CPT: Pharmacometrics & Systems Pharmacology最新文献_第3页

Structured Schemas for Provenance-Rich, LLM-Assisted QSP Model Calibration 来源丰富的结构化模式，llm辅助QSP模型校准。

IF 2.8 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2026-07-31 DOI: 10.1002/psp4.70306

Joel Eliason, Aleksander S. Popel

{"title":"Structured Schemas for Provenance-Rich, LLM-Assisted QSP Model Calibration","authors":"Joel Eliason, Aleksander S. Popel","doi":"10.1002/psp4.70306","DOIUrl":"10.1002/psp4.70306","url":null,"abstract":"Quantitative systems pharmacology (QSP) models require calibration data from literature, yet manual curation is inconsistently documented and large language model (LLM) extraction can hallucinate values and fabricate citations. We present MAPLE (Model-Aware Parameterization from Literature Evidence), which uses structured validation schemas as a collaboration interface between LLMs and modelers. Two schemas span two scales: the SubmodelTarget schema for isolated experiments constraining individual parameters, and the CalibrationTarget schema for clinical and in vivo endpoints constraining the full model. Both separate data extraction from modeling decisions, recording every value with full provenance. Targeted validators catch characteristic LLM errors by matching values to source snippets, resolving DOIs, and executing code. For a pancreatic ductal adenocarcinoma QSP model, we used MAPLE to extract and curate 37 SubmodelTargets and 45 CalibrationTargets. Before any human review, the validators triggered 50 automated retries; every value carries a direct quote from its source and a verified citation; and 11 of 19 parameters are supported by more than one independent source. The LLM drafted usable forward models and code from context, while the modeler supplied the context and scientific judgment it cannot infer, revising forward-model choices in 65% of SubmodelTargets, priors in 46%, and source relevance in all files. This evaluation covers one model in one disease area, by a single group, so it characterizes the framework rather than establishing how broadly it generalizes. MAPLE records the modeler's reasoning in a form that can be re-run and independently checked, so it is not lost when the modeling team changes.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 8","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70306","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148629746","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}

引用次数: 0

Machine Learning Enables Rapid Prediction of Acid-Reducing Agent Drug Interactions: A Streamlined Complement to PBPK Modeling 机器学习能够快速预测酸还原剂药物相互作用：PBPK模型的流线型补充

IF 2.8 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2026-07-31 DOI: 10.1002/psp4.70314

Yuanfang Qin, Lehua Yu, Tao Chen, Yonghui Zuo, Hongyun Wang, Guoping Yang, Qi Pei

{"title":"Machine Learning Enables Rapid Prediction of Acid-Reducing Agent Drug Interactions: A Streamlined Complement to PBPK Modeling","authors":"Yuanfang Qin, Lehua Yu, Tao Chen, Yonghui Zuo, Hongyun Wang, Guoping Yang, Qi Pei","doi":"10.1002/psp4.70314","DOIUrl":"https://doi.org/10.1002/psp4.70314","url":null,"abstract":"pH-dependent drug–drug interactions (DDIs) commonly occur when acid-reducing agents (ARAs) are co-administered with weakly basic drugs. Although physiologically based pharmacokinetic (PBPK) modeling effectively evaluates such DDIs, its use is limited by reliance on costly commercial software. This study developed a PBPK-informed machine learning model to support early assessment of pH-dependent DDI risk in drug development. PBPK models were built for 14 representative weakly basic drugs using literature-derived parameters to identify eight key determinants (e.g., solubility and pKa). Based on these distributions, virtual drugs were generated and simulated under varying gastric pH conditions; compounds with DDI AUC ratios < 0.1 were excluded, yielding 4339 virtual drugs. An extreme gradient boosting (XGBoost) algorithm was used to develop the machine learning model, and an external validation set comprising clinically observed data from an additional eight drugs was employed. The XGBoost model showed excellent internal performance (training: R2 = 1.00, MAPE = 0.99; test: R2 = 0.98, MAPE = 2.64). When evaluated using an external validation set comprising clinically observed data from eight drugs, 100% of the predicted values fell within the 0.5–2.0-fold range of the observed clinical values. For DDI AUC risk classification, the XGBoost model achieved an accuracy of 87.5% (7/8). This PBPK-informed ML framework enables efficient screening of pH-dependent DDI risk for weakly basic drugs co-administered with ARAs. The freely accessible web tool (https://ddi-antacid.xy3yx.com/), integrating structure-based ADMETlab3.0 estimation, offers a practical complement to conventional PBPK modeling for early drug development.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 8","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70314","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148647688","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}

