CPT: Pharmacometrics & Systems Pharmacology最新文献

{"title":"Modeling of Disease Progression of Type 2 Diabetes Using Real-World Data: Quantifying Competing Risks of Morbidity and Mortality.","authors":"Hanna Kunina, Stefan Franzén, Maria C Kjellsson","doi":"10.1002/psp4.13301","DOIUrl":"https://doi.org/10.1002/psp4.13301","url":null,"abstract":"<p><p>Type 2 diabetes (T2D) is a progressive metabolic disorder that could be an underlying cause of long-term complications that increase mortality. The assessment of the probability of such events could be essential for mortality risk management. This work aimed to establish a framework for risk predictions of macrovascular complications (MVC) and diabetic kidney disease (DKD) in patients with T2D, using real-world data from the Swedish National Diabetes Registry (NDR), in the presence of mortality as a competing risk. The study consisted of 41,517 patients with T2D registered in NDR between 2005 and 2013. At inclusion, patients were newly diagnosed (T2D < 1 year) and had no prior evidence of DKD or MVC. Using three-quarters of the data, a five-state multistate model was established to describe competing events of MVC, DKD, a combination thereof, and the terminal state, death. Two hypotheses were investigated: (1) the risk of MVC and DKD are mutually independent, and (2) mortality is independent of morbidities. At the end of the study, the majority of individuals remained in uncomplicated T2D; however, the probability of transition to complications and death increased over time. The mortality hazard depended on the presence of morbidities and was quantified as a life expectancy decreased by 5.0, 9.7, and 12.2 years for MVC, DKD, and the combined morbidity, respectively, compared to uncomplicated T2D. An established framework with a five-state model incorporating competing events was shown to be a useful tool for comorbidities risk assessment in newly diagnosed patients with T2D.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143001260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Population exposure–response analysis of the effect of ritlecitinib on eyebrow assessment and eyelash assessment in patients with alopecia areata","authors":"Yuchen Wang,&nbsp;Yeamin Huh,&nbsp;Alexandre Lejeune","doi":"10.1002/psp4.13283","DOIUrl":"10.1002/psp4.13283","url":null,"abstract":"<p>Ritlecitinib is an orally bioavailable, small molecule that has been approved by the U.S. Food and Drug Administration (FDA) as a once-daily oral treatment option for people 12 years of age and older with severe alopecia areata. This article assessed the exposure–response (ER) relationship of eyebrow and eyelash assessment (EBA/ELA) scores on ritlecitinib and compared them to the Severity of Alopecia Tool (SALT) score (primary endpoint) ER relationship on ritlecitinib. EBA and ELA both are numeric rating scales (NRS) with four levels (0 the most severe, 3 the normal). Longitudinal ER modeling with ordinal regression was conducted to describe ritlecitinib efficacy regarding the hair regrowth in eyebrows and eyelashes separately. The average concentration in the time interval between two adjacent EBA/ELA records was used as the exposure metric. The developed models described the longitudinal EBA/ELA profile and the responder rates adequately. The ER models and the model-based simulations implied that the tested doses in the phase IIb/III clinical trial are in the ascending region, but the magnitude of loading dose effect on earlier efficacy is different across the efficacy endpoints of EBA, ELA, and SALT scores (which could be explained by the estimated <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>E</mi>\u0000 <msub>\u0000 <mi>C</mi>\u0000 <mn>50</mn>\u0000 </msub>\u0000 </mrow>\u0000 </semantics></math> [concentration at half maximum effect]). The established longitudinal ER relationships supported the selection of 50 mg dose for overall Alopecia areata (AA) patients with impaired eyebrow and eyelash hairs. The presented analysis using the ordinal regression model can be utilized in any ER analysis where PD response is an ordinal categorical variable.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"14 2","pages":"389-402"},"PeriodicalIF":3.1,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13283","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142982848","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":"A Multiple-Model-Informed Drug-Development Approach for Optimal Regimen Selection of an Oncolytic Virus in Combination With Pembrolizumab.","authors":"Akihiro Yamada, Mary P Choules, Frances A Brightman, Shigeru Takeshita, Shinsuke Nakao, Nobuaki Amino, Takeshi Nakayama, Masato Takeuchi, Kanji Komatsu, Fernando Ortega, Hitesh Mistry, David Orrell, Christophe Chassagnole, Peter L Bonate","doi":"10.1002/psp4.13297","DOIUrl":"https://doi.org/10.1002/psp4.13297","url":null,"abstract":"<p><p>The antitumor efficacy of an intratumoral injection of a genetically engineered oncolytic vaccinia virus carrying human IL-7 and murine