugs in different types of cancer patients using the Japan Spontaneous Adverse Event Reporting Database (JADER). METHODS: The aim of this study was to identify the disproportionality of stomatitis by analyzing the type of anticancer drug and cancer patients using the Japanese Pharmacovigilance Database. Data obtained from spontaneous reports of adverse events with more than 10 stomatitis outbreaks reported in the Japanese Adverse Drug Event Report database (JADER) between April 2004 and March 2023 were analyzed. The safety signal for an adverse event was defined as the lower limit of the 95% confidence interval of the reporting odds ratio of >1. RESULTS: There were 6178 reports of drugs associated with stomatitis. Among these, 41 drugs were suggested to be associated with stomatitis, and 41 drugs were detected as signals. These drugs were classified based on their efficacy: antipyrimidines (six drugs), folate metabolism antagonists (three drugs), alkylating agents (four drugs), platinum (three drugs), topoisomerase inhibitors (three drugs), microtubule inhibitors (three drugs), mTOR inhibitors (two drugs), kinase inhibitors (seven drugs), anti-growth factor antibodies (five drugs) immune checkpoint inhibitors (one drug), and others (four drugs). CONCLUSION: The drugs that may be associated with stomatitis were cell cycle-dependent drugs, epidermal growth factor receptor-tyrosine kinase inhibitors, and mTOR inhibitors. Thus, the use of JADER suggests that anti-growth factor antibodies and immune checkpoint inhibitors may be associated with stomatitis development.

Synthesis and Biological Evaluation of 2-Azolylmethylene-3-(2H)-benzofuranone Derivatives as Potent Monoamine Oxidases Inhibitors.

A series of 2-azolylmethylene-3-(2H)-benzofuranone derivatives, 2-indolylmethylene-3-(2H)-benzofuranone and 2-pyrrolylmethylene-3-(2H)-benzofuranone derivatives, were synthesized, and their monoamine oxidase (MAO) A and B inhibitory activities were evaluated. Compounds 1b, 3b, 6b, 7b, and 10b showed strong inhibitory activity against MAO-A, and compound 3b showed the highest potency and selectivity, with an IC50 value of 21 nM and a MAO-A selectivity index of 48. Compounds 3c, 4c, 9a, 9c, 10c, 11a, and 11c showed strong inhibitory activity against MAO-B, and compound 4c showed the highest potency and selectivity, with an IC50 value of 16 nM and a MAO-B selectivity index of >1100. Further analysis of these compounds indicated that compound 3b for MAO-A and compound 4c for MAO-B were competitive inhibitors, with Ki values of 10 and 6.1 nM, respectively. Furthermore, computational analyses, such as quantitative structure-activity relationship (QSAR) analysis of the 2-azolylmethylene-3-(2H)-benzofuranone derivatives conducting their pIC50 values with the Molecular Operating Environment (MOE) and Mordred, and molecular docking analysis using MOE-Dock supported that the 2-azolylmethylene-3-(2H)-benzofuranone derivatives are a privileged scaffold for the design and development of novel MAO inhibitors.

Analysis of Opioid-Related Adverse Events in Japan Using FAERS Database.

Adverse events associated with opioid use in palliative care have been extensively studied. However, predicting the occurrence of adverse events based on the specific opioid used remains difficult. This study aimed to comprehensively analyze the adverse events related to µ-opioid receptor stimulation of opioids approved in Japan and investigate the tendencies of adverse event occurrence among different opioids. We utilized the FDA Adverse Event Reporting System database to extract reported adverse events for opioids approved in Japan. Cluster analysis was performed on reporting odds ratios (RORs) of adverse event names among opioids to visualize relationships between opioids and adverse events, facilitating a comparative study of their classifications. We calculated the RORs of adverse events for the target opioids. Cluster analysis based on these RORs resulted in five broad clusters based on the reported adverse events: i.e., strong opioids, weak opioids, loperamide, tapentadol, and remifentanil. This study provides a comprehensive classification of the association between µ-opioid-receptor-stimulating opioids and adverse events.

Predictive Models Based on Molecular Images and Molecular Descriptors for Drug Screening.

