Cts-Clinical and Translational Science最新文献

{"title":"A Patient-Derived 3D Cyst Model of Polycystic Kidney Disease That Mimics Disease Development and Responds to Repurposing Candidates","authors":"Alina Meyer,&nbsp;Bola Khalil,&nbsp;Margarita Iljin,&nbsp;Hester Bange,&nbsp;Leo S. Price,&nbsp;Natalia Dyubankova,&nbsp;Gerard J. P. van Westen,&nbsp;Herman van Vlijmen,&nbsp;Dorien J. M. Peters,&nbsp;Per Artursson","doi":"10.1111/cts.70214","DOIUrl":"https://doi.org/10.1111/cts.70214","url":null,"abstract":"<p>Autosomal dominant polycystic kidney disease (ADPKD) is the most common hereditary kidney disease. Its progressively expanding, fluid-filled renal cysts eventually lead to end-stage renal disease. Despite the relatively high prevalence, treatment options are currently limited to a single drug approved by the FDA and EMA. Here, we investigated human ADPKD patient-derived three-dimensional cyst cultures (3DCC) as an in vitro model for ADPKD and drug repurposing research. First, we analyzed the proteomes of 3DCC derived from healthy and diseased tissues. We then compared the protein expression profiles with those of reference tissues, mainly from the same patients. We quantified 290 proteins affecting drug disposition and proposed target proteins for drug treatment. Lastly, we investigated the functional response of the quantified target proteins after exposure to repurposing candidates in the 3DCC. Proteomic profiling of human 3DCC reflected previously reported pathophysiological alterations, including aberrant protein expression in inflammation and metabolic reprogramming. While the 3DCCs largely recapitulated the disease phenotype in vitro, drug transporter expression was reduced compared to in vivo conditions. Target proteins for proposed repurposing candidates showed similar expression in vitro and in tissues. Exposure to these repurposing candidates inhibited cyst swelling in vitro, supporting the suitability of the 3DCC for ADPKD drug screening. In summary, our results provide new insights into the ADPKD proteome and offer a starting point for further research to improve treatment options for affected individuals.</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"18 4","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cts.70214","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836094","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":"AI-Driven Applications in Clinical Pharmacology and Translational Science: Insights From the ASCPT 2024 AI Preconference","authors":"Mohamed H. Shahin,&nbsp;Prashant Desai,&nbsp;Nadia Terranova,&nbsp;Yuanfang Guan,&nbsp;Tomáš Helikar,&nbsp;Sebastian Lobentanzer,&nbsp;Qi Liu,&nbsp;James Lu,&nbsp;Subha Madhavan,&nbsp;Gary Mo,&nbsp;Flora T. Musuamba,&nbsp;Jagdeep T. Podichetty,&nbsp;Jie Shen,&nbsp;Lei Xie,&nbsp;Mathew Wiens,&nbsp;Cynthia J. Musante","doi":"10.1111/cts.70203","DOIUrl":"https://doi.org/10.1111/cts.70203","url":null,"abstract":"<p>Artificial intelligence (AI) is driving innovation in clinical pharmacology and translational science with tools to advance drug development, clinical trials, and patient care. This review summarizes the key takeaways from the AI preconference at the American Society for Clinical Pharmacology and Therapeutics (ASCPT) 2024 Annual Meeting in Colorado Springs, where experts from academia, industry, and regulatory bodies discussed how AI is streamlining drug discovery, dosing strategies, outcome assessment, and patient care. The theme of the preconference was centered around how AI can empower clinical pharmacologists and translational researchers to make informed decisions and translate research findings into practice. The preconference also looked at the impact of large language models in biomedical research and how these tools are democratizing data analysis and empowering researchers. The application of explainable AI in predicting drug efficacy and safety, and the ethical considerations that should be applied when integrating AI into clinical and biomedical research were also touched upon. By sharing these diverse perspectives and real-world examples, this review shows how AI can be used in clinical pharmacology and translational science to bring efficiency and accelerate drug discovery and development to address patients' unmet clinical needs.