Hyeonji Kim

Purpose: To develop a population pharmacokinetic (PK) model of lenalidomide and propose model-informed precision dosing based on total and unbound concentrations in multiple myeloma (MM) patients with moderate-to-severe renal impairment (RI). Patients and Methods: Thirty MM patients orally receiving lenalidomide were prospectively enrolled with the following stratification based on renal function: 11 with an estimated glomerular filtration rate [eGFR] < 30 mL/min/1.73 m², 11 with eGFR ≥ 30 to < 60 mL/min/1.73 m², and 8 with eGFR ≥ 60 mL/min/1.73 m². Serial plasma samples were collected at steady state. Total and unbound concentrations were analyzed simultaneously by population PK modeling using NONMEM. Monte Carlo simulations were performed to identify dosing regimens targeting the reference systemic exposure (area under the plasma concentration-time curve, AUC) for patients with normal renal function receiving 25 mg/day. Results: A two-compartment model with linear protein binding best described both total and unbound plasma lenalidomide concentration-time data. Apparent unbound clearance (CL/F) increased with higher eGFR and body surface area (BSA), while apparent central volume of distribution (V₁/F) increased with higher BSA (P < 0.05). No other PK parameters were significantly associated with patient characteristics including ABCB1 genotypes. Simulation results indicated that although labeled dosing was generally adequate, BSA-stratified adjustments offered improved precision. Specifically, dose reduction is recommended for patients with BSA < 1.6 m² to maintain therapeutic exposure while minimizing toxicity; conversely, dose escalation is warranted for patients with BSA ≥ 2.0 m² to prevent underexposure. Conclusion: Our model identifies eGFR and BSA as critical covariates for precision dosing of lenalidomide to optimize therapeutic exposure in MM patients with RI. The infographic illustrates the population pharmacokinetics and model-informed precision dosing of lenalidomide for patients with multiple myeloma and renal impairment. It begins with a prospectively conducted clinical study in patients receiving lenalidomide, followed by simultaneous measurement of total and unbound plasma concentrations and genetic analysis. These data were used in population pharmacokinetic modeling and simulation. The lower section shows that apparent unbound clearance (CL/F) was significantly influenced by eGFR and body surface area (BSA), while apparent central volume of distribution (V₁/F) was associated with BSA, and the unbound fraction (Fu) was estimated at 61.4%. Based on the final model, virtual patient populations were generated across renal function and BSA strata, and Monte Carlo simulations were performed to evaluate drug exposure. The simulation results guide dosing adjustments based on BSA, indicating dose reduction for BSA less than 1.6 m² and dose escalation for BSA greater than or equal to 2.0 m².Lenalidomide dosing for renally impaired multiple myeloma patients using population pharmacokinetics.

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: 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: 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.