An in-vivo Intraoral Defect Model for Assessing the Use of P11-4 Self-Assembling Peptide in Periodontal Regeneration
Basmah El-Sayed1,2, Robert Philip Wynn Davies1, Rehab R El-Zehery2
1Division of Oral Biology, School of Dentistry, University of Leeds, Leeds, United Kingdom.
Abstract:
Periodontal disease is one of the most common diseases worldwide. It has a significant impact on oral health and subsequently the individual's quality of life. However, optimal regeneration of periodontal tissues, using current treatments, has yet to be achieved. Peptide self-assembly has provided a step-change in nanobiotechnology and regenerative medicine fields. Our aim was to investigate the effects of a self-assembling peptide (SAP; P11-4) on periodontal regeneration in a preclinical model. Twenty-six bilateral maxillary critical-sized periodontal defects were created surgically in 13 rats. Defects on one side of the mouth were filled with P11-4 hydrogel; the contra-lateral defect was untreated (control). Rats were sacrificed immediately post-surgery (time 0) and after 2 and 4 weeks. Retrieved maxillae were processed for histological, immunohistochemical, and histomorphometric assessments. The results of histological analysis showed greater organization of periodontal fibers in defects treated with P11-4, at both time points, when compared to untreated defects. Histomorphometry showed that treated defects had both a significant increase in functional periodontal ligament length and a reduction in epithelial down growth after 4 weeks. At 2 weeks, treated defects showed a significant increase in expression of osteocalcin and osteoprotegerin as judged by immunohistochemistry. Also, a significantly higher osteoprotegerin/RANKL ratio was shown in treated defects. In conclusion, the results demonstrated enhanced regeneration of periodontal tissues when SAP P11-4 was used to fill periodontal defects in rats. The findings of this study suggest that SAP P11-4 is a promising novel candidate for periodontal regenerative therapy. Further investigations are required for optimization before clinical use.
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