Standardized and axially vascularized calcium phosphate-based implants for segmental mandibular defects: A promising proof of concept

Arnaud Paré (1) (2), Baptiste Charbonnier (1) (3), Joëlle Veziers (1), Caroline Vignes (1), Maeva Dutilleul (1), Gonzague De Pinieux (4), Boris Laure (2), Adeline Bossard (5), Annaëlle Saucet-Zerbib (5), Gwenola Touzot-Jourde (1) (5), Pierre Weiss (1), Pierre Corre (1) (6), Olivier Gauthier (1) (5), David Marchat (3)
Acta Biomaterialia. Volume 154 (December 2022). DOI: https://doi.org/10.1016/j.actbio.2022.09.071

Keywords

Calcium phosphates, additive manufacturing, segmental mandibulectomy, bone regeneration, surgical model

Abstract

The reconstruction of massive segmental mandibular bone defects (SMDs) remains challenging even today; the current gold standard in human clinics being vascularized bone transplantation (VBT). As alternative to this onerous approach, bone tissue engineering strategies have been widely investigated. However, they displayed limited clinical success, particularly in failing to address the essential problem of quick vascularization of the implant. Although routinely used in clinics, the insertion of intrinsic vascularization in bioengineered constructs for the rapid formation of a feeding angiosome remains uncommon. In a clinically relevant model (sheep), a custom calcium phosphate-based bioceramic soaked with autologous bone marrow and perfused by an arteriovenous loop was tested to regenerate a massive SMD and was compared to VBT (clinical standard). Animals did not support well the VBT treatment, and the study was aborted 2 weeks after surgery due to ethical and animal welfare considerations. SMD regeneration was successful with the custom vascularized bone construct. Implants were well osseointegrated and vascularized after only 3 months of implantation and totally entrapped in lamellar bone after 12 months; a healthy yellow bone marrow filled the remaining space.

How Our Software Was Used

Quantitative analyses were performed with Dragonfly using a deep learning module for realistic and accurate quantification of bone tissue. Segmentations were exported as meshes in the STL file format for comparison with the original CAD designs.

Author Affiliation

(1) INSERM, U 1229, Laboratory of Regenerative Medicine and Skeleton, RMeS, Nantes Université, 1 Place Alexis Ricordeau, Nantes 44042, France
(2) Department of Maxillofacial and Plastic surgery, Burn Unit, University Hospital of Tours, Trousseau Hospital, Avenue de la République, Chambray lès Tours 37170, France
(3) Mines Saint-Étienne, Univ Jean Monnet, INSERM, U 1059 Sainbiose, 42023, Saint-Étienne, France
(4) Department of Pathology, University Hospital of Tours, Trousseau Hospital, Avenue de la République, Chambray lès Tours 37170, France
(5) ONIRIS Nantes-Atlantic College of Veterinary Medicine, Research Center of Preclinical Invesitagtion (CRIP), Site de la Chantrerie, 101 route de Gachet, Nantes 44307, France
(6) Clinique de Stomatologie et Chirurgie Maxillo-Faciale, Nantes University Hospital, 1 Place Alexis Ricordeau, Nantes 44042, France