Prediction the clinical EPR effect of nanoparticles in patient-derived xenograft models
- 주제(키워드) EPR effect , Nanoparticles , Patient-derived xenograft model , Tumor heterogeneity , Tumor microenvironment
- 등재 SCIE, SCOPUS
- 발행기관 Elsevier B.V.
- 발행년도 2022
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000194509
- 본문언어 영어
- Published As https://doi.org/10.1016/j.jconrel.2022.09.007
- PubMed https://pubmed.ncbi.nlm.nih.gov/36089170
초록/요약
Many preclinically tested nanoparticles in existing animal models fail to be directly translated into clinical applications because of their poor resemblance to human cancer. Herein, the enhanced permeation and retention (EPR) effect of glycol chitosan nanoparticles (CNPs) in different tumor microenvironments (TMEs) was compared using different pancreatic tumor models, including pancreatic cancer cell line (BxPC3), patient-derived cancer cell (PDC), and patient-derived xenograft (PDX) models. CNPs were intravenously injected into different tumor models, and their accumulation efficiency was evaluated using non-invasive near-infrared fluorescence (NIRF) imaging. In particular, differences in angiogenic vessel density, collagen matrix, and hyaluronic acid content in tumor tissues of the BxPC3, PDC, and PDX models greatly affected the tumor-targeting efficiency of CNPs. In addition, different PDX models were established using different tumor tissues of patients to predict the clinical EPR effect of CNPs in inter-patient TMEs, wherein the gene expression levels of PECAM1, COL4A1, and HAS1 in human tumor tissues were observed to be closely related to the EPR effect of CNPs in PDX models. The results suggested that the PDX models could mimic inter-patient TMEs with different blood vessel structures and extracellular matrix (ECM) content that critically affect the tumor-targeting ability of CNPs in different pancreatic PDX models. This study provides a better understanding of the heterogeneity and complexity of inter-patient TMEs that can predict the response of various nanoparticles in individual tumors for personalized cancer therapy. © 2022 Elsevier B.V.
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