IL-12 genes (hIL-7/mIL-12-VV) was demonstrated in CT26.WT-bearing mice. In the CT26.WT-bearing mouse model, the efficacy of the combination of hIL-7/mIL-12-VV plus the anti-programmed cell death protein (PD)-1 antibody was determined to be correlated with the timing of administration: greater efficacy was observed when hIL-7/mIL-12-VV was administered before the anti-PD-1 agent instead of simultaneous administration. To identify an optimal dosing regimen for first-in-human clinical trials, a multiple model-informed drug-development (MIDD) approach was used through development of a quantitative systems pharmacology (QSP) model and an agent-based model (ABM). All models were built and verified using available literature and preclinical study data. Multiple dosing scenarios were explored using virtual populations by altering the interval between hIL-7/hIL-12-VV and pembrolizumab administration. In contrast with observations from preclinical studies, both the QSP and the ABM models demonstrated no antagonistic effect on the dose-dependent antitumor efficacy of hIL-7/hIL-12-VV by pembrolizumab in simulations of clinical therapy. Based on the MIDD strategy, it was recommended that the highest dose of hIL-7/hIL-12-VV and pembrolizumab should be administered on the same day, but with pembrolizumab administration following hIL-7/hIL-12-VV administration. Multiple different modeling approaches uniquely supported and informed the first-in-human clinical trial design by guiding the optimal dose and regimen selection.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142946268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehensive Parent-Metabolite PBPK/PD Modeling Insights Into Methotrexate Personalized Dosing Strategies in Patients With Rheumatoid Arthritis.","authors":"Xin Wang, Jiangfan Wu, Hongjiang Ye, Xiaofang Zhao, Shenyin Zhu","doi":"10.1002/psp4.13305","DOIUrl":"https://doi.org/10.1002/psp4.13305","url":null,"abstract":"<p><p>Rheumatoid arthritis (RA) is a major public health concern, which can cause serious outcomes. Low-dose methotrexate (MTX) is a cornerstone in RA treatment, but there is significant heterogeneity in clinical response. To evaluate underlying sources of pharmacokinetic variability and clinical response of MTX, a physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) model was developed using PK-sim and Mobi (version 11.1). The PBPK model included metabolism and transportation by AXO1, FPGS, GGH, RFC, and MRP2, with renal and biliary excretion. We also developed various degrees of renal insufficiency populations with subsequent dosing optimizations. A total of 23 MTX plasma concentration-time profiles were used, with 97% of predicted plasma concentrations within a two-fold range compared to observed data. The PBPK/PD modeling and simulation demonstrated that variability in renal clearance and enzymes related to MTX are likely important drivers of PK variability and there is a quantitative relationship between MTX-PG3 and RA treatment response. The PBPK/PD model could be used to guide improvement in MTX dose regimens for RA patients.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142946269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Population Pharmacokinetics and Pharmacodynamics of Sotalol Following Expedited Intravenous Loading in Patients With Atrial Arrhythmias.","authors":"Venkata K Yellepeddi, Mohamed Ismail, T Jared Bunch, Thomas F Deering, Richard Holubkov, Robert Kennedy, Suneet Mittal, Marco Perez, Jonathan P Piccini, Parash Pokharel, Salvatore Savona, Nishant Verma, Benjamin Steinberg, Kevin Watt","doi":"10.1002/psp4.13302","DOIUrl":"https://doi.org/10.1002/psp4.13302","url":null,"abstract":"<p><p>Sotalol, a class III antiarrhythmic agent, is used to maintain sinus rhythm in patients with atrial fibrillation or atrial flutter (AFIB/AFL). Despite its efficacy, sotalol's use is limited by its potential to cause life-threatening ventricular arrhythmias due to QT interval prolongation. Traditionally, sotalol administration required hospitalization to monitor these risks. The FDA approval of intravenous (IV) sotalol for loading before oral maintenance aims to reduce hospitalization duration by facilitating an expedited loading dose, transitioning to oral maintenance therapy. This study evaluates the population pharmacokinetics (PK) and pharmacodynamics (PD) of sotalol using data from the Prospective Evaluation Analysis and Kinetics of IV Sotalol (PEAKS) Registry, which includes patients with atrial arrhythmias undergoing IV sotalol loading. A nonlinear mixed-effect modeling approach was used to describe sotalol PK, considering covariates such as age, weight, sex, and renal function. The study also examined the correlation between sotalol plasma concentrations and corrected QT interval (QTc) prolongation. Sotalol PK after IV loading and two oral maintenance doses was adequately described by a two-compartment model with first-order elimination in patients with atrial arrhythmias. Weight and creatinine clearance (CrCl) were identified as covariates with significant influence on sotalol PK. A linear regression model adequately described the relationship between QTc and plasma sotalol levels (R² = 0.27). The Monte Carlo simulations showed that the IV loading doses recommended in the prescribing information did not result in significant prolongation of QTc. The data from this study supports the current dosing recommendations of IV sotalol in patients with AFIB/AFL.