Translational and Clinical Pharmacology最新文献

Tegoprazan is a potassium-competitive acid blocker that directly inhibits the gastric proton pump, whereas famotidine is a histamine-2 receptor antagonist that indirectly suppresses acid secretion with known tolerance to repeated dosing. This phase 1 study evaluated the pharmacokinetics, pharmacodynamics, and safety of tegoprazan 12.5 mg twice daily (BID) compared with tegoprazan 25 mg once daily (QD) and famotidine 20 mg BID in healthy subjects. Thirty-six participants were randomized to one of the 3 regimens for 14 days. In pharmacokinetic analysis, tegoprazan 12.5 mg BID showed lower steady-state Cmax but higher trough concentrations than 25 mg QD, while overall systemic exposure was comparable, with a Day 14 geometric mean ratio for area under the concentration-time curve over 24 hours of 1.08 (90% confidence interval, 0.85-1.36). Pharmacodynamic assessment using 24-hour intragastric pH monitoring demonstrated superior and sustained acid suppression with tegoprazan 12.5 mg BID. On Day 14, the percentage of time with intragastric pH > 3 over 24 hours was 75.1% with tegoprazan 12.5 mg BID, compared with 55.4% with 25 mg QD and 40.3% with famotidine. Both tegoprazan regimens maintained relatively consistent acid suppression from Day 1 to Day 14 (71.8% to 75.1% and 56.4% to 55.4%), whereas famotidine showed a decline (65.3% to 40.3%). Similar time-dependent patterns were observed during the nighttime period and in the supine position. Tegoprazan 12.5 mg BID was well tolerated, with no adverse events reported in this group. These findings suggest that low-dose tegoprazan 12.5 mg BID provides safe and sustained acid suppression.

Orlistat, phentermine/topiramate, and liraglutide are widely used obesity pharmacotherapies, but comparative data from Asian real-world settings remain limited. We compared their effects on body weight, glycemic control, and persistence. We performed a retrospective cohort study using electronic health records from a tertiary hospital in Korea. Adults prescribed orlistat, phentermine/topiramate, or liraglutide between 2018 and 2025 were included. The primary endpoint was percentage weight change at 6 months. Secondary endpoints were the proportions achieving ≥5% and ≥10% weight loss, weight change at 3, 6, and 12 months, change in HbA1c, and 12-month treatment persistence. We applied multivariable linear and logistic regression, linear mixed-effects models, propensity score-weighted models, and Kaplan-Meier and restricted mean survival time (RMST) methods. Among 1,910 treatment episodes, 505 involved orlistat, 777 phentermine/topiramate, and 628 liraglutide. Phentermine/topiramate showed greater mean percentage weight loss at 6 months than orlistat (adjusted difference, -1.59 percentage points; p < 0.001), whereas liraglutide produced similar weight loss to orlistat. The odds of achieving ≥5% weight loss were higher with phentermine/topiramate than with orlistat (adjusted odds ratio, 2.21), while liraglutide showed a smaller effect. Liraglutide tended to reduce HbA1c more than orlistat, but this difference was attenuated after propensity score weighting. Twelve-month treatment persistence and RMST were lower with liraglutide than with the oral agents. In this real-world cohort, phentermine/topiramate achieved the most pronounced and sustained weight loss. Liraglutide and orlistat produced similar overall weight reduction, but liraglutide showed lower long-term persistence.

Antibody-drug conjugates (ADCs) represent a promising class of anticancer therapies with rapidly expanding development. However, regulatory approval remains complex due to their unique pharmacological properties. We systematically analyzed U.S. Food and Drug Administration (FDA)'s clinical pharmacology considerations across all approved ADCs to identify consistent regulatory patterns and inform future development strategies. We comprehensively reviewed 13 FDA-approved ADCs as of March 2025, examining clinical pharmacology and/or multidisciplinary reviews from Drugs@FDA. Six key parameters from the FDA's Clinical Pharmacology Considerations for Antibody-Drug Conjugates: Guidance for Industry (2024) were evaluated: bioanalytical methods, exposure-response relationships, intrinsic factors, extrinsic factors, QTc prolongation, and immunogenicity. For each parameter, we assessed: 1) submission completeness, 2) FDA's assessment, and 3) whether they led to regulatory actions such as post-marketing requirements/commitments (PMRs/PMCs). All 6 clinical pharmacology considerations were consistently applied throughout ADC approval history, well before the issuance of formal guidance. The FDA maintained particularly rigorous standards across a few critical domains. Exposure-response interpretation considering critical safety issues, intrinsic factor analyses-particularly hepatic impairment and racial variations-and validated immunogenicity assays including the neutralizing antibody emerged as primary scrutiny points. These factors repeatedly triggered similar PMRs/PMCs across multiple ADCs, underscoring their regulatory significance. Core clinical pharmacology parameters consistently shape FDA assessments of ADCs. Proactive alignment with regulatory expectations can streamline development, mitigate potential delays, and reduce post-marketing obligations. Our historical analysis provides strategic insights that may support clinical investigators for optimizing future ADC development.

