Off-target Prediction for Antigen-recognition Molecules
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Solution Overview
Problem
Current methods for engineering antigen-recognition molecules to target cancer cells often result in severe side effects due to off-target interactions with native cells, leading to adverse effects and resource inefficiencies in clinical trials.
Innovation Solution
A computational system is configured to predict amino acid positions involved in interactions with antigen-recognition molecules within MHC-peptide complexes, allowing for the identification of off-target peptides and estimation of their expression in normal tissues, thereby filtering out high-risk peptides and optimizing target selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If antigen-recognition molecules are engineered to target cancer cells, then treatment effectiveness is improved, but off-target interactions with native cells increase causing severe side effects
Solution Approach 1:
The computational method performs preliminary prediction of off-target peptides and their expression in normal tissues before clinical trials. By identifying high-risk peptides in advance and filtering them out during target selection, the system prevents off-target toxicity before it occurs in patients, rather than detecting it during clinical trials.
Solution Approach 2:
The computational prediction system acts as an intermediary between target selection and clinical trials. It provides risk assessment data that mediates the decision-making process, allowing researchers to evaluate and compare potential targets based on predicted off-target interactions before committing to clinical development.
2Reliability
If comprehensive off-target screening is performed, then treatment safety is improved, but time and resource consumption increase
Solution Approach 1:
The patent replaces extensive wet-lab screening and clinical trial testing with a computational prediction system. Instead of performing comprehensive experimental screening of all possible off-target interactions, the system uses in silico methods to predict binding affinities and identify high-risk peptides, dramatically reducing the time and resources required for safety assessment.
Solution Approach 2:
The computational method performs preliminary risk assessment during the target selection phase, before substantial resource investment in development. By identifying and filtering high-risk peptides early in the process, the system prevents wasted time and resources on targets likely to cause off-target toxicity.
3Reliability
If extensive clinical trials are conducted to identify side effects, then treatment safety is improved, but development costs and time expenditure increase significantly
Solution Approach 1:
The system performs preliminary safety assessment during target selection by predicting off-target peptide interactions and their expression in normal tissues. This early risk evaluation prevents progression of high-risk targets to expensive clinical trials, avoiding waste of development resources on targets likely to cause severe side effects.
Solution Approach 2:
The computational method provides feedback on target risk profiles by quantifying predicted off-target interactions. This feedback mechanism allows researchers to compare multiple potential targets and select those with favorable safety profiles before investing in development, optimizing resource allocation.
Data Source
AI summary
Computational systems and methods for predicting amino acid position(s) within a target peptide presented in a complex with a major histocompatibility complex (MHC) molecule (MHC-target peptide complex), the amino acid position(s) being involved in interacting with an antigen-recognition molecule that recognizes said MHC-target peptide complex, are presented herein. Computational systems and methods for estimating a number of off-target peptide(s) for an antigen-recognition molecule that recognizes a target peptide presented in a complex with a major histocompatibility complex (MHC) molecule (MHC-target peptide complex) is presented herein. Computational systems and methods for ranking potential target peptides to mitigate off-target toxicity are presented herein. Such computational systems and methods can streamline development of effective, well tolerated antigen-recognition molecules to treat diseases.