Various toxicity and pharmacokinetic evaluations as screening experiments are needed at the drug discovery stage. Currently, to reduce the use of animal experiments and developmental expenses, the development of high-performance predictive models based on quantitative structure-activity relationship analysis is desired. From these evaluation targets, we selected 50% lethal dose (LD50), blood-brain barrier penetration (BBBP), and the clearance (CL) pathway for this investigation and constructed predictive models for each target using 636-11,886 compounds. First, we constructed predictive models using the DeepSnap-deep learning (DL) method and images of compounds as features. The calculated area under the curve (AUC) and balanced accuracy (BAC) were, respectively, 0.887 and 0.818 for LD50, 0.893 and 0.824 for BBBP, and 0.883 and 0.763 for the CL pathway. Next, molecular descriptors (MDs) of compounds were calculated using Molecular Operating Environment, alvaDesc, and ADMET Predictor to construct predictive models using the MD-based method. Using these MDs, we constructed predictive models using DataRobot. The calculated AUC and BAC were, respectively, 0.931 and 0.805 for LD50, 0.919 and 0.849 for BBBP, and 0.900 and 0.807 for the CL pathway. In this investigation, we constructed predictive models combining the DeepSnap-DL and MD-based methods. In ensemble models using the mean predictive probability of the DeepSnap-DL and MD-based methods, the calculated AUC and BAC were, respectively, 0.942 and 0.842 for LD50, 0.936 and 0.853 for BBBP, and 0.908 and 0.832 for the CL pathway, with improved predictive performance observed for all variables compared with either single method alone. Moreover, in consensus models that adopted only compounds for which the results of the two methods agreed, the calculated BAC for LD50, BBBP, and the CL pathway were 0.916, 0.918, and 0.847, respectively, indicating higher predictive performance than the ensemble models for all three variables. The predictive models combining the DeepSnap-DL and MD-based methods displayed high predictive performance for LD50, BBBP, and the CL pathway. Therefore, the application of this approach to prediction targets in various drug discovery screenings is expected to accelerate drug discovery.

Comprehensive Study of Drug-Induced Pruritus Based on Adverse Drug Reaction Report Database.

Drug-induced pruritus triggers a desire to scratch, thereby diminishing one's quality of life. Certain instances of this phenomenon follow complex mechanisms of action that diverge from histamine-mediated pathways, known contributors to pruritus. However, investigations into the relationship between drugs and pruritus are limited. In this study, data mining techniques were employed to comprehensively analyze the characteristics of drugs linked to pruritus, using the FDA's Adverse Event Reporting System (FAERS) data. Reports linked to pruritus demonstrated noteworthy differences in gender, age, and weight when compared with non-pruritus cases. Among the leading candidates for drugs prompting pruritus were ophthalmic drugs, systemic antibacterials, contrast media, dermatological antifungals, and dermatological preparations. A principal component analysis showed that the second principal component served as an indicator for distinguishing between onsets at mucous membranes or the skin's surface. Additionally, the third principal component functioned as an indicator for categorizing administration methods as either invasive or noninvasive. Furthermore, a hierarchical cluster analysis conducted on these obtained principal components revealed the potential for classifying drugs based on the site of pruritus onset and the method of drug administration. These findings contribute to the development of targeted prevention and treatment strategies for avoiding pruritus in clinical practice.

Analysis of Corticosteroid-Induced Glaucoma Using the Japanese Adverse Drug Event Reporting Database.

Glaucoma is the most common cause of blindness, which significantly reduces quality of life. Most glaucoma cases are primary glaucoma; nevertheless, many patients suffer from glaucoma caused by drugs, such as corticosteroids. A comprehensive review of the risks associated with corticosteroid-induced glaucoma is limited. Therefore, we used the Japanese Adverse Drug Event Reporting Database (JADER) published by the Pharmaceuticals and Medical Devices Agency (PMDA) to analyze the risk factors associated with glaucoma and the trends and characteristics of corticosteroid-induced glaucoma. We did not find sex or age differences associated with the onset of glaucoma. Hierarchical clustering and principal component analysis revealed that triamcinolone acetonide and betamethasone sodium phosphate, which are used around the eyes in Japan, are more likely to induce intraocular pressure (IOP) elevation compared with other corticosteroids. Increased IOP is a direct cause of glaucoma. Based on these findings, it may be necessary to limit or avoid the use of these corticosteroids.

