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Vaccine Detail
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PDTRP-MUC1-Calix[4,8]arene-Pam?Cys-Ser Self-Adjuvant Vaccines (Tetravalent and Octavalent) |
| Vaccine Information |
- Vaccine Name: PDTRP-MUC1-Calix[4,8]arene-Pam?Cys-Ser Self-Adjuvant Vaccines (Tetravalent and Octavalent)
- Target Pathogen: Cancer
- Target Disease: Cancer
- Type: Conjugate vaccine
- Status: Research
- Host Species for Licensed Use: Mouse
- Antigen: MUC1 [Ref6855:Geraci et al., 2013, (Spadaro et al., 2020)
- Immunization Route: Intraperitoneal injection (i.p.)
- Description: Synthetic multiepitope conjugate vaccines consisting of four (tetravalent, calix[4]arene scaffold) or eight (octavalent, calix[8]arene scaffold) copies of the PDTRP immunodominant B-cell epitope from the MUC1 core sequence, covalently conjugated to calixarene platforms with integrated Pam?Cys-Ser (P3CS) TLR2 agonist as a self-adjuvant. The vaccines target tumor-associated MUC1, a membrane-bound glycoprotein overexpressed in aberrant or underglycosylated form in breast, ovarian, prostate, and other epithelial cancers. PDTRP represents the exposed naked peptide core in underglycosylated cancer-associated MUC1. Vaccination stimulates anti-MUC1 IgG antibody production through multivalent B-cell receptor cross-linking. The octavalent construct produces 2-fold higher anti-PDTRP IgG titers compared to the tetravalent construct (5120 vs 2560, p<0.05), attributed to greater conformational flexibility, extended epitope presentation, and superior electrostatic complementarity. Controls lacking PDTRP produced no anti-MUC1 IgG, confirming epitope essentiality. Induced antibodies recognize native MUC1 epitopes on MCF-7 human breast cancer cells, validating functional antibody production [Ref6855:Geraci et al., 2013, (Spadaro et al., 2020).
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| Host Response |
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| References |
Spadaro et al., 2020: Spadaro A, Basile L, Pappalardo M, Bonaccorso C, Rao M, Ronsisvalle S, Granata G, Guccione S. Quantum Chemical and Molecular Dynamics Studies of MUC1 Calix[4,8]arene Scaffold Based Anticancer Vaccine Candidates. Journal of chemical information and modeling. 2020; 60(10); 5162-5171. [PubMed: 32818373].
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