Translational and Clinical Pharmacology最新文献

{"title":"Comments on \"Implementation of Miettinen-Nurminen score method with or without stratification in R\" by Lee MH and Bae KS (2022).","authors":"Peter J Laud","doi":"10.12793/tcp.2025.33.e12","DOIUrl":"10.12793/tcp.2025.33.e12","url":null,"abstract":"","PeriodicalId":23288,"journal":{"name":"Translational and Clinical Pharmacology","volume":"33 3","pages":"129-134"},"PeriodicalIF":1.5,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12518215/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145303757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decoding the EPAR scientific assessment for clinical practice: a tutorial using tislelizumab in NSCLC.","authors":"Tae Min Kim","doi":"10.12793/tcp.2025.33.e17","DOIUrl":"https://doi.org/10.12793/tcp.2025.33.e17","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":23288,"journal":{"name":"Translational and Clinical Pharmacology","volume":"33 3","pages":"111-128"},"PeriodicalIF":1.5,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12518216/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145303718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of biomarkers in gynecologic cancers: a machine learning approach for metabolomics.","authors":"SangHun Cha, Sang-Min Lee, Kyeong Eun Lee, Min-Gul Kim, Sun-Young Lee, Kwang-Hee Shin","doi":"10.12793/tcp.2025.33.e15","DOIUrl":"10.12793/tcp.2025.33.e15","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":23288,"journal":{"name":"Translational and Clinical Pharmacology","volume":"33 3","pages":"168-182"},"PeriodicalIF":1.5,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12518212/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145303753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Insight and benefits from vaccine clinical trials with adaptive designs: a scoping review.","authors":"Andrés Mauricio García-Sierra, Luz Adriana Urbina Moreno, Chloe Flanigan, Jose Jimeno, Diana C Rivera-Pinzón","doi":"10.12793/tcp.2025.33.e11","DOIUrl":"10.12793/tcp.2025.33.e11","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":23288,"journal":{"name":"Translational and Clinical Pharmacology","volume":"33 3","pages":"135-144"},"PeriodicalIF":1.5,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12518217/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145303737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of the characteristics of healthy volunteers participating in Phase 1 clinical trials in Korea and Japan.","authors":"Ji-Hye Seo, Ock-Joo Kim, Eun Kyung Choi, Yuji Kumagai, Mika Maeda, Ji-Eun Park, Ildae Song","doi":"10.12793/tcp.2025.33.e16","DOIUrl":"10.12793/tcp.2025.33.e16","url":null,"abstract":"<p><p>This paper aims to elucidate the characteristics of healthy volunteers in Korea and Japan. Clinical trials can be divided into Phases 1 through 4 based on the stage of research. Unlike other phase, Phase 1 trials administer new drugs to healthy volunteers. Although Phase 1 trials are the first stage of drug administration to humans, without therapeutic purposes. Healthy volunteers must have no history of diseases that could affect the study and must have normal physical measurements. They are also defined as individuals who understand the study and can voluntarily consent to participate. Also, it is crucial to conduct them ethically. To date, there has been insufficient research on the Phase 1 clinical trials in Korea and Japan, and in-depth analysis of the perceptions and the motivations. This study enrolled healthy volunteers in Korea and Japan to conduct surveys related to demographic information and their perception.</p>","PeriodicalId":23288,"journal":{"name":"Translational and Clinical Pharmacology","volume":"33 3","pages":"183-196"},"PeriodicalIF":1.5,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12518214/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145303698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of drug-drug interaction potentials between JP-1366 and celecoxib using physiologically based pharmacokinetic modeling.","authors":"Seung Chan Choi, John Kim, Hyeong-Seok Lim","doi":"10.12793/tcp.2025.33.e10","DOIUrl":"10.12793/tcp.2025.33.e10","url":null,"abstract":"<p><p>Zastaprazan (JP-1366) is a new potassium-competitive acid blocker being developed for treating gastrointestinal reflux disease. It is an orally administered small molecule that inhibits gastric H+ and K+-ATPases differently from proton pump inhibitors, which act quickly and have dose-dependent effects on acid secretion. Celecoxib, a selective cyclooxygenase 2 inhibitor, will likely be used with zastaprazan in clinical settings and trials. The objective of current physiologically based pharmacokinetic (PBPK) modeling study is to predict drug-drug interaction (DDI) risk between zastaprazan (perpetrator) and celecoxib (victim). A human PBPK model for zastaprazan was built using experimental physicochemical properties and <i>in silico</i> predictions. The model was optimized with clinical pharmacokinetic (PK) data from a phase 1 study (Protocol No. JP-1366-105). The PBPK model for celecoxib was constructed using the data from previous studies and <i>in silico</i> predictions. The final PBPK model encompassing zastaprazan and celecoxib was used to quantitatively predicted DDI risks in humans. The final PBPK models accurately predicted zastaprazan's PK profiles after single dose in human, and it also well predicted plasma celecoxib concentrations over time. At doses of 20 mg of zastaprazan citrate (JAQBO^® tablet) and 200 mg of celecoxib, multiple oral doses of zastaprazan every 24 hours for 7 days did not increase celecoxib's area under the curve (AUC) and maximum plasma concentration (C_max), with ratios of 1 in both AUC and C_max, indicating no effect of zastaprazan on celecoxib's PK. The PBPK modeling approach provides scientific predictions of DDIs between zastaprazan and celecoxib, guiding future clinical development.