引用次数: 0

Utilizing Virtual Clinical Trials to Inform Target Coverage That Drives RSV Antiviral Efficacy 利用虚拟临床试验来告知驱动RSV抗病毒疗效的靶标覆盖率

IF 2.8 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2026-07-31 DOI: 10.1002/psp4.70308

J. Cody Herron, Kathryn G. Link, Natalie Meacham, Negar Niki Alami, Daniel W. Brookes, Elaine Thomas, Jan Adams, Sima S. Toussi, Ryan Franke, Cynthia J. Musante, Isabel Najera, Richard Allen, Britton Boras

{"title":"Utilizing Virtual Clinical Trials to Inform Target Coverage That Drives RSV Antiviral Efficacy","authors":"J. Cody Herron, Kathryn G. Link, Natalie Meacham, Negar Niki Alami, Daniel W. Brookes, Elaine Thomas, Jan Adams, Sima S. Toussi, Ryan Franke, Cynthia J. Musante, Isabel Najera, Richard Allen, Britton Boras","doi":"10.1002/psp4.70308","DOIUrl":"https://doi.org/10.1002/psp4.70308","url":null,"abstract":"Respiratory syncytial virus (RSV) infection can result in a range of disease severity from mild upper respiratory tract symptoms to lower respiratory tract illness, with clinical manifestations dependent on age, co-morbidities, and immune status. At present, there is no antiviral approved for adults with RSV disease, and only one treatment option with limited utilization is available for pediatric patients. While several RSV antiviral candidates have demonstrated virological efficacy in healthy adult challenge studies, to date they have subsequently failed to show efficacy in patients. Mechanistically understanding the translation of efficacy from preclinical to challenge to patient studies is essential in addressing this unmet medical need in children and adults with RSV disease. This work uses a quantitative systems pharmacology (QSP) approach to mathematically represent the underlying pathophysiology of RSV infection to simulate and predict the effects of investigational direct acting RSV antiviral therapeutics. We developed virtual populations that capture viral load dynamics in viral challenge studies, where the participants are healthy adult volunteers, upon treatment with the F-protein inhibitor sisunatovir and the N-protein inhibitor zelicapavir. Due to underlying differences in dynamics between key populations, we also developed a pediatric patient virtual population to match the results from the zelicapavir pediatric Phase 2 study. This work projects that for a minimum coverage > 1× free EC90 at Cmin, a treatment window of 5 to 7 days post-symptom onset is feasible for observing virological efficacy in pediatric patients and may lead to potential success in a phase 3 pivotal RSV antiviral drug trial.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 8","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70308","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148647736","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}

引用次数: 0

Refinement of Operating Characteristics for Model-Based TGI Metrics Decision Support to Ungate a Pivotal Trial in Oncology 基于模型的TGI指标决策支持的操作特性改进，以启动肿瘤的关键试验

IF 2.8 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2026-07-30 DOI: 10.1002/psp4.70304

Mathilde Marchand, Kenta Yoshida, Antonio Gonçalves, Rene Bruno, Chunze Li, Jin Y. Jin, Pascal Chanu

引用次数: 0

IF 2.8 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2026-07-29 DOI: 10.1002/psp4.70310

Marianela Chavarría-Rojas, Mub Murshed, Marianela Lorier, Nikoletta Fotaki, Manuel Ibarra

{"title":"Sex-Related Differences in Physiologically-Based Biopharmaceutics Modeling","authors":"Marianela Chavarría-Rojas, Mub Murshed, Marianela Lorier, Nikoletta Fotaki, Manuel Ibarra","doi":"10.1002/psp4.70310","DOIUrl":"https://doi.org/10.1002/psp4.70310","url":null,"abstract":"Physiologically based pharmacokinetic (PBPK) and physiologically-based biopharmaceutics (PBBM) modeling are valuable tools in drug development, allowing mechanistic predictions of drug absorption and disposition. However, sex-related differences in gastrointestinal physiology are often underrepresented in virtual populations, potentially limiting prediction accuracy. This study aimed to evaluate how sex-related physiological differences are incorporated into commonly used PBPK platforms and to illustrate their impact on pharmacokinetic predictions using ketoprofen as a case study. Three PBPK platforms were systematically reviewed to assess predefined sex-specific gastrointestinal parameters. All three platforms incorporated sex-related differences in general anatomy and physiology but overlooked sex-specific variability in gastrointestinal tract parameters. In addition, three PBBM models of ketoprofen were developed and verified in males and subsequently extrapolated to females using default and refined sex-specific parameters. Under default female settings, the models overpredicted Cmax and underestimated Tmax, resulting in concentration–time profiles that were nearly indistinguishable from those of males. Refining gastrointestinal tract parameters for females population improved prediction performance and better reflected observed sex differences. These findings indicate that current PBPK platforms may require user-defined adjustments to adequately represent sex-specific gastrointestinal physiology and that incorporating such parameters could lead to more representative PBBM applications.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 8","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70310","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616555","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}

