The objective of this study was to characterize the epidemiology of using potentially inappropriate medications associated with dementia exacerbation (DPIMs) in elderly outpatients with dementia. Electronic medical records were retrospectively reviewed for geriatric patients with dementia who were prescribed at least one medication in 2016 at a tertiary, university-affiliated hospital. The 2015 Beers criteria were used to define DPIMs. Logistic regression was performed to identify factors associated with prescribing DPIMs in patients with dementia. Among 2100 patients included in our study, 987 (47.0%) patients were prescribed at least one DPIM. Benzodiazepines were the most frequently prescribed DPIM followed by anticholinergics, histamine H2-receptor blockers, and zolpidem. The risk of prescribing DPIMs was significantly increased in female patients (odds ratio (OR) 1.355) with polypharmacy (OR 5.146) and multiple comorbidities (OR 1.129) (p < 0.05 for all). Coexistence of Parkinson’s disease (OR 1.799), mood disorder (OR 1.373), or schizophrenia (OR 4.116) in patients with dementia further increased the likelihood of receiving DPIMs. In conclusion, DPIMs were commonly used in elderly patients with dementia in Korea with benzodiazepines most frequently prescribed followed by anticholinergics. Female patients using polypharmacy with multiple comorbidities should be closely monitored to minimize unnecessary DPIM use and, ultimately, DPIM-related harms.

Background/Objectives: Ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized by relapsing inflammation and incomplete response to conventional therapies. Although biologics have advanced UC management, many patients with moderate-to-severe disease experience treatment failure, relapse, or adverse effects. This review evaluates the pharmacology, efficacy, and safety of oral Janus kinase (JAK) inhibitors—tofacitinib, upadacitinib, and filgotinib—to guide their clinical use in UC. Methods: A comprehensive literature review was conducted using the PubMed, Embase, Cochrane, and Web of Science databases to identify relevant studies on JAK inhibitors in UC. The review included Phase 3 randomized controlled trials (RCTs), real-world observational studies, and recent network meta-analyses. We assessed pharmacologic profiles, clinical efficacy, and safety data for tofacitinib, upadacitinib, and filgotinib. Additionally, we reviewed emerging pipeline agents and future directions in oral immunomodulatory therapy for UC. Results: All three agents demonstrated efficacy in the induction and maintenance of remission. Upadacitinib showed superior performance, including rapid symptom control, high clinical remission rates, and favorable long-term outcomes in both biologic-naïve and -experienced patients. Tofacitinib offered strong efficacy, particularly in early response, but was associated with higher risks of herpes zoster and thromboembolic events. Filgotinib provided moderate efficacy with a favorable safety profile, making it suitable for risk-averse populations. Meta-analyses consistently ranked upadacitinib highest in clinical efficacy and onset of action. Conclusions: JAK inhibitors offer effective and convenient oral treatment options for moderate-to-severe UC. Upadacitinib emerges as a high-efficacy agent; tofacitinib and filgotinib remain valuable based on patient-specific risk profiles. Future studies are needed to clarify optimal sequencing, long-term safety, and the role of emerging agents or combination therapies.

Background/Objectives: This study aimed to develop a fully validated HPLC-MS/MS method for quantifying total and unbound lenalidomide concentrations in human plasma. Methods: Unbound concentrations were measured using plasma ultrafiltrate prepared with Amicon^® Centrifugal Filters. Lenalidomide and lenalidomide-d5 (internal standard) were extracted from 50 μL of human plasma using liquid–liquid extraction. Chromatography was conducted with a Halo^® C18 column using 0.1% formic acid and methanol (20:80, v/v) as the mobile phase. The mass spectrometer was operated in a positive ion mode with an electrospray ionization interface and multiple reaction monitoring modes. Results: Calibration curves were linear over the range of 5 to 1000 ng/mL (r² > 0.996) for both the total and unbound lenalidomide. For total lenalidomide concentrations, between-run precision (coefficients of variation) and accuracy were 1.70–7.65% and 94.45–101.10%, respectively. For unbound concentrations, inter-day precision and accuracy were 1.98–10.55% and 93.95–98.48%, respectively. Conclusions: We developed a highly reproducible, sensitive, and efficient bioanalytical method using a smaller volume of plasma sample (50 μL) with a relatively short run time (2.5 min). The proposed analytical method was successfully applied to measure total and unbound lenalidomide concentrations at various time points in multiple myeloma patients with renal impairment.

