Computational Model Design for Chimeric Antigen Receptor Sequences
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Solution Overview
Problem
The development of chimeric antigen receptors (CARs) for cancer therapy is hindered by a resource-intensive and labor-intensive process that limits the number of candidates that can be progressed, leading to a high risk of failure due to the inability to predict functional outcomes based on antibody characteristics alone, and existing machine learning approaches fail to account for the complexity of CAR functionality and stability.
Innovation Solution
A method using computational models and machine learning to design CARs by defining training sets, objectives, and features to predict desirable CAR sequences, incorporating antigen binding domains, hinge regions, transmembrane domains, and intracellular signaling domains, allowing for the generation of novel CAR sequences with improved functionality and stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional manual CAR development process is used, then CAR functionality can be assessed, but the process is resource-intensive and labor-intensive, limiting the number of candidates that can be progressed
Solution Approach 1:
The patent applies preliminary action by using machine learning models to predict CAR functionality and stability before experimental testing. The computational models forecast which CAR candidates are most likely to succeed, allowing researchers to prioritize a larger number of candidates for progression without proportionally increasing resource expenditure. This predictive approach enables more candidates to be advanced through the development pipeline while maintaining assessment reliability.
2Reliability
If more CAR candidates are progressed through manual screening, then the chance of success increases, but the resource intensity and time required increase proportionally
Solution Approach 1:
The patent replaces the mechanical manual screening process with computational machine learning models. Instead of physically testing each CAR candidate through labor-intensive experimental procedures, the system uses trained ML models to predict functionality and stability outcomes. This substitution dramatically reduces the time required to evaluate multiple candidates while maintaining or improving the reliability of success prediction, as the models can assess numerous candidates simultaneously without additional time costs.
3Ease of operation
If antibody characteristics are used to predict CAR functionality, then the selection process is simplified, but the prediction accuracy is insufficient due to not accounting for CAR complexity
Solution Approach 1:
The patent applies the composite materials principle by integrating multiple data sources and model types to create a comprehensive prediction system. The machine learning models incorporate not only antibody characteristics but also CAR-specific features including stability predictions, structural information, and functional data. This composite approach maintains the simplicity of using computational predictions while significantly improving accuracy by accounting for the full complexity of CAR functionality through multiple integrated prediction factors.
4Measurement precision
If extensive experimental screening is performed to identify successful CARs, then prediction accuracy improves, but the resource intensity and cost increase significantly
Solution Approach 1:
The patent uses copying by creating computational models that replicate the functionality assessment process without requiring physical experimental testing for each candidate. The machine learning models are trained on existing experimental data and then used to generate predictions for new CAR candidates, effectively copying the knowledge gained from previous experiments. This approach maintains high prediction accuracy by leveraging trained model predictions while avoiding the resource-intensive repetition of extensive experimental screening for every new candidate.
Data Source
AI summary
A method for designing a chimeric antigen receptor (CAR), comprising: a) defining a training set of CAR sequences wherein each training CAR is associated with one or more properties; b) defining one or more objectives, each of the one or more objectives defining a desired property of a CAR; c) training a computational model using the training set of the one or more training CAR sequences to provide a trained computational model; d) using the computational model to provide at least one output CAR sequence, and wherein the at least one output CAR sequence is determined based on the one or more objectives. Methods of training a computational model for designing a chimeric antigen receptor (CAR), the trained model provided by such method, along with a CAR sequence output, the CAR encoded by the output CAR sequence and a cell expressing said CAR are also provided.