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: 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: 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.

Background/Objectives: Special populations, including pediatric, geriatric, and organ-impaired patients, are consistently underrepresented in randomized controlled trials (RCTs), resulting in limited evidence for safe and effective dosing. Off-label use is common, and variability in drug exposure and response increases the risk of adverse drug reactions (ADRs). This review aims to examine how integrating pharmacometrics (PMX) with real-world data (RWD) can address evidence gaps by supporting dose optimization, population expansion, and safety evaluation in these vulnerable groups. Methods: A narrative literature review was conducted using PubMed, Embase, and Web of Science (January 2000–November 2025). Using Boolean combinations of PMX and RWD-related search terms, approximately 200–300 records were identified across the three databases; approximately 30 full-text articles were reviewed, and representative case studies were selected based on population diversity, methodological variation, and regulatory or clinical impact. Results: RWD–PMX integration has been applied across three domains: (i) dosing optimization through therapeutic drug monitoring (TDM)-informed PopPK modeling and model external validation in pediatric and neonatal populations; (ii) population expansion supporting dose extrapolation and regulatory decision-making for unapproved groups; and (iii) safety evaluation enabling identification of exposure–toxicity risk factors in vulnerable cohorts. Conclusions: Integrating PMX with RWD provides a practical and mechanistically grounded framework for evaluating dosing, treatment eligibility, and safety in populations insufficiently represented in clinical trials. Accumulating evidence indicates that RWD–PMX methodologies can complement traditional clinical research and inform regulatory decision-making. Continued refinement of data quality standards, validation practices, and guidance frameworks will be essential for broader adoption.