Potentiation of Anticancer Activity of G2/M Blockers by Mild Hyperthermia.

BACKGROUND/AIM: Hyperthermia (HT), combined with chemotherapy, has been used to treat various types of cancer. This study aimed to investigate the HT-sensitivity of malignant and non-malignant cells, and then evaluate the combination effect of docetaxel (DTX) and a newly synthesized chromone derivative (compound A) with HT. MATERIALS AND METHODS: The number of viable cells was determined using the MTT method. Cell cycle distribution was analyzed using a cell sorter, and DNA fragmentation pattern was detected using agarose gel electrophoresis. RESULTS: Among 12 cultured cells, oral squamous cell carcinoma (OSCC), especially Ca9-22 cells, and myelogenous leukemia cells showed higher sensitivity to HT than lung carcinoma and glioblastoma cell lines, while normal oral cells were the most resistant. Cytotoxicity of DTX on Ca9-22 cells was maximum at 41-42°C and 45~60 min exposure to HT. DXT, compound A, and HT induced G2/M arrest of Ca-22 cells. Mild HT enhanced the DTX- and compound A-induced subG1 arrest, in a synergistic fashion. CONCLUSION: The combination G2/M blockers and mild-HT can potentially be used for the treatment of OSCC.

A Comparative Study of Tumor-Specificity and Neurotoxicity between 3-Styrylchromones and Anti-Cancer Drugs.

Background. Many anti-cancer drugs used in clinical practice cause adverse events such as oral mucositis, neurotoxicity, and extravascular leakage. We have reported that two 3-styrylchromone derivatives, 7-methoxy-3-[(1E)-2-phenylethenyl]-4H-1-benzopyran-4-one (Compound A) and 3-[(1E)-2-(4-hydroxyphenyl)ethenyl]-7-methoxy-4H-1-benzopyran-4-one (Compound B), showed the highest tumor-specificity against human oral squamous cell carcinoma (OSCC) cell lines among 291 related compounds. After confirming their superiority by comparing their tumor specificity with newly synthesized 65 derivatives, we investigated the neurotoxicity of these compounds in comparison with four popular anti-cancer drugs. Methods: Tumor-specificity (TSM, TSE, TSN) was evaluated as the ratio of mean CC50 for human normal oral mesenchymal (gingival fibroblast, pulp cell), oral epithelial cells (gingival epithelial progenitor), and neuronal cells (PC-12, SH-SY5Y, LY-PPB6, differentiated PC-12) to OSCC cells (Ca9-22, HSC-2), respectively. Results: Compounds A and B showed one order of magnitude higher TSM than newly synthesized derivatives, confirming its prominent tumor-specificity. Docetaxel showed one order of magnitude higher TSM, but two orders of magnitude lower TSE than Compounds A and B. Compounds A and B showed higher TSM, TSE, and TSN values than doxorubicin, 5-FU, and cisplatin, damaging OSCC cells at concentrations that do not affect the viability of normal epithelial and neuronal cells. QSAR prediction based on the Tox21 database suggested that Compounds A and B may inhibit the signaling pathway of estrogen-related receptors.

Adverse Event Profile of Azacitidine: Analysis by Route of Administration Using Japanese Pharmacovigilance Database.

