T-Cell Target Discovery Through Cross-Reactivity Screening
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Solution Overview
Problem
Current engineered T-cell therapies face challenges in identifying optimal disease targets due to cross-reactivity issues and patient-specific epitopes, leading to severe side effects and regulatory hurdles.
Innovation Solution
A method for identifying novel epitopes by sequencing immune cells from subjects with immune-mediated conditions, engineering soluble TCRs, and screening them against peptide libraries to validate epitopes with improved affinity and reduced cross-reactivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the affinity of TCRs in engineered T-cells is increased to known disease antigens, then the effectiveness of the therapy is improved, but the affinity to non-disease-specific peptides also increases, resulting in severe side effects
Solution Approach 1:
The patent segments the epitope identification process into multiple stages: computational prediction of candidate epitopes, in silico screening to filter cross-reactivity risks, and experimental validation. This segmentation allows systematic evaluation of each epitope's specificity before therapeutic application, resolving the contradiction between affinity enhancement and cross-reactivity reduction.
Solution Approach 2:
The patent performs preliminary computational analysis and in silico screening of epitopes before experimental validation and clinical application. By predicting and filtering potential cross-reactivity issues in advance through bioinformatics approaches, the method prevents harmful cross-reactivity while maintaining therapeutic effectiveness.
2Reliability
If engineered T-cell therapies are developed to target disease antigens, then treatment efficacy is improved, but dangerous cross-reactivity halts development even where cross-reactivity was not predicted
Solution Approach 1:
The patent implements a feedback mechanism where computational predictions of cross-reactivity are continuously refined based on experimental validation results. The in silico screening system learns from validated epitopes and adjusts prediction algorithms to better identify potential cross-reactivity issues, preventing future development halts.
Solution Approach 2:
The patent performs comprehensive preliminary computational screening and cross-reactivity assessment before experimental validation. By using in silico methods to predict and filter epitopes with potential cross-reactivity risks in advance, the system prevents unexpected cross-reactivity issues from halting development later in the process.
3Measurement precision
If TCR affinity to disease antigens is increased, then disease target recognition is improved, but affinity to non-disease-specific peptides increases causing intolerable side effects
Solution Approach 1:
The patent applies local quality assessment by evaluating the specificity of each epitope's binding characteristics separately. The in silico screening analyzes local sequence features and binding patterns to identify epitopes that maintain high affinity for disease targets while showing low affinity for non-disease peptides, resolving the specificity-generalizability contradiction.
Solution Approach 2:
The patent changes the parameters used for epitope evaluation by incorporating computational predictions of cross-reactivity alongside binding affinity measurements. This multi-parameter approach identifies epitopes with optimal balance between disease target recognition and cross-reactivity minimization.
Data Source
AI summary
The present invention provides methods and systems that identify novel antigens that bind to a particular T cell receptor and also validate the immunogenicity of the potential antigens to activate the TCR. The methods allow for development of an exhaustively profile of on-target and off-target reactivity of novel antigens.