Cts-Clinical and Translational Science最新文献

{"title":"Evaluation of the Effect of Loperamide on the Cardiac Repolarization Interval Using Exposure–Response Analysis","authors":"Belén Valenzuela,&nbsp;Per Olsson Gisleskog,&nbsp;Iolanda Cirillo,&nbsp;Erwin Coenen,&nbsp;Jay Ariyawansa,&nbsp;Saberi Rana Ali,&nbsp;Samiha Takhtoukh,&nbsp;Juan José Pérez-Ruixo,&nbsp;Oliver Ackaert","doi":"10.1111/cts.70114","DOIUrl":"10.1111/cts.70114","url":null,"abstract":"<p>This analysis assessed the relationship between the plasma concentrations of loperamide and its <i>N</i>-desmethyl loperamide meta- bolite (M1) and the potential QT interval prolongation at therapeutic and supratherapeutic doses. The exposure–response analysis was performed using the data from healthy adults participating in a randomized, double-blind, single-dose, four-way (placebo; loperamide 8 mg [therapeutic]; loperamide 48 mg [supratherapeutic]; moxifloxacin 400 mg [positive control]) crossover study. The electrocardiographic measurements extracted from 12-lead digital Holter recordings were time-matched to pharmacokinetic sampling of loperamide/M1. The primary response variable was placebo-adjusted change from baseline in Fridericia-corrected QT interval (ΔΔQTcF); the exposure variable was loperamide and/or M1 concentration. A total of 53 participants with 1408 time-matched pharmacokinetic and ΔΔQTcF measurements was analyzed. Hysteresis between both loperamide and M1 concentrations and ΔΔQTcF was observed with supratherapeutic dose. The pre-specified linear concentration-ΔΔQTcF relationship was driven by M1 concentrations in the effect compartment. The model-predicted mean ΔΔQTcF at the geometric mean of the maximum concentration in the effect compartment was −0.526 msec (90% CI, −1.51 to 0.462) following 8-mg dose (2.1 ng/mL) and 6.06 msec (90% CI, 3.86–8.27) following 48-mg dose (14.2 ng/mL). The upper bound of two-sided 90% CI was &lt; 10 msec for both doses. The sensitivity analysis considering loperamide concentrations in the effect compartment instead of M1 as input for the concentration-ΔΔQTcF analysis confirmed these findings. The data showed that loperamide or M1 does not have an effect on cardiac repolarization that exceeds the threshold of regulatory concern in healthy participants at doses of 8 and 48 mg.</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"18 2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11770325/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143048557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Knowledge, Attitude, Practice, and Barriers of Adverse Drug Reaction Reporting Among Healthcare Professionals in Timor-Leste: A Cross-Sectional Survey","authors":"Juanina da Costa,&nbsp;Wichit Nosoongnoen,&nbsp;Watcharee Rungapiromnan,&nbsp;Pramote Tragulpiankit","doi":"10.1111/cts.70134","DOIUrl":"10.1111/cts.70134","url":null,"abstract":"<p>The Timor-Leste Pharmacovigilance (PV) became an associate member of the WHO Programme for International Drug Monitoring in 2019; however, the adverse drug reaction (ADR) reporting rate remains low, with only nine reports per 1342 million inhabitants over 5 years. This study aimed to evaluate the knowledge, attitude, practice, and barriers related to ADRs, pharmacovigilance, and ADR reporting among healthcare professionals (HCPs) in Timor-Leste. A cross-sectional survey with a validated, self-administered questionnaire was conducted among 600 HCPs, including clinical doctors, nurses, and pharmacy employees from one national referral and five referral hospitals. Of the 461 HCPs who responded (76.8% response rate), 98 were clinical doctors (21.3%), 311 nurses (67.4%), and 52 pharmacy employees (11.3%). The knowledge score on ADRs was 3.81 ± 0.36 out of 8, with clinical doctors, nurses, and pharmacy employees scoring 4.49 ± 0.51, 3.47 ± 0.24, and 4.56 ± 0.26, respectively. On pharmacovigilance and ADR reporting, the score was 3.00 ± 0.16 out of 8, with clinical doctors, nurses, and pharmacy employees scoring 3.36 ± 0.26, 2.81 ± 0.08, and 3.50 ± 0.24, respectively. All scores referred to the number of correctly answered questions. Positive attitudes were observed, with 53.4% agreeing that ADR reporting is crucial for drug safety, although only 22.0% reported observed ADRs. Key barriers included unavailability of reporting forms (81.0%), insufficient financial support (71.9%), and lack of reporting by colleagues (71.4%). These findings highlight the need for increased awareness, training, and resources to improve ADR reporting in Timor-Leste.