引用次数: 0

Post-Marketing Requirements for Anticancer Drugs Approved in Japan, 2001–2024: A Cross-Sectional Analysis 2001-2024年日本批准的抗癌药物上市后要求：横断面分析

IF 2.8 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2026-07-28 DOI: 10.1002/psp4.70309

Aina Tsuno, Hiroe Kitagaki, Hideki Maeda

{"title":"Post-Marketing Requirements for Anticancer Drugs Approved in Japan, 2001–2024: A Cross-Sectional Analysis","authors":"Aina Tsuno, Hiroe Kitagaki, Hideki Maeda","doi":"10.1002/psp4.70309","DOIUrl":"10.1002/psp4.70309","url":null,"abstract":"The accelerated development of innovative anticancer therapies has led to the early approval of an increasing number of drugs. However, clinical evidence available at the time of approval is often limited, necessitating additional post-marketing studies to supplement safety and efficacy data. Therefore, post-marketing requirements (PMRs) have gained greater regulatory importance. Nevertheless, the current landscape of PMRs for anticancer drugs in Japan has not been clearly characterized. In this study, we aimed to characterize the status, content, and trends of PMRs for anticancer drugs approved in Japan. We reviewed publicly available regulatory documents for anticancer drugs approved in Japan between 2001 and 2024 and extracted data on background characteristics and PMRs. Analysis of 471 anticancer agents identified 328 (69.6%) drugs requiring at least one PMR. The temporal trend analysis showed a consistent increase in the number of drugs requiring PMRs after 2011. Among 453 PMR assignments, the development and implementation of a risk management plan (RMP) was the most frequent PMR type (n = 254, 56.1%), followed by post-marketing all-case surveillance (n = 147, 32.5%). Twelve drugs (2.6%) required post-marketing clinical trials, among which one (0.2%) involved dose optimization. In conclusion, our findings show that PMRs for anticancer drugs in Japan are predominantly focused on safety monitoring, and that only one PMR was related to dose optimization. These findings suggest that further consideration may be needed in Japan to strengthen post-approval evidence generation for dose setting and to support more evidence-based dose selection.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 8","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70309","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148599737","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}

引用次数: 0

Correction to “Development of an Agent-Based Model to Investigate Durability of Factor IX Activity in Hemophilia B Patients Treated With Etranacogene Dezaparvovec” 更正“开发一种基于药物的模型来研究经外透原地扎帕洛夫韦治疗的B型血友病患者体内因子IX活性的持久性”。

IF 2.8 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2026-07-27 DOI: 10.1002/psp4.70312

引用次数: 0

DDIapp—A Web-Based Application for Static Drug–Drug Interaction Assessment ddiapp -基于web的静态药物-药物相互作用评估应用。

IF 2.8 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2026-07-25 DOI: 10.1002/psp4.70300

Hirotaka Watase, Sylvie Klieber, Yorgos M. Psarellis, Nikhil Pillai, Panteleimon D. Mavroudis, Saroj Dhakal

{"title":"DDIapp—A Web-Based Application for Static Drug–Drug Interaction Assessment","authors":"Hirotaka Watase, Sylvie Klieber, Yorgos M. Psarellis, Nikhil Pillai, Panteleimon D. Mavroudis, Saroj Dhakal","doi":"10.1002/psp4.70300","DOIUrl":"10.1002/psp4.70300","url":null,"abstract":"In this work we describe the development of a web-based application for static drug–drug interaction (DDI) risk assessment in accordance with the International Council for Harmonization (ICH) M12 guidance. The app was built using the Shiny for Python framework and it employs a modular mathematical modeling structure that incorporates models for the assessment of enzyme inhibition, enzyme induction, and transporter inhibition at intestinal, hepatic, and renal levels. In addition, a “net effect” model estimates the combined impact of inhibition and induction on victim drug exposure, expressed as the area under the curve ratio (AUCR). Matrix-specific approaches for estimating unbound fractions in microsomes and hepatocytes (fu,mic and fu,hep), as indicated in regulatory alignment, are implemented. The application provides a dynamic interface that supports flexible parameter input, real-time evaluation across multiple DDI scenarios, and automated risk categorization using predefined thresholds based on the ICH M12 guidelines, with visual cues to aid risk interpretation. Additional features include integrated equation display for transparency and interpretation, export capabilities to PDF and Excel formats and a glossary with links to guidance documents and resources from major regulatory authorities (FDA, EMA, PMDA and NMPA). This DDIapp is validated against Certara's drug–drug interaction calculator under ICH M12 evaluation conditions. DDIapp offers a user-friendly platform for assessing the risk of pharmacokinetic drug interactions as a perpetrator involving metabolic enzymes and transporters, supporting both scientific research and regulatory decision-making.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 8","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13401703/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148590905","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}