Background/Objectives: Earlier detection of severe immune-related hematological adverse events (irHAEs) in cancer patients treated with a PD-1 or PD-L1 inhibitor is critical to improving treatment outcomes. The study aimed to develop a simple machine learning (ML) model for predicting irHAEs associated with PD-1/PD-L1 inhibitors. Methods: We utilized the Observational Medical Outcomes Partnership–Common Data Model based on electronic medical records from a tertiary (KHMC) and a secondary (KHNMC) hospital in South Korea. Severe irHAEs were defined as Grades 3–5 by the Common Terminology Criteria for Adverse Events (version 5.0). The predictive model was developed using the KHMC dataset, and then cross-validated against an independent cohort (KHNMC). The full ML models were then simplified by selecting critical features based on the feature importance values (FIVs). Results: Overall, 397 and 255 patients were included in the primary (KHMC) and cross-validation (KHNMC) cohort, respectively. Among the tested ML algorithms, random forest achieved the highest accuracy (area under the receiver operating characteristic curve [AUROC] 0.88 for both cohorts). Parsimonious models reduced to 50% FIVs of the full models showed comparable performance to the full models (AUROC 0.83–0.86, p > 0.05). The KHMC and KHNMC parsimonious models shared common predictive features including furosemide, oxygen gas, piperacillin/tazobactam, and acetylcysteine. Conclusions: Considering the simplicity and adequate predictive performance, our simplified ML models might be easily implemented in clinical practice with broad applicability. Our model might enhance early diagnostic screening of irHAEs induced by PD-1/PD-L1 inhibitors, contributing to minimizing the risk of severe irHAEs and improving the effectiveness of cancer immunotherapy.

Background/Objectives: This study aimed to investigate trends in antidiabetic drug use and assess the risk of metformin-associated lactic acidosis (MALA) in patients with chronic kidney disease (CKD). Methods: A retrospective observational analysis based on the common data model was conducted using electronic medical records from 2010 to 2021. The patients included were aged ≥18, diagnosed with CKD and type 2 diabetes, and had received antidiabetic medications for ≥30 days. MALA was defined as pH ≤ 7.35 and arterial lactate ≥4 mmol/L. Results: A total of 8318 patients were included, with 6185 in CKD stages 1–2 and 2133 in stages 3a–5. Metformin monotherapy was the most prescribed regimen, except in stage 5 CKD. As CKD progressed, metformin use significantly declined; insulin and meglitinides were most frequently prescribed in end-stage renal disease. Over the study period, the use of SGLT2 inhibitors (13.3%) and DPP-4 inhibitors (24.5%) increased significantly, while sulfonylurea use decreased (p < 0.05). Metformin use remained stable in earlier CKD stages but significantly decreased in stage 3b or worse. The incidence rate (IR) of MALA was 1.22 per 1000 patient-years, with a significantly increased IR in stage 4 or worse CKD (p < 0.001). Conclusions: Metformin was the most prescribed antidiabetic drug in CKD patients in Korea with a low risk of MALA. Antidiabetic drug use patterns varied across CKD stages, with a notable decline in metformin use in advanced CKD and a rise in SGLT2 inhibitor prescriptions, underscoring the need for further optimized therapy.

Background/Objectives: Selinexor is a selective nuclear-export inhibitor approved for hematologic malignancies, characterized by extensive plasma protein binding (>95%). However, a validated analytical method to accurately measure the clinically relevant unbound fraction of selinexor in human plasma has not yet been established. This study aimed to develop a fully validated bioanalytical assay for simultaneous quantification of total and unbound selinexor concentrations in human plasma. Methods: We established and fully validated an analytical method based on liquid chromatography–tandem mass spectrometry (LC-MS/MS) capable of quantifying total and unbound selinexor concentrations in human plasma. Unbound selinexor was separated using ultrafiltration, and selinexor was efficiently extracted from 50 μL of plasma by liquid–liquid extraction. Chromatographic separation was achieved on a C18 column using an isocratic mobile phase (0.1% formic acid:methanol, 12:88 v/v) with a relatively short runtime of 2.5 min. Results: Calibration curves showed excellent linearity over a range of 5–2000 ng/mL for total selinexor (r² ≥ 0.998) and 0.05–20 ng/mL for unbound selinexor (r² ≥ 0.995). The precision (%CV ≤ 10.35%) and accuracy (92.5–104.3%) for both analytes met the regulatory criteria. This method successfully quantified selinexor in plasma samples from renally impaired patients with multiple myeloma, demonstrating potential inter-individual differences in unbound drug concentrations. Conclusions: This validated bioanalytical assay enables precise clinical pharmacokinetic assessments in a short runtime using a small plasma volume and, thus, assists in individualized dosing of selinexor, particularly for renally impaired patients with altered protein binding.