</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"18 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11754541/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143025558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical Research Network: JHCRN Infrastructure and Lessons Learned","authors":"Rahul Kashyap,&nbsp;Gayane Yenokyan,&nbsp;Robert Joyner,&nbsp;Melissa Gerstenhaber,&nbsp;Mary Alderfer,&nbsp;Erika Siegrist,&nbsp;Joan Moore,&nbsp;Channing J. Paller,&nbsp;Hanan Aboumatar,&nbsp;James J. Potter,&nbsp;Stanley Watkins Jr,&nbsp;John E. Niederhuber,&nbsp;Daniel E. Ford,&nbsp;Adrian Dobs","doi":"10.1111/cts.70123","DOIUrl":"10.1111/cts.70123","url":null,"abstract":"<p>Clinical research studies are becoming increasingly complex resulting in compounded work burden and longer study cycle times, each fueling runaway costs. The impact of protocol complexity often results in inadequate recruitment and insufficient sample sizes, which challenges validity and generalizability. Understanding the need to provide an alternative model to engage researchers and sponsors and bringing clinical research opportunities to the broader community, clinical research networks (CRN) have been proposed and initiated in the United States and other parts of the world. We report on the Johns Hopkins Clinical Research Network (JHCRN), established in 2009 as a multi-disease research collaboration between the academic medical centers and community hospitals/health systems. We have discussed vision, governance, infrastructure, participating hospitals' characteristics, and lessons learned in creating this partnership. Designed to leverage organized patient communities, community-based investigators, and academic researchers, the JHCRN provides expedited research across nine health systems in the mid-Atlantic region. With one IRB of record, a centralized contracting office, and a pool of dedicated network coordinators, it facilitates research partnerships to expand research collaborations among the differing sizes and types of hospitals/health systems in a region. As of August 2024, total 81 studies-clinical trials, cohort studies, and comparative effectiveness research have been conducted, with funding from the NIH, private foundations, and industry. The JHCRN experience has enhanced understanding of the complexity of participating sites and associated ambulatory practices. In conclusion, the CRN, as an academic–community partnership, provides an infrastructure for multiple disease studies, shared risk, and increased investigator and volunteer engagement.</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"18 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11754071/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143025555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Establishment and Validation of a Machine-Learning Prediction Nomogram Based on Lymphocyte Subtyping for Intra-Abdominal Candidiasis in Septic Patients","authors":"Jiahui Zhang,&nbsp;Wei Cheng,&nbsp;Dongkai Li,&nbsp;Guoyu Zhao,&nbsp;Xianli Lei,&nbsp;Na Cui","doi":"10.1111/cts.70140","DOIUrl":"10.1111/cts.70140","url":null,"abstract":"<p>This study aimed to develop and validate a nomogram based on lymphocyte subtyping and clinical factors for the early and rapid prediction of Intra-abdominal candidiasis (IAC) in septic patients. A prospective cohort study of 633 consecutive patients diagnosed with sepsis and intra-abdominal infection (IAI) was performed. We assessed the clinical characteristics and lymphocyte subsets at the onset of IAI. A machine-learning random forest model was used to select important variables, and multivariate logistic regression was used to analyze the factors influencing IAC. A nomogram model was constructed, and the discrimination, calibration, and clinical effectiveness of the model were verified. High-dose corticosteroids receipt, the CD4⁺T/CD8⁺ T ratio, total parenteral nutrition, gastrointestinal perforation, (1,3)-β-D-glucan (BDG) positivity and broad-spectrum antibiotics receipt were independent predictors of IAC. Using the above parameters to establish a nomogram, the area under the curve (AUC) values of the nomogram in the derivation and validation cohorts were 0.822 (95% CI 0.777–0.868) and 0.808 (95% CI 0.739–0.876), respectively. The AUC in the derivation cohort was greater than the Candida score [0.822 (95% CI 0.777–0.868) vs. 0.521 (95% CI 0.478–0.563), <i>p</i> &lt; 0.001]. The calibration curve showed good predictive values and observed values of the nomogram; the Decision Curve Analysis (DCA) results showed that the nomogram had high clinical value. In conclusion, we established a nomogram based on the CD4⁺/CD8⁺ T-cell ratio and clinical risk factors that can help clinical physicians quickly rule out IAC or identify patients at greater risk for IAC at the onset of infection.