</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"18 4","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cts.70203","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143818757","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":"Exploratory Study of Sex Differences in P-Glycoprotein Function at the Blood–Brain Barrier","authors":"Giordana Salvi de Souza,&nbsp;Wanling Liu,&nbsp;Pascalle Mossel,&nbsp;Joost F. Somsen,&nbsp;Anna L. Bartels,&nbsp;Cristiane R. G. Furini,&nbsp;Adriaan A. Lammertsma,&nbsp;Charalampos Tsoumpas,&nbsp;Gert Luurtsema","doi":"10.1111/cts.70196","DOIUrl":"https://doi.org/10.1111/cts.70196","url":null,"abstract":"<p>Permeability-glycoprotein (P-gp), a crucial efflux pump transporter encoded by the ABCB1 gene, plays a pivotal role in drug disposition at the blood–brain barrier (BBB) and is involved in the pharmacokinetics of numerous therapeutic agents. This study investigates differences in P-gp function at the BBB between males and females in a cohort of older (55+) healthy volunteers (HV) using [¹⁸F]MC225 and PET. Twenty HV (11 males and 9 females), free from medications that affect P-gp function and without a history of neurological or psychiatric disorders, underwent [¹⁸F]MC225 PET scans with manual arterial blood sampling. Tissue time-activity curves (TAC) were extracted using the Hammers maximum-probability atlas. Whole-blood TAC was derived from the internal carotid arteries, calibrated using manual arterial samples, and adjusted for the plasma-to-whole blood ratio and plasma parent fraction to obtain the image-derived input function. The volume of distribution (<i>V</i>_T) was estimated using a reversible two-tissue compartment model, yielding the parameter of interest. Statistical analysis revealed no significant differences in P-gp function between sexes, based on <i>V</i>_T values across various brain regions (Cohen's <i>d</i> &lt; 0.2). Furthermore, the arterial blood concentration, plasma parent fraction, and microparameters demonstrated no statistical differences between male and female participants. These findings suggest that P-gp function at the BBB does not exhibit substantial sex-related variability in healthy older adults (55+). For future [¹⁸F]MC225 PET studies, a mixed-sex population can serve as an appropriate age-matched control group for neurodegenerative studies. Further research is needed to explore sex-related differences in younger populations, particularly with respect to hormonal cycles.</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"18 4","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cts.70196","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809580","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":"Large Language Models and Their Applications in Drug Discovery and Development: A Primer","authors":"James Lu,&nbsp;Keunwoo Choi,&nbsp;Maksim Eremeev,&nbsp;Jogarao Gobburu,&nbsp;Srijib Goswami,&nbsp;Qi Liu,&nbsp;Gary Mo,&nbsp;Cynthia J. Musante,&nbsp;Mohamed H. Shahin","doi":"10.1111/cts.70205","DOIUrl":"https://doi.org/10.1111/cts.70205","url":null,"abstract":"<p>Large language models (LLMs) have emerged as powerful tools in many fields, including clinical pharmacology and translational medicine. This paper aims to provide a comprehensive primer on the applications of LLMs to these disciplines. We will explore the fundamental concepts of LLMs, their potential applications in drug discovery and development processes ranging from facilitating target identification to aiding preclinical research and clinical trial analysis, and practical use cases such as assisting with medical writing and accelerating analytical workflows in quantitative clinical pharmacology. By the end of this paper, clinical pharmacologists and translational scientists will have a clearer understanding of how to leverage LLMs to enhance their research and development efforts.</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"18 4","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cts.70205","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143818736","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 Evaluation of the Effect of Aficamten on QT/QTc Interval in Healthy Participants","authors":"Neha Maharao,&nbsp;Donghong Xu,&nbsp;Tyrell J. Simkins,&nbsp;Owen Bowles,&nbsp;Genzhou Liu,&nbsp;Youcef Benattia,&nbsp;Adrienne Griffith,&nbsp;Stephen B. Heitner,&nbsp;Stuart Kupfer,&nbsp;Polina German","doi":"10.1111/cts.70218","DOIUrl":"https://doi.org/10.1111/cts.70218","url":null,"abstract":"<p>Aficamten is a next-in-class, small-molecule, cardiac myosin inhibitor in development for treating hypertrophic cardiomyopathy (HCM). This 2-part study evaluated aficamten's