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      Regulating cancer associated fibroblasts with losartan-loaded injectable peptide hydrogel to potentiate chemotherapy in inhibiting growth and lung metastasis of triple negative breast cancer

      , , , , , , , , ,
      Biomaterials
      Elsevier BV

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          Abstract

          Preoperative chemotherapy is effective in improving the prognosis of patients, but its efficacy is impeded by cancer associated fibroblasts (CAFs) that enhance the survival, growth, and metastasis of cancer cells. To inhibit the activity of CAFs, prolonged and localized drug exposure is necessary. Here, we report on the rational design, screening, and evaluation of an injectable peptide hydrogel as a local losartan depot aiming to inhibit CAFs and potentiate chemotherapy. We synthesized a set of peptide derivatives and found that C16-GNNQQNYKD-OH (C16-N) surpassed the others in hydrogel formation and drug encapsulation, due to its flexible hydrocarbon tail and interpeptide hydrogen bonding that allowed supramolecular self-assembly into long filaments with hydrophobic cores. C16-N co-assembled with losartan to form hydrogel from which losartan was sustainably released over 9 days. After intratumoral injection, the hydrogel could be retained in the tumor for more than 9 days, significantly inhibited the CAFs and collagen synthesis in orthotopic 4T1 tumors, and enhanced the efficacy of PEGylated doxorubicin-loaded liposomes (Dox-L) in inhibiting the tumor growth (64% vs. Dox-L alone) and lung metastasis (80% vs. Dox-L alone). These results provide important guiding principles for the rational design of injectable peptide hydrogels aiming to regulate CAFs and improve chemotherapy.

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          Author and article information

          Journal
          Biomaterials
          Biomaterials
          Elsevier BV
          01429612
          November 2017
          November 2017
          : 144
          : 60-72
          Article
          10.1016/j.biomaterials.2017.08.009
          28823844
          c23bcd20-3d22-4db6-a0cf-753c4c4549e0
          © 2017

          https://www.elsevier.com/tdm/userlicense/1.0/

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