INTRODUCTION: Azacitidine is a useful drug for myelodysplastic syndromes and acute myeloid leukemia. In clinical trials, hematologic toxicity and infection have been observed as adverse events (AEs) of this drug. However, information on the time to onset of high risk AEs and subsequent outcomes, as well as differences in the frequency of AEs due to the route of administration is lacking. In this study, we investigated azacitidine-induced AEs comprehensively using the Japanese Adverse Event Reporting Database (JADER) published by the Pharmaceuticals and Medical Devices Agency, with disproportionate analysis of AE incidence trends, time to onset, and subsequent outcomes. In addition, we analyzed the differences in AEs by route of administration and the number of days until the occurrence of AEs and generated hypotheses. METHODS: The study used JADER data reported from April 2004 to June 2022. Risk estimation was conducted using reported odds ratio. A signal was detected when the lower limit of the 95% confidence interval of the calculated ROR was ≥1. RESULTS: A total of 34 signals were detected as AEs due to azacitidine. Among them, 15 were hematologic toxicities and 10 were infections, which demonstrated a particularly high rate of death. Signals of AEs such as tumor lysis syndrome (TLS) and cardiac failure, which have been described in case reports, were also detected, and the rate of death after onset was high. In addition, more AEs generally occurred within the first month of treatment. CONCLUSION: The results of this study suggest that more attention should be paid to cardiac failure, hematologic toxicity, infection, and TLS. Because many patients in clinical trials have discontinued treatment due to serious AEs before the therapeutic effect became apparent, appropriate supportive care, dose reduction, and drug withdrawal are important for the continuation of treatment.

Identifying Crude Drugs in Kampo Medicines Associated with Drug-Induced Liver Injury Using the Japanese Adverse Drug Event Report Database: A Comprehensive Survey.

The current study aimed to identify the crude drugs associated with drug-induced liver injury (DILI) in 148 Kampo medicines prescribed throughout Japan using the Japanese Adverse Drug Event Report (JADER) database, a large-scale spontaneous reporting system in Japan. First, we tabulated the number of DILI reports from the report-based dataset and the background information from the patient-based dataset. Thereafter, we combined the 126 crude drugs into 104 crude drug groups to examine multicollinearity. Finally, the reporting odds ratios (RORs), 95% confidence intervals, p values for Fisher's exact test, and number of reports were calculated for each crude group to identify those associated with DILI. Notably, the number of adverse event reports for DILI (63,955) exceeded that for interstitial lung disease (51,347), the most common adverse event. In total, 78 crude drug groups (90 crude drugs) were reported to have an ROR > 1, a p < 0.05, and ≥10 reported cases. Our results highlight DILI as an essential issue, given that it was among the most frequently reported adverse drug reactions. We were able to clearly identify the crude drugs associated with DILI, which could help manage adverse drug reactions attributed to Kampo medicines and crude drugs.

Examination of Risk Factors and Expression Patterns of Atypical Femoral Fractures Using the Japanese Adverse Drug Event Report Database: A Retrospective Pharmacovigilance Study.

Atypical femoral fracture (AFF) is a rare complication related to the use of bisphosphonates (BPs). Herein, we analyzed the risk factors and onset patterns of AFF using the Japanese Adverse Drug Event Report database and reported the findings. First, the independent risk factors for AFF were gender (female), high body mass index, and medical history of osteoporosis, arthritis, and systemic lupus erythematosus (SLE). Drug-related risk factors for AFF included BPs (i.e., alendronic acid, ibandronic acid, etidronic acid, zoledronic acid, minodronic acid, risedronic acid), denosumab, prednisolone, lansoprazole, rabeprazole, exemestane, letrozole, eldecalcitol, and menatetrenone. Therefore, it appears that AFF is influenced by a combination of patient backgrounds and drugs, and that the risk of developing AFF is particularly high in patients with fragile bones (e.g., osteoporosis, arthritis, and SLE). Second, in the analysis of AFF onset patterns, the onset of AFF from BPs and denosumab took a long time (>1 year) to develop. Analysis using a Weibull distribution showed wear-out failure-type AFF onset for BPs and denosumab, and both osteoporosis and cancer patients with long-term administration of these drugs showed a tendency to have an increased risk of onset. AFF developed earlier in osteoporosis patients with long-term administration of BPs and denosumab than in cancer patients.

Estimating drug-induced liver injury risk by in vitro molecular initiation response and pharmacokinetic parameters for during early drug development.

