Clinical Pharmacology & Therapeutics最新文献

{"title":"Advances in New Approach Methodologies at FDA's Division of Applied Regulatory Science","authors":"Ksenia Blinova,&nbsp;Kimberly Chiu,&nbsp;Anik Chaturbedi,&nbsp;Jahan Cooper,&nbsp;Tromondae K. Feaster,&nbsp;Paula L. Hyland,&nbsp;Lidiya Stavitskaya,&nbsp;Wendy W. Wu,&nbsp;Jeffry Florian,&nbsp;Rodney Rouse","doi":"10.1002/cpt.70472","DOIUrl":"10.1002/cpt.70472","url":null,"abstract":"<p>New Approach Methodologies (NAMs) represent a paradigm shift in drug development and regulatory science, offering human-relevant alternatives to traditional preclinical models. This mini-review highlights recent advances in NAM development, validation, and regulatory application from FDA's Division of Applied Regulatory Science (DARS). We describe <i>in silico</i> NAMs including quantitative systems pharmacology (QSP) modeling for opioid overdose scenarios, (quantitative) structure–activity relationship ((Q)SAR) modeling for toxicity prediction and drug–drug interaction assessment, and multi-omics approaches for pharmacodynamic biomarker discovery. Additionally, we present <i>in vitro</i> NAMs including human-induced pluripotent stem cell (hiPSC)-derived neural networks for opioid pharmacology assessment, microphysiological systems (MPS) for pulmonary drug permeability, gastrointestinal models for intestinal transport, and standardized cardiac ion channel and hiPSC-CM MEA assays for proarrhythmia risk prediction. These case studies demonstrate how NAMs can reduce reliance on animal studies while improving translation to clinical outcomes. The NAMs described here range from exploratory research tools to approaches actively informing regulatory submissions review. The integration of <i>in silico</i> and <i>in vitro</i> NAMs into regulatory decision-making frameworks represents a critical step toward more efficient, human-relevant drug development and safety assessments.</p>","PeriodicalId":153,"journal":{"name":"Clinical Pharmacology & Therapeutics","volume":"120 5","pages":"1250-1254"},"PeriodicalIF":4.9,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13539334/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148878689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Prediction to Decision Making: PBPK and QSP as Regulatory-Grade NAMs","authors":"Karen Rowland Yeo,&nbsp;Piet H. van der Graaf","doi":"10.1002/cpt.70483","DOIUrl":"10.1002/cpt.70483","url":null,"abstract":"<p>New approach methodologies (NAMs) encompass a diverse and rapidly evolving set of experimental and computational tools designed to generate human-relevant mechanistic data for use in drug development and regulatory decision making. Experimental NAMs provide insights into drug disposition, pharmacological activity, and disease biology that are difficult or impossible to obtain from traditional animal-based models. Physiologically based pharmacokinetic (PBPK) and quantitative systems pharmacology (QSP) models represent a complementary class of computational NAMs. Together, experimental and computational NAMs form an integrated translational framework that converts mechanistic biological data into quantitative predictions of human exposure, efficacy, and safety across the drug development continuum. In this state-of-the-art review, we present our perspective on the current and emerging role of PBPK and QSP as computational NAMs, supported by case studies spanning a range of regulatory and clinical applications. Across all case studies, the integration of human-relevant experimental data into mechanistic models is shown to be the critical determinant of translational success. We also discuss the evolving regulatory landscape for NAMs, including the recent FDA draft guidance on QSP-based MABEL determination, and the ICH M15 framework for model-informed drug development. Collectively, these developments signal a fundamental shift in how mechanistic models are positioned within drug development and regulatory decision making: not as alternatives to animal testing alone, but as quantitative decision-support frameworks that generate the human-relevant evidence needed to support safer, more effective, and more equitable medicines.