</p>","PeriodicalId":23288,"journal":{"name":"Translational and Clinical Pharmacology","volume":"33 2","pages":"90-99"},"PeriodicalIF":1.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12242391/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144627166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Web-based automated therapeutic drug monitoring application for precision medicine in tuberculosis management.","authors":"Young-Kyung Choi, Rannissa Puspita Jayanti, Nguyen Thuy Ha Uyen, Yong-Soon Cho, Jae-Gook Shin","doi":"10.12793/tcp.2025.33.e9","DOIUrl":"10.12793/tcp.2025.33.e9","url":null,"abstract":"<p><p>Tuberculosis (TB) remains one of the leading causes of infectious disease-related deaths worldwide. Model-informed precision dosing-based therapeutic drug monitoring (TDM) is a promising strategy to optimize anti-TB drugs doses based on pharmacokinetic (PK) profiles of patients. However, this approach requires significant time and trained personnel to interpret the results. To address this limitation, we developed and utilized an automated, web-based TDM platform that simplifies implementation and enhances accessibility, ultimately aiming to improve treatment outcomes. The system incorporates population PK models for both first- and second-line anti-TB drugs, integrating clinical data including demographics, <i>NAT2</i> genotype and drug concentrations from limited sampling strategies. Bayesian forecasting is used to estimate individual PK parameters and simulate optimized dosing regimens. Clinicians can use the platform to automatically generate the individual concentration-time curve plot that compares a patient's exposure with population level references, along with a table displaying the estimated individual PK parameters. If the dose adjustment is needed, users may input alternative regimens and run the simulation to predict the corresponding PK metrics. These features enable users to visualize predicted outcomes, compare exposures against therapeutic targets, and support optimal dose selection. The system produces downloadable reports containing patient specific data, PK parameter values, graphical PK profiles, and pharmacogenomic interpretations with minimal user input. This automated web-based platform enhances the time-efficiency and accessibility of TDM, making it a practical tool for personalized TB therapy. It is especially valuable in resource-limited settings where expert support is limited, by supporting clinical decision making and improving patient outcomes.</p>","PeriodicalId":23288,"journal":{"name":"Translational and Clinical Pharmacology","volume":"33 2","pages":"51-65"},"PeriodicalIF":1.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12242390/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144627169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pharmacokinetic comparison between the fixed-dose combination of rosuvastatin/ezetimibe 2.5/10 mg and the co-administration of individual formulations in healthy subjects.","authors":"Dowon Lee, Sejung Hwang, Taewon Lee, Kyung-Sang Yu, SeungHwan Lee","doi":"10.12793/tcp.2025.33.e6","DOIUrl":"10.12793/tcp.2025.33.e6","url":null,"abstract":"<p><p>The combination therapy of low-dose rosuvastatin and ezetimibe has shown similar efficacy in decreasing low-density lipoprotein cholesterol (LDL-C) compared to high-dose rosuvastatin monotherapy. This study aimed to compare the pharmacokinetics (PKs) between the fixed-dose combination (FDC) of rosuvastatin/ezetimibe 2.5/10 mg and the co-administration of individual formulations. A randomized, open-label, single-dose, 2-sequence, 2-period, crossover study was conducted in healthy volunteers. Subjects were randomized in each sequence and received a single dose of the FDC of rosuvastatin/ezetimibe 2.5/10 mg or the co-administration of individual formulations in each period with a 14-day washout. Serial blood samples for PK analysis were collected up to 48 hours post-dose for rosuvastatin and 72 hours post-dose for ezetimibe. The geometric mean ratios (GMRs) and 90% confidence intervals (CIs) of the FDC to the co-administration for maximum plasma concentration (C_max) and area under the curve from zero to the last measurable time point (AUC_last) were calculated. Forty-seven subjects were randomized, and 41 subjects completed the study. The GMRs (90% CIs) of C_max and AUC_last for rosuvastatin were 0.9789 (0.9020-1.0622) and 0.9741 (0.9105-1.0422). The corresponding values were 1.0419 (0.9219-1.1776) and 0.9983 (0.9522-1.0465) for total ezetimibe, and 1.0396 (0.9087-1.1893) and 0.9743 (0.8997-1.0550) for free ezetimibe, respectively. All the values were within the conventional bioequivalence criteria of 0.8 to 1.25. In conclusion, the FDC of rosuvastatin/ezetimibe 2.5/10 mg showed comparable PK with the co-administration of individual formulations.