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Data from Doxorubicin-Loaded Polymeric Meshes Prevent Local Recurrence after Sarcoma Resection While Avoiding Cardiotoxicity

Posted on 2023-03-31 - 05:44
Abstract

Surgery is the only potentially curative treatment for localized soft-tissue sarcomas. However, for sarcomas arising in the retroperitoneum, locoregional recurrence rates are 35% to 59% despite resection. Doxorubicin (DOX) is the standard first-line systemic chemotherapy for advanced soft-tissue sarcoma, yet its intravenous administration yields limited clinical efficacy and results in dose-limiting cardiotoxicity. We report the fabrication and optimization of a novel electrospun poly(caprolactone) (PCL) surgical mesh coated with layers of a hydrophobic polymer (poly(glycerol monostearate-co-caprolactone), PGC-C18), which delivers DOX directly to the operative bed following sarcoma resection. In xenograft models of liposarcoma and chondrosarcoma, DOX-loaded meshes (DoM) increased overall survival 4-fold compared with systemically administered DOX and prevented local recurrence in all but one animal. Importantly, mice implanted with DoMs exhibited preserved cardiac function, whereas mice receiving an equivalent dose systemically displayed a 23% decrease from baseline in both cardiac output and ejection fraction 20 days after administration. Collectively, this work demonstrates a feasible therapeutic approach to simultaneously prevent post-surgical tumor recurrence and minimize cardiotoxicity in soft-tissue sarcoma.

Significance:

A proof-of-principle study in animal models shows that a novel local drug delivery approach can prevent tumor recurrence as well as drug-related adverse events following surgical resection of soft-tissue sarcomas.

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FUNDING

National Institutes of Health (NIH)

Thoracic Surgery Foundation (TSF)

Society of University Surgeons Foundation (SUSF)

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Cancer Research

AUTHORS (11)

  • Eric M. Bressler
    Ngoc-Quynh Chu
    Robert C. Sabatelle
    David A. Mahvi
    Jenny T. Korunes-Miller
    Fumiaki Nagashima
    Fumito Ichinose
    Rong Liu
    Mark W. Grinstaff
    Yolonda L. Colson
    Chandrajit P. Raut

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