</p>","PeriodicalId":153,"journal":{"name":"Clinical Pharmacology & Therapeutics","volume":"120 5","pages":"1195-1207"},"PeriodicalIF":4.9,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13590434/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149011742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pediatric Developmental Safety Assessment and New Approach Methodologies: US FDA/Triangle CERSI Workshop Report","authors":"Gilbert J. Burckart,&nbsp;Gelareh Abulwerdi,&nbsp;Christin L. Daniels,&nbsp;Calib Choi,&nbsp;Rebecca Racz,&nbsp;Jean-Pierre Valentin,&nbsp;Kristin Ardlie,&nbsp;Nicholas Tatonetti,&nbsp;Susan Laffan,&nbsp;Marjory Moreau,&nbsp;Will Redfern,&nbsp;Lang Li,&nbsp;James J. Hickman,&nbsp;Rajanikanth Madabushi,&nbsp;Bhagwat Prasad,&nbsp;Lynne Yao,&nbsp;Paul B. Watkins","doi":"10.1002/cpt.70493","DOIUrl":"10.1002/cpt.70493","url":null,"abstract":"<p>Pediatric developmental safety assessment presents distinct challenges in drug development. Children undergo dynamic changes in organ systems and drug disposition pathways during development, and drug-related effects may be delayed or manifested differently than in adults. Pharmacovigilance data, primarily from adults, is not always predictive of the response in children. Historically, juvenile animal studies have been used for nonclinical pediatric drug safety assessments exclusively, but the reduction in animal testing is a goal of public health agencies. To address this concern, the Office of Clinical Pharmacology at the U.S. FDA and the Triangle CERSI (Center of Excellence in Regulatory Science and Innovation) convened a workshop entitled: “Pediatric Developmental Safety Assessment and New Approach Methodologies (NAMs),” attended by experts from regulatory agencies, academia, and industry. Discussions focused on three overarching topics: (i) Secondary Pharmacology Screening Tools for Assessing Pediatric Developmental Safety; (ii) Non-clinical Tools for Pediatric Developmental Safety Assessment; and (iii) Computational and Data-Driven Tools for Pediatric Developmental Safety. These topics were examined within the framework of the S11 Nonclinical Safety Testing In Support of Development of Pediatric Pharmaceuticals and E11A Pediatric Extrapolation guidances, which promote structured, question-based pediatric extrapolation in drug development and authorization. The workshop concluded that NAMs hold considerable promise to advance the assessment of pediatric drug developmental safety.</p>","PeriodicalId":153,"journal":{"name":"Clinical Pharmacology & Therapeutics","volume":"120 5","pages":"1240-1249"},"PeriodicalIF":4.9,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13631915/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149101481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Secondary Pharmacology Targets for Pediatric Developmental Safety Assessment of New Drugs: Clinical Pharmacology Considerations","authors":"Gilbert J. Burckart,&nbsp;Sherbet Samuels,&nbsp;Susan M. Abdel-Rahman,&nbsp;Gelareh Abulwerdi,&nbsp;Elimika Fletcher,&nbsp;Mohamed A. Mohamoud,&nbsp;Apoorva Srinivasan,&nbsp;Nicholas Tatonetti,&nbsp;Rebecca Racz","doi":"10.1002/cpt.70465","DOIUrl":"10.1002/cpt.70465","url":null,"abstract":"<p>Drug safety in pediatric patients has always been a concern. Major tragedies with drugs in pediatric patients created an environment of caution, but the experience with pediatric legislation and drug development over the past 20 years has eased the cautionary approach to pediatric drug safety. The advent of new methods of safety testing, driven by the movement away from animal use in research, provides new opportunities to screen for and prevent adverse developmental effects of drugs on pediatric patients. One approach that can be explored for use in pediatric developmental safety testing is secondary pharmacology, or screening for off-target drug effects. Secondary pharmacology, which is an established screening mechanism described in ICH S7A, could provide the opportunity to answer important off-target questions. Examples of 12 specific secondary pharmacology targets related to adverse effects on pediatric development are provided. Clinical pharmacology plays a critical role in the interpretation of secondary pharmacology through quantitative studies necessary to validate positive findings and through the final assessment of the risk:benefit associated with these findings. In conclusion, secondary pharmacology screening has the potential to reduce animal testing while addressing two critical objectives: screening drugs for off-target effects and providing information on off-target effects relevant to pediatric developmental safety.