Drug-induced liver injury (DILI) is a major factor influencing new drug withdrawal; therefore, an appropriate toxicity assessment at the preclinical stage is required. Previous in silico models have been established using compound information listed in large data sources, thereby limiting the DILI risk prediction for new drugs. Herein, we first constructed a model to predict DILI risk based on a molecular initiating event (MIE) predicted by quantitative structure-activity relationships, admetSAR parameters (e.g. cytochrome P450 reactivity, plasma protein binding, and water-solubility), and clinical information (maximum daily dose [MDD] and reactive metabolite [RM]) for 186 compounds. The accuracy of the models using MIE, MDD, RM, and admetSAR alone were 43.2%, 47.3%, 77.0%, and 68.9%, while the "predicted MIE + admetSAR + MDD + RM" model's accuracy was 75.7%. The contribution of MIE to the overall prediction accuracy was little effect or rather worsening it. However, it was considered that MIE was a valuable parameter and that it contributed to detect high DILI risk compounds in the early development stage. We next examined the effect of stepwise changes in MDD on altering the DILI risk and estimating the maximum safety dose (MSD) for clinical use based on structural information, admetSAR, and MIE parameters because it is important to estimate the dose that could prevent the DILI onset in clinical conditions. Low-MSD compounds might increase the DILI risk, as these compounds were classified as "most-DILI concern" at low doses. In conclusion, MIE parameters were especially useful to check the DILI concern compounds and to prevent the underestimation of DILI risk in the early stage of drug development.

Ensemble Learning, Deep Learning-Based and Molecular Descriptor-Based Quantitative Structure-Activity Relationships.

A deep learning-based quantitative structure-activity relationship analysis, namely the molecular image-based DeepSNAP-deep learning method, can successfully and automatically capture the spatial and temporal features in an image generated from a three-dimensional (3D) structure of a chemical compound. It allows building high-performance prediction models without extracting and selecting features because of its powerful feature discrimination capability. Deep learning (DL) is based on a neural network with multiple intermediate layers that makes it possible to solve highly complex problems and improve the prediction accuracy by increasing the number of hidden layers. However, DL models are too complex when it comes to understanding the derivation of predictions. Instead, molecular descriptor-based machine learning has clear features owing to the selection and analysis of features. However, molecular descriptor-based machine learning has some limitations in terms of prediction performance, calculation cost, feature selection, etc., while the DeepSNAP-deep learning method outperforms molecular descriptor-based machine learning due to the utilization of 3D structure information and the advanced computer processing power of DL.

Assessment of Time-to-onset and Outcome of Lung Adverse Events With Pomalidomide from a Pharmacovigilance Study.

BACKGROUND/AIM: Pomalidomide is an immunomodulatory drug that is used to treat multiple myeloma. We examined the time-to-onset and outcome of lung adverse events (LAEs) related to pomalidomide in Japanese patients based on information obtained from the spontaneous reporting system of the Japanese Adverse Drug Event Report database (JADER) of the Pharmaceuticals and Medical Devices Agency. PATIENTS AND METHODS: We analyzed adverse events (AEs) reports recorded between April 2004 and March 2021 from JADER. Data on LAEs were extracted, and the relative risk of AEs was estimated using the reporting odds ratio and 95% confidence interval. We analyzed 1,772,494 reports and identified 2,918 reports of AEs caused by pomalidomide. Of these, 253 LAEs were reportedly associated with pomalidomide. RESULTS: Signals were detected for five LAEs: pneumonia, pneumocystis jirovecii pneumonia, bronchitis, pneumonia bacterial, and pneumonia pneumococcal. Pneumonia was the most frequently mentioned condition (68.8%). The median time-to-onset of pneumonia was 66 days, but some cases of pneumonia occurred as late as 20 months after the start of administration. Fatal outcomes were observed in two of the five AEs wherein signals were detected and were due to pneumonia and bacterial pneumonia. CONCLUSION: Serious outcomes can occur after pomalidomide administration. It has been suggested that these LAEs occur relatively early after pomalidomide administration. Since some situations can result in fatal consequences, patients should be monitored for the emergence of these AEs over a prolonged period of time, especially for pneumonia.

The Influence of the Rapid Increase in the Number of Adverse Event Reports for COVID-19 Vaccine on the Disproportionality Analysis Using JADER.

