Evaluating neoantigen-vaccine responses through mechanistic and model-based frameworks.

Area of Science:

• Immunology
• Pharmacology
• Computational Biology

Background:

• Therapeutic cancer vaccines aim to activate tumor-specific T-cells for tumor reduction.
• Integrating complex preclinical data for vaccine development presents significant challenges.
• Existing approaches require better methods for preclinical data analysis and translation.

Purpose of the Study:

• To present a model-based framework for integrating diverse preclinical data in cancer vaccine research.
• To compare synthetic long peptide vaccines with adjuvant stimuli against a vaccine-drug conjugate approach.
• To evaluate the efficacy of Adaptable Drug Affinity Conjugate technology in preclinical cancer models.

Main Methods:

• Developed a semi-mechanistic modeling framework integrating pharmacokinetic, antigen-presenting cell uptake, T-cell response, and tumor growth sub-models.
• Utilized Adaptable Drug Affinity Conjugate technology for modular conjugate formation via high-affinity binding.
• Compared vaccine strategies in TC-1 and MC38 preclinical tumor models.

Main Results:

• Model-based simulations demonstrated the critical role of affinity conjugation in enhancing pharmacokinetics and vaccine efficacy.
• Effector T-cells were identified as the primary mediators of tumor shrinkage.
• Dose-dependent effects of the antibody conjugate were quantified, particularly in the immune-responsive MC38 model.

Conclusions:

• The developed framework successfully links dosing strategies to tumor dynamics, providing actionable insights.
• Model-based simulations highlight the advantages of affinity conjugation for improved vaccine performance.
• This approach supports rational optimization of cancer vaccines and aids translational decision-making.