impact on QTc interval in healthy participants. Part A (<i>n</i> = 10) was an open-label study to find the appropriate dose for thorough QT (TQT) evaluation in Part B. Part B (<i>n</i> = 34) was a double-blind, 3-way crossover TQT study conducted as per ICH E14 guidance using negative (placebo) and positive (moxifloxacin) controls. A single 50 mg aficamten dose achieved exposures (C_max range: 124–1660 ng/mL) comparable to the highest clinical dose (20 mg QD) in obstructive HCM patients (NCT05186818; [C_max range: 131–1230 ng/mL]) and was chosen for TQT evaluation. Using concentration-QT (C-QT) modeling, the placebo- and baseline-corrected QT interval using Fridericia's correction (ΔΔQTcF) was −1.82 msec (90% CI: −3.43, −0.214) at peak aficamten concentrations (298.3 ng/mL) following the 50 mg dose. The 90% CI upper bound of ΔΔQTcF for aficamten was &lt; 10 msec at all post-dose time points. Assay sensitivity was established by the 90% CI lower bound for moxifloxacin (ΔΔQTcF) exceeding 5 msec. Aficamten did not cause QTc prolongation (using C-QT and by time point analyses) within observed plasma concentrations up to 1660 ng/mL (aficamten), 213 ng/mL (metabolite CK-3834282), and 343 ng/mL (metabolite CK-3834283). No clinically meaningful effect on electrocardiogram parameters, including absolute QTcF (≤ 450 msec) and change from baseline in QTcF (≤ 30 msec) was noted in aficamten-treated participants. Aficamten was generally well tolerated. In conclusion, there was no evidence of aficamten-mediated QTc prolongation across the therapeutic concentration range in a formal TQT study.</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"18 4","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cts.70218","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143801696","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":"Identifying Concepts of Physical Activity Which Are Clinically Meaningful to Patients and Care Providers: A Systematic Review of Qualitative Research","authors":"Candice Taguibao,&nbsp;Salma Ajraoui,&nbsp;Jake Centra,&nbsp;Kieran F. Reid,&nbsp;Christina Daskalopoulou,&nbsp;Alberto Conde Freniche,&nbsp;Alan L. Hamilton,&nbsp;Astrid M. H. Horstman,&nbsp;Benjamin X. Collins,&nbsp;Jessilyn Dunn,&nbsp;Elena S. Izmailova","doi":"10.1111/cts.70191","DOIUrl":"https://doi.org/10.1111/cts.70191","url":null,"abstract":"<p>Physical activity (PA) is indispensable for overall health. Sub-optimal PA is linked to reduced quality of life (QOL) and premature death. In clinical research and therapeutics development, defining aspects of PA that are meaningful to patients and care providers is essential for designing tailored interventions, identifying individual contextual factors, and enhancing patient satisfaction and engagement in their own well-being. As digital health technologies (DHTs) measuring PA rapidly evolve, there is an opportunity to further define concepts. A systematic review of qualitative studies to identify concepts of PA that are meaningful to patients and care providers was conducted. Conditions covered included Parkinson's disease, multiple sclerosis, chronic obstructive pulmonary disease, cancer, Duchenne muscular dystrophy, chronic heart failure, sickle cell disease, osteoarthritis, and sarcopenia. We analyzed studies published in the last 20 years utilizing qualitative or mixed methods techniques to describe aspects of PA that patients want to prevent from worsening or improve. Among the 5228 articles returned, 105 studies were included. Thematic synthesis revealed five meaningful aspects of health (MAH) related to PA: ambulation-dependent activities, balance-dependent activities, activities needing upper limb function, changing body positions, and participating in activities of different intensities. Patients also reported PA as important to QOL and influenced by internal and external facilitators and barriers. This research presents new findings related to PA MAHs across various therapeutic areas, which go beyond walking. The findings provide a foundation for defining concepts of interest, measures, and endpoints, with applications in clinical research and care, including patient-focused development of digitally derived measures.