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142920753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A sample size analysis of a mathematical model of longitudinal tumor volume and progression-free survival for Bayesian individual dynamic predictions in recurrent high-grade glioma.","authors":"Daniel J Glazar, Solmaz Sahebjam, Hsiang-Husan M Yu, Dung-Tsa Chen, Menal Bhandari, Heiko Enderling","doi":"10.1002/psp4.13290","DOIUrl":"https://doi.org/10.1002/psp4.13290","url":null,"abstract":"<p><p>Patients with recurrent high-grade glioma (rHGG) have a poor prognosis with median progression-free survival (PFS) of <7 months. Responses to treatment are heterogenous, suggesting a clinical need for prognostic models. Bayesian data analysis can exploit individual patient follow-up imaging studies to adaptively predict the risk of progression. We propose a novel sample size analysis for Bayesian individual dynamic predictions and demonstrate proof of principle. We coupled a nonlinear mixed effects tumor growth inhibition model with a survival model. Longitudinal tumor volumes and time-to-progression were simulated for 2000 in silico rHGG patients. Bayesian individual dynamic predictions of PFS curves were evaluated using area under the receiver operating characteristic curve (AUC) and Brier skill score (BSS). We investigated the effects of sample size on AUC and BSS margins of error. A power law relationship was observed between sample size and margins of error of AUC and BSS. Sample size was also found to be negatively correlated with margins of error and landmark time. We explored the use of this sample size analysis as a clinical look-up table for prospective clinical trial design and retrospective clinical data analysis. Here, we motivate the application of Bayesian individual dynamic predictions as a clinical end point for clinical trial design. Doing so could aid in the development of study protocols with patient-specific adaptations (escalate or de-escalate dose or frequency of drug administration, increase or decrease the frequency of follow-up, or change therapeutic modality) according to patient-specific prognosis. Future developments of this approach will focus on further model development and validation.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142906647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"QSP Modeling Shows Pathological Synergism Between Insulin Resistance and Amyloid-Beta Exposure in Upregulating VCAM1 Expression at the BBB Endothelium.","authors":"Zengtao Wang, Vaishnavi Veerareddy, Xiaojiao Tang, Kevin J Thompson, Sunil Krishnan, Krishna R Kalari, Karunya K Kandimalla","doi":"10.1002/psp4.13296","DOIUrl":"10.1002/psp4.13296","url":null,"abstract":"<p><p>Type 2 diabetes mellitus (T2DM), characterized by insulin resistance, is closely associated with Alzheimer's disease (AD). Cerebrovascular dysfunction is manifested in both T2DM and AD, and is often considered as a pathological link between the two diseases. Insulin signaling regulates critical functions of the blood-brain barrier (BBB), and endothelial insulin resistance could lead to BBB dysfunction, aggravating AD pathology. However, insulin signaling is intrinsically dynamic and involves interactions among numerous molecular mediators. Hence, a mechanistic systems biology model is needed to understand how insulin regulates BBB physiology and the consequences of its impairment in T2DM and AD. In this study, we investigated the pharmacodynamic effect of insulin on the expression of vascular cell adhesion molecule 1 (VCAM1), a marker of cerebrovascular inflammation. Intriguingly, normal insulin concentrations selectively activated the PI3K-AKT pathway, leading to decreased VCAM1 expression. However, exposure to supraphysiological insulin levels, which is present in insulin resistance, activated both PI3K-AKT and MEK-ERK pathways, and increased VCAM1 expression. We developed a mathematical model that adequately described the dynamics of various insulin signaling nodes and VCAM1 expression. Further, the model was integrated with in vitro proteomics and transcriptomics data from AD patients to simulate VCAM1 expression under pathological conditions. This approach allowed us to establish a quantitative systems pharmacology framework to investigate BBB dysfunction in AD and metabolic syndrome, thereby offering opportunities to identify specific disruptions in molecular networks that will enable us to identify novel therapeutic targets.