Cts-Clinical and Translational Science最新文献

{"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}

{"title":"New Insights Into Hepatic Impairment (HI) Trials","authors":"Sebastian Haertter,&nbsp;Maximilian Lobmeyer,&nbsp;Brian C. Ferslew,&nbsp;Pallabi Mitra,&nbsp;Thomas Arnhold","doi":"10.1111/cts.70130","DOIUrl":"10.1111/cts.70130","url":null,"abstract":"<p>Hepatic impairment (HI) trials are traditionally part of the clinical pharmacology development to assess the need for dose adaptation in people with impaired metabolic capacity due to their diseased liver. This review aimed at looking into the data from dedicated HI studies, cluster these data into various categories and connect the effect by HI with reported pharmacokinetics (PK) properties in order to identify patterns that may allow waiver, extrapolations, or adapted HI study designs. Based on a ratio ≥ 2 or ≤ 0.5 in AUC or Cmax between hepatically impaired participants/healthy controls these were considered “positive” or “negative”. In case of more than one HI severity stratum per compound included in the HI trial, the comparison of the AUC ratios for mild, moderate, or severe HI were used to investigate the increase across HI categories. For the in total 436 hits, relevant PK information could be retrieved for 273 compounds of which 199 were categorized negative, 69 positive ups and 5 positive downs. Fourteen out of 69 compounds demonstrated a steep increase in the AUC ratios from mild to severe HI. Compounds demonstrating a steep increase typically had a high plasma protein binding of &gt; 95%, high volume of distribution, lower absolute bioavailability, minor elimination via the kidneys, were predominantly metabolized by CYP3A4 or CYP2D6 and the majority of these compounds were substrates of OATP1B1. While for compounds with steep increase studies in all severity strata may be warranted they may also offer the potential to estimate the appropriate doses in an HI trial. On the other hand, for compounds with slow or no increase across HI severity strata, reduced HI trials may be justified, e.g. only testing PK in moderate HI.</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"18 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11724149/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142967405","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":"Integrating Model-Informed Drug Development With AI: A Synergistic Approach to Accelerating Pharmaceutical Innovation","authors":"Karthik Raman,&nbsp;Rukmini Kumar,&nbsp;Cynthia J. Musante,&nbsp;Subha Madhavan","doi":"10.1111/cts.70124","DOIUrl":"10.1111/cts.70124","url":null,"abstract":"<p>The pharmaceutical industry constantly strives to improve drug development processes to reduce costs, increase efficiencies, and enhance therapeutic outcomes for patients. Model-Informed Drug Development (MIDD) uses mathematical models to simulate intricate processes involved in drug absorption, distribution, metabolism, and excretion, as well as pharmacokinetics and pharmacodynamics. Artificial intelligence (AI), encompassing techniques such as machine learning, deep learning, and Generative AI, offers powerful tools and algorithms to efficiently identify meaningful patterns, correlations, and drug–target interactions from big data, enabling more accurate predictions and novel hypothesis generation. The union of MIDD with AI enables pharmaceutical researchers to optimize drug candidate selection, dosage regimens, and treatment strategies through virtual trials to help derisk drug candidates. However, several challenges, including the availability of relevant, labeled, high-quality datasets, data privacy concerns, model interpretability, and algorithmic bias, must be carefully managed. Standardization of model architectures, data formats, and validation processes is imperative to ensure reliable and reproducible results. Moreover, regulatory agencies have recognized the need to adapt their guidelines to evaluate recommendations from AI-enhanced MIDD methods. In conclusion, integrating model-driven drug development with AI offers a transformative paradigm for pharmaceutical innovation. By integrating the predictive power of computational models and the data-driven insights of AI, the synergy between these approaches has the potential to accelerate drug discovery, optimize treatment strategies, and usher in a new era of personalized medicine, benefiting patients, researchers, and the pharmaceutical industry as a whole.