BACKGROUND/AIM: The COVID-19 prophylactic vaccine for the prevention of coronavirus infection was approved in Japan on February 14, 2021. Adverse event reports for the vaccine were collected from the Japan Adverse Drug Event Relief (JADER) database, similar to those for drugs. Reported odds ratios (RORs) and proportional reporting ratios (PRRs) are commonly used in disproportionality analysis to detect safety signals. Therefore, adverse event reports from the vaccinated population may affect the detection of safety signals for the registered drugs. This study determined the impact of adverse event reports on the detection of safety signals for a COVID-19 prophylactic vaccine by analyzing the JADER database using disproportionality analysis. PATIENTS AND METHODS: We extracted data from the JADER dataset, in which the COVID-19 vaccine was reported as a suspected drug, and selected the top 10 adverse events in terms of the number of reports. We then extracted the top 30 drugs by the amount of information in the selected 10 adverse events and compared the changes in the number of signal detections with and without the COVID-19 vaccine report data. RESULTS: The total number of adverse events reported in the JADER database during the study period was 2,002,564. Of the total number of reports, 85,489 (4.3%) reported adverse events related to the COVID-19 vaccine. Of the top 30 drugs reported in the 10 selected adverse events, the ROR and PRR were found to be lower with the inclusion of COVID-19 vaccine data than without. Detection by ROR excluded 23 out of 245 drugs, and detection by PRR excluded 34 out of 204 drugs. CONCLUSION: The rapid increase in the number of adverse event reports for the COVID-19 vaccine in JADER may affect the detection of safety signals by disproportionality analysis.

Impact of Drugs and Patient Characteristics on Life Expectancy during the Induction Phase of Dialysis.

Various factors affect the prognosis of dialysis patients. Analysis of the drugs used and clinical and demographic characteristics of the patient at the time of dialysis initiation is a useful means of estimating prognosis. In this study, we investigated the drugs used by dialysis patients during the induction phase of dialysis and performed a detailed analysis of variables predictive of prognosis. Patients who underwent dialysis between June 1998 and January 2019 and died during this period were included in the study (n = 118). The induction phase of dialysis was defined as the first month after dialysis began. Dialysis duration was defined as the time between dialysis initiation and death. A univariate regression analysis was performed, with dialysis duration as the objective variable and the drugs used during the induction phase of dialysis, blood laboratory values, age at start of dialysis, sex, body height, body weight, medical history and cause of death as the explanatory variables. In addition, multiple logistic regression analysis with stepwise variable selection of significant factors was performed to determine the factors related to dialysis duration. Antihypertensives, hemoglobin (Hb), and age at start of dialysis were found to have significant effects on dialysis duration. It was posited that antihypertensives prolong dialysis duration, thereby improving life expectancy. The regression model developed allowed estimation of prognosis based on the drugs used during the induction phase of dialysis and patient characteristics. These findings may be used to improve drug adherence in dialysis patients and guide physicians in their treatment.

Analysis of drug-induced liver-related adverse event trend reporting between 1997 and 2019.

AIM: This study aimed to analyze the current trends of drug-induced liver-related adverse events in the Food and Drug Administration Adverse Event Reporting System (FAERS) and Japanese Adverse Drug Event Report (JADER) databases. METHODS: The characteristics of implicated drugs were investigated by analyzing big data on drug-induced liver-related adverse events over the past 20 years in FAERS, comparing drug rankings between the JADER and FAERS databases, and calculating rankings of drugs inducing liver-related adverse events using the Medical Dictionary for Regulatory Activities Terminology. RESULTS: In the 452 272 cases registered in FAERS from 1997 to 2019, warfarin, paracetamol, and adalimumab were the drugs most related to drug-induced liver injury (DILI). In the 38 919 cases registered in JADER from 2004 to 2019, sorafenib, nivolumab, and herbal extracts were the drugs most related to DILI. No associations were found between the top 30 drugs in either of the two databases. Notably, the number of drug-induced liver-related adverse event reports and total adverse events has sharply increased in recent years. CONCLUSIONS: Although liver-related adverse events are largely caused by host immunity and other constitutional factors, differences in primary diseases, countries, and historical backgrounds lead to differences in the number of reports. Securing an appropriate database and a mechanism to collect real-time information on the frequency of adverse drug reactions is warranted.