</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"18 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11747989/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143015670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Penetration of linezolid and tedizolid in cerebrospinal fluid of mouse and impact of blood–brain barrier disruption","authors":"Marin Lahouati,&nbsp;Mélanie Oudart,&nbsp;Philippe Alzieu,&nbsp;Candice Chapouly,&nbsp;Antoine Petitcollin,&nbsp;Fabien Xuereb","doi":"10.1111/cts.70100","DOIUrl":"10.1111/cts.70100","url":null,"abstract":"<p>Penetration of antimicrobial treatments into the cerebrospinal fluid is essential to successfully treat infections of the central nervous system. This penetration is hindered by different barriers, including the blood–brain barrier, which is the most impermeable. However, inflammation may lead to structural alterations of these barriers, modifying their permeability. The impact of blood–brain barrier disruption on linezolid and tedizolid (antibiotics that may be alternatives to treat nosocomial meningitis) penetration in cerebrospinal fluid (CSF) remains unknown. The aim of this study is to evaluate the impact of blood brain barrier disruption on CSF penetration of linezolid and tedizolid. Female C57BI/6 J mice were used. Blood–brain barrier disruption was induced by an intraperitoneal administration of lipopolysaccharide. Linezolid (40 mg/kg) or tedizolid-phosphate (20 mg/kg) were injected intraperitoneally. All the plasma and CSF samples were analyzed with a validated UPLC-MS/MS method. Pharmacokinetic parameters were calculated using a non-compartmental approach based on the free drug concentration. The penetration ratio from the plasma into the CSF was calculated by the AUC_0-8h (Area Under Curve) ratio (AUC_0-8hCSF/AUC_0-8hplasma). Linezolid penetration ratio was 46.5% in control group and 46.1% in lipopolysaccharide group. Concerning tedizolid, penetration ratio was 5.5% in control group and 15.5% in lipopolysaccharide group. In conclusion, CSF penetration of linezolid is not impacted by blood–brain barrier disruption, unlike tedizolid, whose penetration ratio increased.</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"18 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11746922/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143061419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"First, Do No Harm: Addressing AI's Challenges With Out-of-Distribution Data in Medicine","authors":"Chu Weng,&nbsp;Wesley Lin,&nbsp;Sherry Dong,&nbsp;Qi Liu,&nbsp;Hanrui Zhang","doi":"10.1111/cts.70132","DOIUrl":"10.1111/cts.70132","url":null,"abstract":"&lt;p&gt;The advent of AI has brought transformative changes across many fields, particularly in biomedical field, where AI is now being used to facilitate drug discovery and development, enhance diagnostic and prognostic accuracy, and support clinical decision-making. For example, since 2021, there has been a notable increase in AI-related submissions to the US Food and Drug Administration (FDA) Center for Drug Evaluation and Research (CDER), reflecting the rapid expansion of AI applications in drug development [&lt;span&gt;1&lt;/span&gt;]. In addition, the rapid growth in AI health applications is reflected by the exponential increase in the number of such studies found on PubMed [&lt;span&gt;2&lt;/span&gt;]. However, the translation of AI models from development to real-world deployment remains challenging. This is due to various factors, including data drift, where the characteristics of data in the deployment phase differ from those used in model training. Consequently, ensuring the performance of medical AI models in the deployment phase has become a critical area of focus, as AI models that excel in controlled environments may still struggle with real-world variability, leading to poor predictions for patients whose characteristics differ significantly from the training set. Such cases, often referred to as OOD samples, present a major challenge for AI-driven decision-making, such as making diagnosis or selecting treatments for a patient. The failure to recognize these OOD samples can result in suboptimal or even harmful decisions.&lt;/p&gt;&lt;p&gt;To address this, we propose a prescreening procedure for medical AI model deployment (especially when the AI model risk is high), aimed at avoiding or flagging the predictions by AI models on OOD samples (Figure 1a). This procedure, we believe, can be beneficial for ensuring the trustworthiness of AI in medicine.&lt;/p&gt;&lt;p&gt;OOD scenarios are a common challenge in medical AI applications. For instance, a model trained predominantly on data from a specific demographic group may underperform when applied to patients from different demographic groups, resulting in inaccurate predictions. OOD cases can also arise when AI models encounter data that differ from the training data due to factors like variations in medical practices and treatment landscapes of the clinical trials. These issues can potentially lead to harm to patients (e.g., misdiagnosis, inappropriate treatment recommendations), and a loss of trust in AI systems.