</p>","PeriodicalId":153,"journal":{"name":"Clinical Pharmacology & Therapeutics","volume":"120 5","pages":"1227-1239"},"PeriodicalIF":4.9,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148885816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In Silico Clinical Trials in Drug Development: Virtual Patients, Applications, and Regulatory Convergence","authors":"Maximilian Balmus,&nbsp;Sheng-Ya Wang,&nbsp;Edward W. G. Ashton,&nbsp;Rosie K. Barrows,&nbsp;Molly Monks,&nbsp;Yuzhang Ge,&nbsp;Arthur Lefebvre,&nbsp;Steven A. Niederer","doi":"10.1002/cpt.70492","DOIUrl":"10.1002/cpt.70492","url":null,"abstract":"<p>Conventional clinical trials remain the benchmark for evaluating therapeutic safety and efficacy, yet they are constrained by escalating costs, withdrawal over the extended follow-up periods, recruitment difficulties, ethical limits, and a restricted ability to characterize heterogeneous populations. In silico clinical trials, which use computational models of patient physiology to simulate the effect of interventions across a virtual cohort, have emerged as a complementary paradigm that is efficient, scalable, and mechanistically informed. However, the maturity of in silico clinical trial applications varies considerably between physiological systems. This review pursues three aims. First, we introduce a common taxonomy for virtual patients and in silico trials, spanning levels of model personalization, from fully synthetic populations through hybrid cohorts to patient-specific digital twins, and levels of abstraction, from compartment-based models to whole-organ anatomically accurate models. Second, we survey applications across multiple organ systems, drawing on illustrative examples that expose markedly different degrees of modeling maturity, from comparatively established cardiac and hepatic safety and efficacy studies to areas where mechanistic models remain early in development. Third, we examine the regulatory landscape, tracing its evolution towards risk-informed credibility assessment, and the recent harmonization of model-informed drug development guidance. Although personalized modeling and regulatory pathways are evolving, they often do not converge within a unified framework. Their wider adoption will require robust evaluation against experimental and clinical evidence to demonstrate their predictive reliability.</p>","PeriodicalId":153,"journal":{"name":"Clinical Pharmacology & Therapeutics","volume":"120 5","pages":"1181-1194"},"PeriodicalIF":4.9,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13617259/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149068978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging a QSP Platform for Prediction of Clinical Efficacy and Safety Biomarkers in Immuno-Oncology Combination Therapy","authors":"Daniela Santurio,&nbsp;Sirin Yonucu,&nbsp;Aiman Alam-Nazki,&nbsp;Birses Debir,&nbsp;Maciej Swat,&nbsp;Rachel H. Rose,&nbsp;Inmaculada C. Sorribes,&nbsp;Miao Zhao,&nbsp;Tomoko Freshwater,&nbsp;Piet H. van der Graaf,&nbsp;Andrzej M. Kierzek","doi":"10.1002/cpt.70515","DOIUrl":"10.1002/cpt.70515","url":null,"abstract":"<p>IO Simulator is a Quantitative Systems Pharmacology platform implementing a mechanistic model of solid tumor–immune system interactions and the action of standard immuno-oncology drugs. Using a tarlatamab case study, we demonstrate how IO Simulator predicts combination therapy efficacy and cytokine biomarkers, which can be statistically related to the risk of cytokine release syndrome (CRS). This allows patient stratification and identification of doses that maximize efficacy without a substantial increase in cytokine release.