</p><p><strong>Trial registration: </strong>Clinical Research Information Service Identifier: KCT0009254.</p>","PeriodicalId":23288,"journal":{"name":"Translational and Clinical Pharmacology","volume":"33 2","pages":"100-110"},"PeriodicalIF":1.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12242394/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144627167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association between the difference in creatinine-based and creatinine-cystatin C-based estimated glomerular filtration rates and vancomycin systemic exposure after ventricular assist device surgery.","authors":"Jaegu Kang, Jisoo Song, SeungHwan Lee, Kyung-Sang Yu, Ki Young Huh","doi":"10.12793/tcp.2025.33.e8","DOIUrl":"10.12793/tcp.2025.33.e8","url":null,"abstract":"<p><p>Vancomycin is widely used as a prophylactic antibiotic for ventricular assist device (VAD) implantation to prevent infections, the most common complication. As vancomycin is renally eliminated, an accurate renal function estimation is essential. However, it has been reported that creatinine-based models inaccurately estimate renal function in VAD recipients, and cystatin C could alleviate the limitation. This study analyzed the association between renal function estimation methods and vancomycin trough concentrations in VAD recipients. Clinical data of VAD recipients who received prophylactic vancomycin at Seoul National University Hospital between 2014 and 2023 were retrospectively analyzed. Recipients were categorized into high trough (> 15 mg/dL) or non-high trough (≤ 15 mg/dL) groups based on the 1st vancomycin trough concentration after surgery. The estimated glomerular filtration rates (eGFRs) based on creatinine alone (eGFR_Cr), combined with cystatin C (eGFR_Cr-Cys-C), and their differences (eGFR_diff) were compared between the 2 groups. The association between the eGFRs and vancomycin trough concentrations was evaluated using Pearson's method. Among the 20 recipients, 13 were the high trough group and 7 were the non-high trough group. The high trough group had a significantly higher eGFR_diff than non-high trough group (8.9 vs. -5.1 mL/min/1.73 m², <i>p</i> = 0.0265), while other eGFRs were comparable. Among the three eGFR estimates, eGFR_diff showed the strongest correlation (<i>r</i> = 0.41) with the first measured vancomycin trough levels. In conclusion, creatinine-based eGFR might not fully capture vancomycin pharmacokinetics in VAD recipients. The difference between eGFR_Cr and eGFR_Cr-Cys-C is associated with vancomycin trough concentration in VAD recipients.</p>","PeriodicalId":23288,"journal":{"name":"Translational and Clinical Pharmacology","volume":"33 2","pages":"80-89"},"PeriodicalIF":1.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12242392/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144627165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitation of methotrexate polyglutamates in red blood cells and application in patients with Crohn's disease.","authors":"Hakmin Kim, Kyeong-Seog Kim, Sihyun Kim, Jihyun Kang, Hyun Chul Kim, Sejung Hwang, Jae-Yong Chung, Hyuk Yoon, Joo-Youn Cho","doi":"10.12793/tcp.2025.33.e7","DOIUrl":"10.12793/tcp.2025.33.e7","url":null,"abstract":"<p><p>Methotrexate (MTX), a folate antagonist, is commonly administered at low doses for the treatment of Crohn's disease (CD). Anti-inflammatory effects of MTX are facilitated by its intracellular conversion to MTX polyglutamates (MTX-PGs). Because plasma-based monitoring of therapeutic response does not accurately reflect the therapeutic efficacy of MTX, quantifying intracellular MTX-PGs, potential biomarkers of the MTX response, is crucial. However, it is challenging to routinely monitor intracellular MTX metabolites in patients with CD due to the low concentrations of MTX-PGs. Therefore, quantitating MTX-PGs in clinical samples with a high-sensitivity method is necessary. We established a high-sensitivity method to quantify three MTX-PGs using perchloric acid deproteinization followed by high-performance liquid chromatography-tandem mass spectrometry. Calibration curves were generated using human red blood cells as biological matrix. This method was applied to analyze MTX-PGs in red blood cells (RBCs) from patients with CD undergoing MTX therapy. The method achieved a lower limit of quantification of 1 ng/mL for individual MTX-PGs. A nine-point calibration curve covering 1-400 ng/mL showed excellent linearity. Precision (relative standard deviation < 15%) and accuracy (93.41-109.37%) were satisfactory in both intra- and inter-day assays. Plasma MTX levels were not significantly correlated with any individual RBC MTX-PG level (<i>p</i> = 0.998, 0.640, and 0.587, respectively). The lack of correlation supports our conclusion that plasma MTX levels may not reliably represent intracellular accumulation. The developed quantitative method provides a useful tool to improve our understanding of MTX metabolism and may facilitate therapeutic drug monitoring in MTX therapy.</p>","PeriodicalId":23288,"journal":{"name":"Translational and Clinical Pharmacology","volume":"33 2","pages":"66-79"},"PeriodicalIF":1.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12242393/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144627168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}