Immobilization of murine anti-BMP-2 monoclonal antibody on various biomaterials for bone tissue engineering.
Sahar AnsariMarcelo O FreireEun-Kyoung PangAlaa I AbdelhamidMohammad AlmohaimeedHomayoun H ZadehPublished in: BioMed research international (2014)
Biomaterials are widely used as scaffolds for tissue engineering. We have developed a strategy for bone tissue engineering that entails application of immobilized anti-BMP-2 monoclonal antibodies (mAbs) to capture endogenous BMPs in vivo and promote antibody-mediated osseous regeneration (AMOR). The purpose of the current study was to compare the efficacy of immobilization of a specific murine anti-BMP-2 mAb on three different types of biomaterials and to evaluate their suitability as scaffolds for AMOR. Anti-BMP-2 mAb or isotype control mAb was immobilized on titanium (Ti) microbeads, alginate hydrogel, and ACS. The treated biomaterials were surgically implanted in rat critical-sized calvarial defects. After 8 weeks, de novo bone formation was assessed using micro-CT and histomorphometric analyses. Results showed de novo bone regeneration with all three scaffolds with immobilized anti-BMP-2 mAb, but not isotype control mAb. Ti microbeads showed the highest volume of bone regeneration, followed by ACS. Alginate showed the lowest volume of bone. Localization of BMP-2, -4, and -7 antigens was detected on all 3 scaffolds with immobilized anti-BMP-2 mAb implanted in calvarial defects. Altogether, these data suggested a potential mechanism for bone regeneration through entrapment of endogenous BMP-2, -4, and -7 proteins leading to bone formation using different types of scaffolds via AMOR.
Keyphrases
- bone regeneration
- tissue engineering
- monoclonal antibody
- acute coronary syndrome
- stem cells
- magnetic nanoparticles
- ionic liquid
- oxidative stress
- magnetic resonance imaging
- risk assessment
- bone marrow
- immune response
- big data
- electronic health record
- machine learning
- artificial intelligence
- mass spectrometry
- deep learning
- climate change
- image quality