</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"18 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11724156/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142967402","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":"Safety, Tolerability, and Pharmacokinetics of NIM-1324 an Oral LANCL2 Agonist in a Randomized, Double-Blind, Placebo-Controlled Phase I Clinical Trial","authors":"Andrew Leber,&nbsp;Raquel Hontecillas,&nbsp;Nuria Tubau-Juni,&nbsp;Josep Bassaganya-Riera","doi":"10.1111/cts.70129","DOIUrl":"10.1111/cts.70129","url":null,"abstract":"<p>NIM-1324 is an oral investigational new drug for autoimmune disease that targets the Lanthionine Synthetase C-like 2 (LANCL2) pathway. Through activation of LANCL2, NIM-1324 modulates CD4+ T cells to bias signaling and cellular metabolism toward increased immunoregulatory function while providing similar support to phagocytes. In primary human immune cells, NIM-1324 reduces type I interferon and inflammatory cytokine (IL-6, IL-8) production. Oral NIM-1324 was assessed for safety, tolerability and PK in normal healthy volunteers in a randomized, double-blind, placebo-controlled trial. Subjects (<i>n</i> = 57) were randomized into five single ascending dose (SAD) cohorts (250, 500, 750, 1000, 1500 mg, p.o.) and three multiple ascending dose (MAD) cohorts (250, 750, 1500 mg QD for 7 days, p.o.). NIM-1324 did not increase total AE rates in individual cohorts or pooled active groups in SAD or MAD with no SAEs in the study. Oral NIM-1324 dosing does not result in any clinically significant findings by biochemistry, coagulation, ECG, hematology, or urinalysis when compared to placebo. Plasma exposure, as measured by area under the curve from 0 to 24 h (AUC_0-24), scaled dose proportionally over 250–1000 mg. At 250 mg, NIM-1324 successfully engaged the target with an upregulation of Lancl2 and key transcriptional biomarkers in whole blood. In conclusion, NIM-1324 treatment is well-tolerated up to daily oral doses of at least 1500 mg (nominal), a ≥ six-fold margin over the anticipated therapeutic dose, and 1000 mg (maximum observed exposure), at least a four-fold margin over the anticipated therapeutic dose with no dose limiting toxicities.</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"18 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11724152/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143055927","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":"Vaccine hesitancy or hesitancies? A latent class analysis of pediatric patients' parents","authors":"Don E. Willis,&nbsp;Marie-Rachelle Narcisse,&nbsp;Laura James,&nbsp;James P. Selig,&nbsp;Mohammed Ason,&nbsp;Aaron J. Scott,&nbsp;Lawrence E. Cornett,&nbsp;Pearl A. McElfish","doi":"10.1111/cts.70042","DOIUrl":"10.1111/cts.70042","url":null,"abstract":"<p>Vaccine hesitancy is an attitude of indecision toward vaccination that is related to but not determinative of vaccination behaviors. Although theories of vaccine hesitancy emphasize it is often vaccine-specific, we do not know the extent to which this is true across sociodemographic groups. In this study, we asked: What latent classes of vaccine hesitancy might exist when examining parents' attitudes toward vaccines in general and COVID-19 and human papillomavirus (HPV) vaccination specifically? Which sociodemographic, health access, and health-related variables are predictive of membership in those classes? To answer those questions, we analyze online survey data from parents of pediatric patients recruited through eight clinics within the University of Arkansas for Medical Sciences Rural Research Network. Data were collected between September 16, 2022 and December 6, 2022. Latent class analysis revealed three underlying classes of vaccine hesitancy, or hesitancies: The “Selectively Hesitant,” the “COVID-Centric Hesitant,” and the “Pervasively Hesitant.” Significant predictors of class membership were age, education, health insurance status, and usual source of care. Vaccine hesitancy may be specific to certain vaccines for some parents and more generalized for others. The distinct classes of vaccine hesitancy revealed in this study suggest the need for distinct approaches to addressing vaccine hesitancy depending on the population.</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"18 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11713929/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142958397","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}