引用次数: 0

Physiologically Based Pharmacokinetic Model of Brigatinib in Healthy Volunteers and Patients With Cancer 布加替尼在健康志愿者和癌症患者体内基于生理的药代动力学模型

IF 2.8 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2026-07-21 DOI: 10.1002/psp4.70302

Yichao Xu, Xinhua Hu, Pengfei Zhao, Lu Wang, Zourong Ruan, Bo Jiang, Honggang Lou

{"title":"Physiologically Based Pharmacokinetic Model of Brigatinib in Healthy Volunteers and Patients With Cancer","authors":"Yichao Xu, Xinhua Hu, Pengfei Zhao, Lu Wang, Zourong Ruan, Bo Jiang, Honggang Lou","doi":"10.1002/psp4.70302","DOIUrl":"https://doi.org/10.1002/psp4.70302","url":null,"abstract":"Brigatinib, an oral ALK inhibitor for metastatic NSCLC, lacks dosing guidance for special populations such as the Chinese. This study developed a physiologically based pharmacokinetic (PBPK) model using European data from patients with hepatic/renal impairment and drug–drug interaction (DDI) studies (itraconazole, rifampin). The model was then applied to predict (1) pharmacokinetics (PK) in the Chinese population; (2) PK in Chinese patients with hepatic/renal impairment; and (3) DDI in Chinese patients. Validated against clinical data, the model successfully predicted brigatinib PK alone and with CYP3A4 modulators. Food simulations showed a slight absorption delay without clinically meaningful exposure reduction. In hepatic/renal impairment, the model accurately predicted exposure changes across severity groups (fold error < 2). Extending the European-validated model to Chinese populations, the findings demonstrate reliable predictions of brigatinib PK in Chinese individuals as well as in Chinese patients with hepatic or renal impairment.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 8","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70302","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148533910","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}

引用次数: 0

Using Stochastic Simulation-Estimation and Automated Model Development to Assess Power and Accuracy for Covariate Identification 使用随机模拟-估计和自动模型开发来评估协变量识别的能力和准确性

IF 2.8 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2026-07-21 DOI: 10.1002/psp4.70299

Ya-Han Hsu, Bárbara Costa, Nuno Vale, Thomas P. C. Dorlo, Mats O. Karlsson

{"title":"Using Stochastic Simulation-Estimation and Automated Model Development to Assess Power and Accuracy for Covariate Identification","authors":"Ya-Han Hsu, Bárbara Costa, Nuno Vale, Thomas P. C. Dorlo, Mats O. Karlsson","doi":"10.1002/psp4.70299","DOIUrl":"https://doi.org/10.1002/psp4.70299","url":null,"abstract":"When study designs are evaluated using clinical trial simulations for their ability to identify covariate effects in population pharmacokinetic (PopPK) modeling, it is typically assumed that the true model will be known at the data analysis stage. In this study, this was compared with the more realistic assumption that the PopPK model needs to be built on the data generated by the planned study. Three approaches were compared: (i) stochastic simulation and re-estimation (SSE) with the simulation model, (ii) automated model development (AMD) with exploratory covariate search (AMD-exploratory), and (iii) AMD forcing the covariate effect into the model from the start and reevaluating it in the end (AMD-structural). With a simulated covariate effect (a hypothetical pregnancy effect on clearance), we assessed (i) the type 1 error (T1E) and the power of covariate identification and (ii) covariate parameter accuracy. The T1E rate was controlled in SSE and AMD-exploratory but 20% inflated for AMD-structural. The power of covariate identification in rich, medium, and sparse designs was (i) 99%, 100%, and 79% in SSE, (ii) 74%, 72%, and 41% in AMD-exploratory, and (iii) 92%, 93%, and 80% in AMD-structural. Sparse designs amplified power differences between strategies, with AMD-exploratory often selecting alternative or no covariates. The rRMSE of covariate parameter estimates was lowest in SSE (27%, 22%, and 42%), followed by AMD-exploratory (34%, 26%, and 49%) and then AMD-structural (42%, 47%, and 60%). SSE provides optimistic power estimates as model building is data-driven, while AMD-based approaches incorporate model uncertainty and reflect real-world analysis conditions.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 8","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70299","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148533907","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}

引用次数: 0