The number of patients with Alzheimer’s disease is increasing annually. Most of these patients are older adults with comorbid physical illnesses, which means that they are often treated with a combination of medications for the disease they have and those for Alzheimer’s disease. Thus, older adults with Alzheimer’s disease are potentially at risk for polypharmacy. In addition, the drug interactions between Alzheimer’s disease medications and those for the treatment of physical illnesses may reduce their efficacy and increase side effects. This article reviews polypharmacy and drug interactions in elderly patients with Alzheimer’s disease, with a focus on psychotropic drugs.

Globally, the numbers of head and neck cancer (HNC) cases and related deaths have recently increased. In Japan, few studies have examined crude or age-adjusted HNC mortality rates. Therefore, this study aimed to determine the trends in crude and age-adjusted mortality rates for HNC per million individuals in Japan from 1999 to 2019. Data on HNC-associated deaths were extracted from the national death certificate database using the International Classification of Diseases, Tenth Revision (n = 156,742). HNC mortality trends were analysed using joinpoint regression models to estimate annual percentage change (APC) and average APC (AAPC). Among men, no significant change was observed in the age-adjusted death rate trend from 1999 to 2014; however, a marked decrease was observed from 2014 to 2019. No changing point was observed in women. Age-adjusted mortality rates continuously decreased over the 21-year period, with an AAPC of −0.7% in men and −0.6% in women. In conclusion, the overall trend in age-adjusted rates of HNC-associated deaths decreased, particularly among men, in the past 5 years. These results will contribute to the formulation of medical policies to develop targeted screening and prevention programmes for HNC in Japan and determine the direction of treatment strategies.

Background/Objectives: The risk of fractures associated with immune checkpoint inhibitors (ICIs) is increasing; however, the relationship between fracture risk and potential factors, such as osteoporosis and hyperthyroidism, remains unclear. Methods: Using VigiBase, the World Health Organization’s global pharmacovigilance database, we investigated the signals for osteoporosis, hyperthyroidism, and fractures associated with ICIs (nivolumab, pembrolizumab, atezolizumab, durvalumab, ipilimumab, and tremelimumab) by calculating information components (ICs) and their 95% confidence intervals (CIs). Furthermore, we estimated the association between the occurrence of fractures in patients receiving ICIs and osteoporosis or hyperthyroidism. Results: Signals of hyperthyroidism (IC = 4.66, 95% CI: 4.58–4.73), but not osteoporosis (IC = −1.79, 95% CI: −2.22 to −1.36) or fractures (IC = −0.21, 95% CI: −0.36 to −0.06), were detected in patients using ICIs. Osteoporosis (odds ratio: 118.00, 95% CI: 61.00–230.00) was associated with an increased reporting frequency of fractures related to ICIs, whereas hyperthyroidism (odds ratio: 0.60, 95% CI: 0.19–1.87) was not associated with such an increase. Conclusions: The VigiBase analysis indicates that the use of ICIs does not increase the reporting frequency of osteoporosis or fractures. Additionally, hyperthyroidism did not increase the reporting frequency of fractures associated with ICIs.

Alterations in bile acid metabolism have been observed in individuals with multiple sclerosis (MS), yet the therapeutic implications of bile acid supplementation remain uncertain. We conducted a two-stage study integrating pharmacovigilance analysis with preclinical validation to evaluate bile acid derivatives as candidate therapies for MS. A disproportionality analysis of the FDA Adverse Event Reporting System (FAERS; Q4/2003-Q2/2025) was performed to identify inverse associations between MS and bile acid preparations. The effects of ursodeoxycholic acid (UDCA) and obeticholic acid (6-ECDCA) were evaluated in a therapeutic experimental autoimmune encephalomyelitis (EAE) model, with treatment initiated after disease onset. Among 13,734,539 FAERS reports, 75,659 involved MS. Inverse associations were identified for UDCA (odds ratio [OR]: 0.197, 95% confidence interval [CI]: 0.117-0.333) and 6-ECDCA (OR: 0.128, 95% CI: 0.041-0.396). In the EAE model, UDCA was associated with lower clinical scores at the peak (day 18) and late phases (days 26-28), whereas 6-ECDCA showed only a non-significant trend toward improvement at day 28. This two-stage investigation highlights the potential utility of pharmacovigilance-guided approaches for identifying therapeutic candidates. Bile acid derivatives, particularly UDCA, are biologically plausible candidates meriting further investigation in the context of MS.

Immune checkpoint inhibitors (ICIs), essential in cancer therapy, can cause severe immune-related adverse events (irAEs), including myocarditis with a high fatality rate. Currently, the pathogenesis, biomarkers, and risk factors of ICI-induced myocarditis (ICIM) are not fully understood. This exploratory study aimed to develop machine learning-based models to predict the onset of ICIM within 3 months of starting ICI therapy, using a large health insurance database. The models were constructed using the Light Gradient Boosting Machine (LightGBM) and Random Forest algorithms, incorporating clinical variables such as comorbidities and prior medication classifications. In this study, a strategy combining undersampling and bagging was used to minimize the impact of highly imbalanced datasets. The Random Forest model demonstrated superior performance compared with the LightGBM model, and the SHapley Additive exPlanations (SHAP) analysis for the Random Forest model revealed that the concurrent use of ICIs was the most important variable for predictions. Although predictive performance remains limited (AUROC ≈ 0.63), this exploratory framework demonstrates the feasibility of developing data-driven risk prediction models for ICIM. Future studies with expanded datasets and integration of laboratory parameters are warranted to improve predictive accuracy and potential clinical applicability.