</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"18 4","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cts.70191","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143770190","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":"Potential Meets Practicality: AI's Current Impact on the Evidence Generation and Synthesis Pipeline in Health Economics","authors":"Nichola R. Naylor,&nbsp;Noemi Hummel,&nbsp;Carl de Moor,&nbsp;Ananth Kadambi","doi":"10.1111/cts.70206","DOIUrl":"https://doi.org/10.1111/cts.70206","url":null,"abstract":"&lt;p&gt;Health economics and outcomes research (HEOR) plays a key but often underappreciated role in drug development, providing essential evidence to inform healthcare policy and reimbursement by global payors. While there is broad enthusiasm around the potential of artificial intelligence (AI) among HEOR researchers [&lt;span&gt;1&lt;/span&gt;], there are challenges related to its widespread adoption. First, there are a variety of approaches that fall under the AI umbrella, including generative AI based on natural language processing (NLP), large language models (LLMs), and machine learning (ML) methods that classify, cluster, and predict outcomes. Each has potential distinct roles in HEOR, but there are few published evaluations of how these technologies can be practically applied across a breadth of research areas. Second, stakeholder perspectives may impact the value of AI. For example, within the pharmaceutical industry, AI end-users may be interested in more efficient execution of rote tasks, while regulatory bodies and health technology assessment (HTA) agencies, with a wider-ranging public health purview, advise a cautious approach that upholds established scientific practices while ensuring compliance with legal, ethical, data protection requirements and quality standards [&lt;span&gt;2&lt;/span&gt;].&lt;/p&gt;&lt;p&gt;Herein, we examine the current practical and potential future applications of AI across activities critical to payor reimbursement in HEOR. Figure 1 highlights the connectivity of three major HEOR research tools (evidence synthesis, economic modeling and real-world evidence (RWE) generation and evaluation), while also giving an overview of how AI is currently being applied in these fields.&lt;/p&gt;&lt;p&gt;Evidence synthesis refers broadly to activities related to review, curation, and extraction of information from published literature. Within this space, including informal targeted and formal systematic literature reviews (SLRs), AI has been applied to facilitate more efficient and thorough processes via automation, enhanced precision, and the management of large volumes of data while saving time, including:&lt;/p&gt;&lt;p&gt;AI has the potential to transform how researchers analyze RWD and improve their efficiency in generating insights from RWE. AI excels at processing unstructured data, such as text from clinical notes, medical images, and social media posts, using tools like NLP and image recognition [&lt;span&gt;5&lt;/span&gt;]. This has enabled the assessment of the patient journey, treatment patterns, and resource utilization, and can also be used to monitor pharmacovigilance or flu dynamics. ML algorithms, particularly deep learning models, can handle high-dimensional data (e.g., genomics, imaging, and multi-omics data) with millions of variables, and multimodal data (e.g., electronic health record [EHR] data, genomic data, imaging, and sensor data) can be integrated and learned from to find patterns across different types of inputs. As such, these data sources can be used to automatica","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"18 4","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cts.70206","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143770377","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":"Identification of Risk Factors and Predictive Indicators for Tigecycline-Associated Hypofibrinogenemia","authors":"Xiaohui Liu,&nbsp;Xuefeng Yuan,&nbsp;Long Wen,&nbsp;Xin Tan,&nbsp;Qian Sui,&nbsp;Jiheng Liu","doi":"10.1111/cts.70213","DOIUrl":"https://doi.org/10.1111/cts.70213","url":null,"abstract":"<p>To investigate the prevalence, clinical manifestations, and risk factors of hypofibrinogenemia after tigecycline use, which can disrupt coagulation and potentially hinder antimicrobial therapy. This observational study was conducted from January to December 2021 at a tertiary general hospital in China. All patients over 18 years old who received tigecycline for more than 48 h were included. After treatment with tigecycline, patients were divided into two groups based on fibrinogen plasma concentrations of less than 2.0 g/L. Multivariable logistic regression was performed to identify risk factors for