Vaccine Peptide Selection Through Manufacturing Simulation
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Solution Overview
Problem
Selecting the optimal combination of peptides for vaccine production that are both manufacturable and effective in triggering an immune response is challenging, as existing methods are inefficient and prone to errors.
Innovation Solution
A system and method for intelligently selecting peptides using a statistical model that optimizes manufacturability and immunogenicity, employing simulation algorithms to refine peptide selection based on criteria such as peptide length, target mutations, and immune response, reducing human error and enhancing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual methods are used to select peptides, then flexibility and adaptability are maintained, but efficiency and reliability are reduced
Solution Approach 1:
The patent replaces manual mechanical selection processes with an automated statistical model pipeline that uses computer algorithms to evaluate and select peptides based on quantitative criteria, thereby improving efficiency while managing complexity through systematic automation
Solution Approach 2:
The system transforms subjective manual evaluation into objective quantitative parameters including immunogenicity scores, manufacturability scores, and statistical significance thresholds, enabling consistent and reproducible peptide selection through measurable criteria
2Measurement precision
If comprehensive peptide evaluation is performed, then selection accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary filtering and pre-evaluation of peptides using statistical models before final selection, systematically eliminating unsuitable candidates early in the process to reduce overall processing time while maintaining high accuracy through structured multi-stage assessment
Solution Approach 2:
The statistical model pipeline operates continuously to evaluate multiple peptides simultaneously using parallel processing, maintaining consistent evaluation standards throughout the entire selection process without interruption or manual intervention delays
3Reliability
If multiple peptides are selected to ensure coverage, then effectiveness is improved, but manufacturability decreases
Solution Approach 1:
The system uses weighted scoring parameters that balance immunogenicity and manufacturability, assigning different weights to each criterion based on project requirements to identify optimal peptide combinations that satisfy both effectiveness and manufacturing feasibility
Solution Approach 2:
The statistical model incorporates feedback loops that continuously refine peptide selections by evaluating manufacturing feasibility against immunogenicity targets, adjusting selections based on real-time analysis of both criteria to achieve optimal balance
Data Source
AI summary
Methods and systems are disclosed for selecting a set of peptides from a plurality of peptides for producing a drug product. A request may be received to produce a vaccine that meets specific requirements, including the desired immune response and the inclusion of certain types of peptides. The system may rank peptides using one or more metrics that factor in immunogenicity and/or manufacturability. Based on the ranking, the system may select a group of peptides for inclusion in a manufacturing simulation process, which returns a set of peptides that are predicted to be successfully manufactured. The system refines its selection to a subset of manufacturable peptides based on specific criteria. This iterative process continues until predefined conditions are met, such as the convergence of the simulation results. Based on these results, the system identifies an optimal or near-optimal set of peptides that can be used for effective drug production.