&lt;/p&gt;&lt;p&gt;The importance of detecting OOD samples to define the scope of use for AI models has been highlighted in multiple research and clinical studies. A well-known example is the Medical Out-of-Distribution-Analysis (MOOD) Challenge [&lt;span&gt;3&lt;/span&gt;], which benchmarked OOD detection algorithms across several supervised and unsupervised models, including autoencoder neural networks, U-Net, vector-quantized variational autoencoders, principle component analysis (PCA), and linear Gaussian process regression. These algorithms wer","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"18 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11739455/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143015671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CYP2C19 Genotype-Guided Antiplatelet Therapy and Clinical Outcomes in Patients Undergoing a Neurointerventional Procedure","authors":"Kayla R. Tunehag,&nbsp;Ashton F. Pearce,&nbsp;Layna P. Fox,&nbsp;George A. Stouffer,&nbsp;Sten Solander,&nbsp;Craig R. Lee","doi":"10.1111/cts.70131","DOIUrl":"10.1111/cts.70131","url":null,"abstract":"<p>In neurovascular settings, including treatment and prevention of ischemic stroke and prevention of thromboembolic complications after percutaneous neurointerventional procedures, dual antiplatelet therapy with a P2Y12 inhibitor and aspirin is the standard of care. Clopidogrel remains the most commonly prescribed P2Y12 inhibitor for neurovascular indications. However, patients carrying <i>CYP2C19</i> no-function alleles have diminished capacity for inhibition of platelet reactivity due to reduced formation of clopidogrel's active metabolite. In patients with cardiovascular disease undergoing a percutaneous coronary intervention, <i>CYP2C19</i> no-function allele carriers treated with clopidogrel experience a higher risk of major adverse cardiovascular outcomes, and multiple large prospective outcomes studies have shown an improvement in clinical outcomes when antiplatelet therapy selection was guided by <i>CYP2C19</i> genotype. Similarly, accumulating evidence has associated <i>CYP2C19</i> no-function alleles with poor clinical outcomes in clopidogrel-treated patients in neurovascular settings. However, the utility of implementing a genotype-guided antiplatelet therapy selection strategy in the setting of neurovascular disease and the clinical outcomes evidence in neurointerventional procedures remains unclear. In this review, we will (1) summarize existing evidence and guideline recommendations related to <i>CYP2C19</i> genotype-guided antiplatelet therapy in the setting of neurovascular disease, (2) evaluate and synthesize the existing evidence on the relationship of clinical outcomes to <i>CYP2C19</i> genotype and clopidogrel treatment in patients undergoing a percutaneous neurointerventional procedure, and (3) identify knowledge gaps and discuss future research directions.</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"18 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11739457/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143015669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Lab to Clinic: Effect of Academia–Industry Collaboration Characteristics on Oncology Phase 1 Trial Entry","authors":"Wonseok Yang,&nbsp;Sang-Won Lee","doi":"10.1111/cts.70135","DOIUrl":"10.1111/cts.70135","url":null,"abstract":"<p>This study investigated the success rate of Phase 1 clinical trial entry and the factors influencing it in oncology projects involving academia–industry collaboration during the discovery and preclinical stages. A total of 344 oncology projects in the discovery stage and 360 in the preclinical stage, initiated through collaborations with universities or hospitals between 2015 and 2019, were analyzed. The Phase 1 clinical trial entry success rates for oncology collaborative projects were 9.9% from the discovery stage and 24.2% from the preclinical stage. For discovery stage contracts, strong statistical significance was observed for contract type (co-development OR 16.45, <i>p</i> = 0.008; licensing OR 42.43, <i>p</i> = 0.000) and technology (cell or gene therapy OR 3.82, <i>p</i> = 0.008). In contrast, for preclinical stage contracts, significant changes were noted for cancer type (blood cancer OR 2.24, <i>p</i> = 0.004), while the year of contract signing showed a relatively weak statistical significance (OR 1.24, <i>p</i> = 0.021). No significant changes were observed concerning partner firm size and the partnership territory. This study sheds light on how the characteristics of partnerships influence the success rates of early-phase research, providing valuable insights for future strategic planning in oncology drug development.