</p>","PeriodicalId":153,"journal":{"name":"Clinical Pharmacology & Therapeutics","volume":"120 5","pages":"1266-1280"},"PeriodicalIF":4.9,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149126389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of Acute Liver Injury Trajectory in Patients Following Acetaminophen Overdose: A Multibiomarker Machine Learning Proof-of-Concept Study","authors":"Chris Humphries,&nbsp;Alastair M. Kilpatrick,&nbsp;Kathleen M. Scullion,&nbsp;Rhona Aird,&nbsp;Lorraine Bruce,&nbsp;Maria Elena Candela,&nbsp;Tak Yung Man,&nbsp;Stuart J. Forbes,&nbsp;James W. Dear","doi":"10.1002/cpt.70320","DOIUrl":"10.1002/cpt.70320","url":null,"abstract":"<p>Clinical translation of novel therapies can be hindered by heterogeneity-driven sample size inflation in late-stage trials. In acetaminophen-induced liver injury (APAP DILI), many patients recover spontaneously, diluting investigational drug efficacy signals. We developed a prognostic enrichment tool to identify patients with worsening injury trajectories for more efficient trial designs. Biomarker model discovery and evaluation used serum samples from three UK cohorts: the MAPP2 APAP DILI biobank (<i>n</i> = 147), an independent pre-intervention evaluation cohort from the ongoing MAIL trial (<i>n</i> = 34), and healthy controls (<i>n</i> = 13). We measured 63 biomarkers and evaluated 321,682 combinations using kernel naïve Bayes classification to predict liver injury trajectory (ALT rising vs. falling). Sensitivity analysis using patient-level grouped cross-validation showed combining multiple biomarkers while constraining collinearity was necessary to maximize performance. A four-biomarker model (MCSFR, WBC, Sodium, K18) achieved AUC 0.868 (derivation) and 0.854 (evaluation). When optimized for prognostic certainty, the model yielded a Positive Likelihood Ratio of 14.4, increasing the Positive Predictive Value for worsening injury from a baseline of 29.4% to 85.7%. Time-dependent cost-minimization modeling for a hypothetical phase 3 trial identified an application threshold (sensitivity 80.0%, specificity 91.7%, Number Needed to Screen 3.4) as the global economic optimum, resulting in an illustrative trial cost reduction from $39.0 M to $8.3 M. This proof-of-concept demonstrates multidimensional biomarker models can resolve signal dilution. Distinguishing patients destined for injury progression reduces sample size requirements, which could de-risk novel therapy development.</p>","PeriodicalId":153,"journal":{"name":"Clinical Pharmacology & Therapeutics","volume":"120 5","pages":"1326-1337"},"PeriodicalIF":4.9,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13339636/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147866074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Successful Translational Prediction of Bedaquiline's Phase 2a Clinical Trial Results against Tuberculosis: The Zebrafish as a New Approach Methodology","authors":"Bart van Lieshout,&nbsp;Aanisah Hanuun,&nbsp;J. G. Coen van Hasselt,&nbsp;Rob C. van Wijk","doi":"10.1002/cpt.70430","DOIUrl":"10.1002/cpt.70430","url":null,"abstract":"<p>Tuberculosis remains an urgent global health crisis that requires an accelerated development of novel regimens. Rodent and rabbit studies currently inform early translational predictions. However, the zebrafish (<i>Danio rerio</i>) embryo/larva, as a New Approach Methodology, provides an ethical, cost-effective, and high-throughput non-mammalian disease model to study early anti-tuberculosis drug pharmacology and bridge the gap between <i>in vitro</i> drug screens and (confirmatory) rodent experiments. This study aimed to translate the bedaquiline exposure–response relationship from zebrafish to humans to predict two phase 2a trial results in order to strengthen the use of zebrafish as a New Approach Methodology for translational anti-tuberculosis pharmacology. An established exposure–response relationship for bedaquiline in zebrafish against <i>Mycobacterium marinum</i> as a tuberculosis disease model was combined with a clinical population pharmacokinetic model to predict bacterial burden profiles from two published phase 2a clinical trials (25 to 400 mg daily). This translation incorporated the minimum inhibitory concentration ratio between <i>Mycobacterium marinum</i> and <i>Mycobacterium tuberculosis</i> to scale the zebrafish bedaquiline EC₅₀ to humans. Clinical simulations based on the scaled zebrafish exposure–response relationship demonstrated that accounting for <i>in vitro</i> drug sensitivity resulted in close alignment with observed bacterial burden across all doses and timepoints, with a mean absolute percentage error of 10.37% and a root mean squared error of 0.74 log₁₀ colony-forming units per milliliter of sputum. These findings demonstrate that early-phase clinical results for bedaquiline in tuberculosis can be successfully predicted from pharmacokinetics–pharmacodynamics in the zebrafish tuberculosis disease model. These results help advance the zebrafish as a resource-efficient and sustainable New Approach Methodology for tuberculosis drug development.