Alzheimer's disease (AD) is the most common cause of dementia. AD exhibits notable sex-related disparities in prevalence, progression, and treatment response. With the recent approval of anti-amyloid-beta monoclonal antibodies-aducanumab, lecanemab, and donanemab-understanding sex differences in their clinical effects has become increasingly relevant. This review article investigates sex differences in the pharmacokinetics, efficacy, and safety of aducanumab, lecanemab, and donanemab and discusses the possible mechanism underlying the observed differences. Although sex-specific analyses were largely underreported in clinical trials, population pharmacokinetic models identified sex as a covariate affecting clearance and volume of distribution for aducanumab and lecanemab and higher exposure to lecanemab was predicted for females. Subgroup analyses of phase 3 trials revealed that males tended to experience greater benefit from aducanumab and lecanemab, whereas females showed better response to donanemab. The overall incidence of adverse events, including amyloid-related imaging abnormalities, did not show significant differences between sexes. Potential mechanisms underlying these differences include sex-related variations in blood-brain barrier permeability, apolipoprotein E4-associated neuroinflammatory responses, and baseline disease characteristics. These findings underscore the need for future AD clinical trials to incorporate sex-based analyses and to consider sex as a key factor in optimizing treatment strategies.

Serum insulin-like growth factor 1 (IGF-1) and insulin-like growth factor binding protein 3 (IGFBP-3) levels are regulated by growth hormone, nutritional status, and other endocrine factors, and are implicated in aging, metabolic disorders, and malignancies. Establishing appropriate reference ranges for these biomarkers is essential for safety monitoring and pharmacodynamic assessments in clinical trials. Although various detection methods-including enzyme-linked immunosorbent assay, chemiluminescence immunoassay, radioimmunoassay, and immunoradiometric assay (IRMA)-are available, defining reliable reference intervals remains challenging due to variability linked to age, sex, season, and assay methodology. IRMA manufacturers typically provide generalized reference values and recommend that laboratories establish context-specific ranges; however, data from healthy adult populations are limited. We analyzed data from 255 self-reported healthy Korean men aged 19-40 years using the IRMA method and R software. Age-specific reference ranges and standardized centile curves for serum IGF-1 and IGFBP-3 were established. Both biomarkers demonstrated significant seasonal variation, with notable differences observed among spring, autumn, and winter groups. This study presents a method for establishing institution-specific reference ranges and highlights the importance of considering seasonal variations to help reduce unnecessary dropouts during clinical trials.

Everolimus, an inhibitor of the mammalian target of the rapamycin signaling pathway, is used to treat tuberous sclerosis complex (TSC), certain cancers, and organ transplantation. Although a dispersible formulation has been approved for pediatric patients with TSC-associated seizures, no dispersible everolimus formulation has been approved for adults with swallowing difficulties. This study aimed to evaluate the pharmacokinetics (PK) of a newly developed dispersible everolimus tablet (SVG101) in healthy adult males and determine an optimal dosing regimen to achieve therapeutic trough concentrations. This randomized, open-label, single-dose, 2 × 2 crossover study included 26 healthy Korean males. The participants received either the reference tablet or SVG101, with a minimum 10-day washout period between treatments. Blood samples were collected up to 144 hours post-dosing. Population PK modeling was performed using a two-compartment model with dual absorption kinetics (zero-order kinetics, followed by first-order kinetics with lag time). The model evaluation demonstrated good agreement between the observed and predicted concentrations, with no significant covariates identified. Simulation of steady-state trough concentrations indicated that daily doses of 3-4 mg of dispersible everolimus resulted in trough levels within the therapeutic range (5-15 ng/mL), whereas the 5 mg dose exceeded this range. These findings suggest that a daily dose of 3-4 mg is appropriate to maintain target trough concentrations in adults. This study supports the development of a dispersible everolimus formulation for adults with swallowing difficulties that can improve adherence and clinical outcomes. Further studies involving diverse patient populations are required to confirm these findings.