hypofibrinogenemia associated with tigecycline. A total of 50 patients (mean age 71.3 ± 20.2 years) were analyzed. The median duration of treatment was 8 days (range: 3 to 20 days). Among the 24 patients who developed hypofibrinogenemia, three gastrointestinal bleeding events were observed, and four of these patients required fibrinogen administration. We identified the total therapeutic dose (odds ratio (OR) = 15.28, 95% confidence interval (CI) 2.10–111.02, <i>p</i> = 0.01) and a baseline direct bilirubin level greater than 0.4 mg/dL (OR = 5.79, 95% CI 1.13–27.98, <i>p</i> = 0.04) as risk factors for tigecycline-induced hypofibrinogenemia. Conversely, a baseline fibrinogen level (OR = 0.53, 95% CI 0.29–0.97, <i>p</i> = 0.04) appeared to be a protective factor. Healthcare professionals should be aware that the administration of tigecycline may be associated with hypofibrinogenemia and severe adverse reactions. Regular monitoring of coagulation is essential, particularly for patients with liver dysfunction, low baseline fibrinogen levels, elevated baseline direct bilirubin levels, or those receiving higher total therapeutic doses.</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"18 4","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cts.70213","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769974","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":"New Function for Safety Signal Monitoring in MID-NET®: The Case of an Anti-COVID-19 Drug","authors":"Yusuke Okada,&nbsp;Takashi Ando,&nbsp;Fumitaka Takahashi,&nbsp;Kenichi Watanabe,&nbsp;Kazuhiro Kajiyama,&nbsp;Tomoaki Hasegawa,&nbsp;Satomi Inomata,&nbsp;Yuki Kinoshita,&nbsp;Shinya Watanabe,&nbsp;Yoshiaki Uyama","doi":"10.1111/cts.70208","DOIUrl":"https://doi.org/10.1111/cts.70208","url":null,"abstract":"<p>Real-world data play a key role in monitoring drug safety at the post-marketing stage. However, challenges on how to rapidly and continuously obtain analytical results of many outcomes for drug safety signal monitoring still remain. We aimed to establish a rapid and continuous monitoring tool for drug safety assessment based on real-world data in Japan. An automated process for a new-user cohort design with customizable analytical conditions was developed. The customizable analytical conditions include exposure and control drugs, 46 outcomes related to liver and kidney functions, blood tests, biomarkers, and time period of interest. Statistical analyses were performed to evaluate the outcome status (present/absent) and calculate the adjusted hazard ratio, with a 95% confidence interval of exposure to control. We monitored the safety signals of an anti-COVID-19 drug (combination of tixagevimab and cilgavimab) and compared them with those of two controls (peramivir and the combination of casirivimab and imdevimab) to examine the practical utility of this new tool. Our study provided helpful information (e.g., new safety signals) on many outcomes at multiple time points, which could enhance the understanding of drug safety profiles soon after approval. Our function can be used to rapidly and continuously monitor drug safety signals and contribute to strengthening drug safety monitoring in Japan.</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"18 4","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cts.70208","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143749318","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":"Correction to “Toward an Effective Translational Science Engine”","authors":"","doi":"10.1111/cts.70219","DOIUrl":"https://doi.org/10.1111/cts.70219","url":null,"abstract":"<p>Ioachimescu OC, Shaker R. Toward an Effective Translational Science Engine. <i>Clin Transl Sci</i>. 2024 Nov;17(11):e70069. doi: 10.1111/cts.70069. PMID: 39539022; PMCID: PMC11561134.</p><p>The funding statement should read “NIH (NCATS) UL1 TR001436 (MCW IRB# FP00015891)” rather than “CTSA: FP00015891.”</p><p>The corresponding author's email address should be changed to <span>[email protected]</span>.</p><p>Affiliation 1 should be Milwaukee, Wisconsin, not Hartland, Wisconsin.</p><p>The address for the corresponding author should be 8701 Watertown Plank Rd., Suite M1350, Milwaukee, WI 53226, USA.</p><p>We apologize for this error.</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"18 4","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cts.70219","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761961","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}