</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"18 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11730079/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142980522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of Different Machine Learning Methodologies for Predicting the Non-Specific Treatment Response in Placebo Controlled Major Depressive Disorder Clinical Trials","authors":"Roberto Gomeni,&nbsp;Françoise Bressolle-Gomeni","doi":"10.1111/cts.70128","DOIUrl":"10.1111/cts.70128","url":null,"abstract":"<p>Placebo effect represents a serious confounder for the assessment of treatment effect to the extent that it has become increasingly difficult to develop antidepressant medications appropriate for outperforming placebo. Treatment effect in randomized, placebo-controlled trials, is usually estimated by the mean baseline adjusted difference of treatment response in active and placebo arms and is function of treatment-specific and non-specific effects. The non-specific treatment effect varies subject by subject conditional to the individual propensity to respond to placebo. This effect is not estimable at an individual level using the conventional parallel-group study design, since each subject enrolled in the trial is assigned to receive either active treatment or placebo, but not both. The objective of this study was to conduct a comparative analysis of the machine learning methodologies to estimate the individual probability of a non-specific treatment effect. The estimated probability is expected to support novel methodological approaches for better controlling effect of excessively high placebo response. At this purpose, six machine learning methodologies (gradient boosting machine, lasso regression, logistic regression, support vector machines, <i>k</i>-nearest neighbors, and random forests) were compared to the multilayer perceptrons artificial neural network (ANN) methodology for predicting the probability of individual non-specific treatment response. ANN achieved the highest overall accuracy among all methods tested. A fivefold cross-validation was used to assess performances and risks of overfitting of the ANN model. The analysis conducted without subjects with non-specific effect indicated a significant increase of signal detection with significant increase in effect size.</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"18 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11729444/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142980598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Navigating Recent Changes in Dosing Information: Dynamics of FDA-Approved Monoclonal Antibodies in Post-Marketing Realities","authors":"Nai Lee,&nbsp;Su-jin Rhee,&nbsp;Seong Min Koo,&nbsp;So Won Kim,&nbsp;Gyo Eun Lee,&nbsp;Yoon A Yie,&nbsp;Yun Kim","doi":"10.1111/cts.70125","DOIUrl":"10.1111/cts.70125","url":null,"abstract":"<p>Monoclonal antibodies (mAbs) are critical components in the therapeutic landscape, but their dosing strategies often evolve post-approval as new data emerge. This review evaluates post-marketing label changes in dosing information for FDA-approved mAbs from January 2015 to September 2024, with a focus on both initial and extended indications. We systematically analyzed dosing modifications, categorizing them into six predefined groups: Dose increases or decreases, inclusion of new patient populations by body weight or age, shifts from body weight-based dosing to fixed regimens, and adjustments in infusion rates. Among the 86 mAbs evaluated, 21% (<i>n</i> = 18) exhibited changes in dosing information for the initial indication, with a median time to modification of 37.5 months (range: 5–76 months). Furthermore, for mAbs with extended indications (<i>n</i> = 26), 19.2% (<i>n</i> = 5) underwent dosing changes in their first extensions, with a median time to adjustment of 31 months (range: 8–71 months). Key drivers for these adjustments included optimizing therapeutic efficacy, addressing safety concerns, accommodating special populations, and enhancing patient convenience. We also discuss the role of model-informed drug development, real-world evidence, and pharmacogenomics in refining mAb dosing strategies. These insights underscore the importance of ongoing monitoring and data integration in the post-marketing phase, providing a foundation for future precision medicine approaches in mAb therapy.</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"18 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11729449/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142980524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}