European Public Assessment Reports (EPARs) are among the most scientifically rigorous sources available for evaluating newly approved oncology therapeutics, yet their complexity often limits clinical use. This tutorial aims to equip practicing oncologists and clinical pharmacologists with practical strategies for extracting actionable insights from EPARs. Using the regulatory assessment of tislelizumab in non-small cell lung cancer as an exemplar, this tutorial provides a structured framework for navigating EPAR content and translating technical findings into clinical practice. The analysis highlights how the EMA evaluates molecular design, pharmacokinetic modeling, exposure-response relationships, and safety data to guide therapeutic decisions. The tutorial illustrates how population pharmacokinetics informed the adoption of flat dosing, how exposure-efficacy analyses were interpreted in light of confounding by disease severity, and how differential indication approvals were justified by benefit-risk analyses. Mastering EPAR review enables clinicians to refine patient selection, anticipate pharmacologic variability, and better understand the scientific rationale behind a therapeutic label. This framework supports more informed, evidence-based prescribing decisions across oncology practice.

This study aimed to identify the clinical metabolic markers associated with gynecologic cancer (GC) by comparing machine learning (ML) algorithms with orthogonal partial least squares-discriminant analysis (OPLS-DA). Untargeted metabolomic analysis was performed on plasma from 42 patients with GC (24 cervical cancer [CC], 9 endometrial cancer [EC], and 9 ovarian cancer [OC]) and 57 healthy female participants. GC and healthy control groups were classified using OPLS-DA and 8 ML algorithms. The ML algorithm with the best classification performance was used to assess CC, EC, and OC with healthy subjects, and metabolite candidates involved in each GC were selected. Upon comparing the classification model performance between the GC and control groups, random forest (RF) model displayed the best performance with an area under the curve (AUC) of 0.9999. The multi-classification RF model was established to distinguish all 4 groups and was achieved an AUC of 0.8351. The AUCs of the 3 GC subgroup assessment RF models comparing patients with CC, EC, and OC with healthy subjects were 0.9838, 0.7500, and 0.7321, respectively. Plasma concentrations of 2 identified metabolites significantly increased in patients with GCs. Several ML algorithms were used to distinguish GC, showed better performance than conventional OPLS-DA. Proline betaine and lysophosphatidyl ethanolamine (18:0/0:0) selected in RF models were suggested as metabolite candidates associated with GCs.

Vaccines are vital to global public health, especially as population growth and climate change increase the risk of infectious disease outbreaks. While randomized controlled trials (RCTs) remain the gold standard for evaluating vaccine safety and efficacy, traditional RCTs often face limitations including time constraints, resource demands, and ethical concerns. Adaptive endpoint-driven trial designs offer a promising alternative by allowing protocol modifications based on interim analyses while preserving scientific validity. Despite their potential, the adoption of such designs in prophylactic vaccine trials remains limited, with few real-world examples. This scoping review investigates the implementation of adaptive endpoint-driven methodologies in prophylactic vaccine RCTs. Using PRISMA-P and Joanna Briggs Institute guidelines, a systematic search was conducted across MEDLINE, Embase, and ClinicalTrials.gov. Eligible studies were Phase I-III vaccine RCTs employing adaptive endpoint-driven strategies. Multiple reviewers independently extracted data and assessed risk of bias, with findings synthesized descriptively. Seven studies met inclusion criteria, demonstrating adaptive methods such as interim analyses, sample size re-estimation, and modification of trial arms. Endpoints were identified through clinical evaluations, laboratory confirmation, and epidemiological surveillance, often supported by digital tools like mobile-based reporting and real-time diagnostics. These approaches enhanced flexibility and operational efficiency but faced challenges including statistical complexity, regulatory constraints, and maintaining control of bias. The findings highlight the emerging utility of adaptive endpoint-driven designs in vaccine research. Future efforts should focus on improving endpoint tools, addressing regulatory concerns, and evaluating how adaptive designs impact approval timelines and public health outcomes.