</p>","PeriodicalId":153,"journal":{"name":"Clinical Pharmacology & Therapeutics","volume":"120 5","pages":"1281-1286"},"PeriodicalIF":4.9,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13503988/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148811692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing Regulatory Adoption of New Approach Methodologies","authors":"Jeffry Florian,&nbsp;Kimberly Chiu,&nbsp;Rebecca Racz,&nbsp;C. Lee Elmore,&nbsp;Shirley K. Seo,&nbsp;Ksenia Blinova,&nbsp;Wendy W. Wu,&nbsp;Rodney Rouse","doi":"10.1002/cpt.70482","DOIUrl":"10.1002/cpt.70482","url":null,"abstract":"<p>The U.S. Food and Drug Administration (FDA) Center for Drug Evaluation and Research (CDER) has a long history advancing new approach methodologies (NAMs). In collaboration with external organizations and other regulatory agencies, FDA supports continued development of NAMs for regulatory use. Within CDER, qualification programs and interdisciplinary review teams provide opportunities for increased regulatory impact of NAMs. CDER also continues to provide regulatory input to NAMs developers and end users to advance regulatory acceptance of NAMs.</p>","PeriodicalId":153,"journal":{"name":"Clinical Pharmacology & Therapeutics","volume":"120 5","pages":"1178-1180"},"PeriodicalIF":4.9,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13587586/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148987360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Biological Mechanisms to Causal Inference: Quantitative Systems Pharmacology and Causal Frameworks in Drug Development","authors":"Thomas Klabunde,&nbsp;Ramon Hernandez,&nbsp;Piet H. van der Graaf,&nbsp;Tommaso Andreani","doi":"10.1002/cpt.70486","DOIUrl":"10.1002/cpt.70486","url":null,"abstract":"<p>Digital patients and <i>in silico</i> clinical trials offer new opportunities to advance pharmacological research, clinical development, and regulatory decision-making by enabling the simulation of treatment effects prior to testing in patients. Among the most promising of these opportunities is the combined use of quantitative systems pharmacology (QSP) and causal inference (CI), two complementary paradigms that, together, address different but interlocking aspects of causal reasoning in drug development: one grounded in mechanistic simulation, the other in data-driven estimation. Mechanistic quantitative systems pharmacology (QSP) models generate physiologically interpretable predictions by encoding biological knowledge—such as compartmental flows, receptor kinetics, and feedback loops—typically in systems of differential equations. As such, QSP addresses the question: given specified mechanistic assumptions, how does the system behave under intervention? Complementing this mechanistic lens, causal inference approaches start from observed data and estimate the effects of interventions using formal causal frameworks and comparative outcome analysis. These methods explicitly address confounding, multicausality, and clinical heterogeneity, asking instead: given the data, what are the causal effects of intervening in the real world? These paradigms reflect fundamentally different representations of causality—mechanism-driven versus data-driven—two complementary lenses on the same underlying goal of understanding and predicting intervention effects. This distinction raises a central question: how can causality be consistently represented, inferred, and validated across modeling approaches, and what is required to integrate them to support robust and credible decision-making?</p>","PeriodicalId":153,"journal":{"name":"Clinical Pharmacology & Therapeutics","volume":"120 5","pages":"1255-1265"},"PeriodicalIF":4.9,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13599373/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149023342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}