Neoantigen Vaccine Sequence Selection via In Silico Simulation

The patent applies preliminary action by simulating cancer cell presentation of candidate neoantigen sequences before actual vaccine administration. The method predicts which sequences will be presented on MHC molecules and will elicit immune responses, allowing selection of optimal sequences in advance. This preliminary simulation prevents selection of sequences that could cause off-target responses while ensuring selection of sequences that will effectively target cancer cells.

The patent uses computational copying by creating in silico models of cancer cells that replicate the biological processes of antigen presentation. Instead of testing actual vaccine candidates on real cells (which would be time-consuming and risky), the method creates virtual copies that simulate MHC binding and T cell recognition, allowing safe prediction of immune responses before clinical application.

The patent applies universality by developing a computational method that can evaluate multiple candidate neoantigen sequences against multiple MHC alleles simultaneously. The system is designed to handle diverse cancer cell types and mutation profiles through a unified simulation framework, allowing the same methodology to be applied across different patients and cancer types without requiring separate approaches for each case.

The patent applies segmentation by breaking down the complex immune response prediction into discrete, manageable components: individual MHC binding predictions, individual T cell recognition predictions, and individual cancer cell simulation. By segmenting the overall prediction task into these smaller units, the system can systematically evaluate multiple candidates across multiple cell types while maintaining computational tractability.