DOI: 10.3390/transplantology7040022 ISSN: 2673-3943

Evaluation of Murine Orthotopic Tracheal Transplantation as an In Vivo Platform for Antifibrotic Drug Testing

Ekroop Dhillon, Amer Ali Abd El-Hafeez, Ajaypal Sahota, Aijaz Ahmad, Joe L. Hsu

Background/objectives: Transplant-related pulmonary fibrosis (TPF) occurs after allogeneic hematopoietic cell transplant and after lung transplantation. Effective therapies that prevent or reverse established transplant-related airway fibrosis remain limited. Human cell-based models are valuable for initial drug screening, but complementary in vivo models are needed to evaluate treatment responses in the presence of an allogeneic graft, an intact recipient immune system, systemic drug exposure, and multicellular tissue remodeling. This study evaluated murine orthotopic tracheal transplantation (OTT) as a practical in vivo platform for antifibrotic drug testing, using pirfenidone as a benchmark antifibrotic intervention. Methods: C57BL/6J mice underwent syngeneic or allogeneic OTT and received vehicle or pirfenidone (250 mg/kg daily) from day 21 to 30 posttransplant. Explanted grafts were analyzed for the expression of alpha-smooth muscle actin (α-SMA), fibronectin 1 (Fn1), collagen type I alpha 1 chain (Col1a1), and tenascin C (Tnc) by quantitative reverse-transcription polymerase chain reaction (qRT-PCR). Total collagen content was assessed by measuring hydroxyproline levels using a colorimetric hydroxyproline assay. Results: Pirfenidone normalized the expression of α-SMA, Fn1, Col1a1 and Tnc to levels comparable to those of syngeneic controls and significantly reduced hydroxyproline (p < 0.0001). Conclusions: Using pirfenidone as a benchmark antifibrotic, the murine OTT model detected concordant treatment-associated reductions in profibrotic gene expression and collagen content. These findings support the feasibility and pharmacodynamic responsiveness of OTT as an in vivo platform for secondary-stage antifibrotic drug testing. Additional investigational therapies will be required to establish the broader generalizability and predictive performance of the platform.

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