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142892113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Disease Progression Mathematical Modeling With a Case Study on Hepatitis B Virus Infection.","authors":"Clémence Boivin-Champeaux, Nieves Velez de Mendizabal, Aksana Jones, Scott Balsitis, Stephan Schmidt, Justin S Feigelman, Francine Johansson Azeredo","doi":"10.1002/psp4.13298","DOIUrl":"https://doi.org/10.1002/psp4.13298","url":null,"abstract":"<p><p>Chronic Hepatitis B presents a significant health and socioeconomic burden. The risk of hepatocellular carcinoma remains elevated although treatments are available. Achieving an optimal treatment regimen necessitates a deep comprehension of the dynamic relationship between the virus and its host across disease states. This tutorial elucidates essential considerations for establishing a disease modeling platform to facilitate informed decision-making in hepatitis B treatment strategies. We review several published models of varying complexity and describe the context that motivated each model's structure and assumptions. Several of the models are made available in an interactive RShiny app to demonstrate the influence of model choice and sensitivity to the choice of parameter values.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142892777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mechanistic Learning for Predicting Survival Outcomes in Head and Neck Squamous Cell Carcinoma.","authors":"Kevin Atsou, Anne Auperin, Jôel Guigay, Sébastien Salas, Sebastien Benzekry","doi":"10.1002/psp4.13294","DOIUrl":"https://doi.org/10.1002/psp4.13294","url":null,"abstract":"<p><p>We employed a mechanistic learning approach, integrating on-treatment tumor kinetics (TK) modeling with various machine learning (ML) models to address the challenge of predicting post-progression survival (PPS)-the duration from the time of documented disease progression to death-and overall survival (OS) in Head and Neck Squamous Cell Carcinoma (HNSCC). We compared the predictive power of model-derived TK parameters versus RECIST and assessed the efficacy of nine TK-OS ML models against conventional survival models. Data from 526 advanced HNSCC patients treated with chemotherapy and cetuximab in the TPExtreme trial were analyzed using a double-exponential model. TK parameters from the first line and maintenance (TKL1) or after four cycles (TK4) were used to predict PPS and post-cycle 4 OS (OS4), combined with 12 baseline parameters. While ML algorithms underperformed compared to the Cox model for PPS, a random survival forest was superior for OS prediction using TK4 and surpassed RECIST-based metrics. This model demonstrated unbiased OS4 prediction, suggesting its potential for improving HNSCC treatment evaluation. Trial Registration: ClinicalTrials.gov identifier: NCT02268695.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142892778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a physiologically-based pharmacokinetic model for Ritonavir characterizing exposure and drug interaction potential at both acute and steady-state conditions.","authors":"Lien Thi Ngo, Woojin Jung, Tham Thi Bui, Hwi-Yeol Yun, Jung-Woo Chae, Jeremiah D Momper","doi":"10.1002/psp4.13293","DOIUrl":"https://doi.org/10.1002/psp4.13293","url":null,"abstract":"<p><p>Ritonavir (RTV) is a potent CYP3A inhibitor that is widely used as a pharmacokinetic (PK) enhancer to increase exposure to select protease inhibitors. However, as a strong and complex perpetrator of CYP3A interactions, RTV can also enhance the exposure of other co-administered CYP3A substrates, potentially causing toxicity. Therefore, the prediction of drug-drug interactions (DDIs) and estimation of dosing requirements for concomitantly administered drugs is imperative. In this study, we aimed to develop a physiologically-based PK (PBPK) model for RTV using the PK-sim® software platform. A total of 13 clinical PK studies of RTV covering a wide dose range (100 to 600 mg including both single and multiple dosing), and eight clinical DDI studies with RTV on CYP3A and P-gp substrates, including alprazolam, midazolam, rivaroxaban, clarithromycin, fluconazole, sildenafil, and digoxin were used for the model development and evaluation. Chronopharmacokinetic differences (between morning vs. evening doses) and limitations in parameter estimation for biochemical processes of RTV from in vitro studies were incorporated in the PBPK model. The final developed PBPK model predicted 100% of RTV AUC_last and C_max within a twofold dimension error. The geometric mean fold error (GMFE) from all PK datasets was 1.275 and 1.194, respectively. In addition, 97% of the DDI profiles were predicted with the DDI ratios within a twofold dimension error. The GMFE values from all DDI datasets were 1.297 and 1.212, respectively. Accordingly, this model could be applied to the prediction of DDI profiles of RTV and CYP3A substrates and used to estimate dosing requirements for concomitantly administered drugs.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142876588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}