AI Sequence Design System for CAR T-Cell Nucleotide Optimization
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Solution Overview
Problem
Current methods for designing cell nucleotide sequences in CAR T-cell therapy are time-consuming, expensive, and lack scalability due to human and laboratory involvement, leading to inefficiencies and higher failure rates in clinical trials.
Innovation Solution
A computer-implemented method using an AI-based prediction model that receives historical data, predicts cell nucleotide sequences with specific characteristics, identifies feasible sequences, and generates a ranked list to optimize cell nucleotide sequence design for targeted antigens, reducing the number of possible sequences and minimizing adverse immune responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional in-vivo and experimental approaches are used to identify optimal cell nucleotide sequences, then accuracy of sequence selection can be maintained through human expertise and lab validation, but the process becomes time-consuming and not scalable
Solution Approach 1:
The AI model performs preliminary analysis and prediction of optimal nucleotide sequences before experimental validation. By pre-screening and ranking candidate sequences using machine learning predictions of binding affinity and off-target effects, the system prepares a prioritized list of candidates that can then be efficiently validated in the lab, reducing overall development time while maintaining accuracy through subsequent experimental verification
Solution Approach 2:
An AI-based prediction model serves as an intermediary between theoretical sequence design and experimental validation. This intermediary system processes large numbers of candidate sequences, predicting their biological activity and compatibility, thereby filtering and prioritizing candidates before they undergo time-consuming wet lab experiments. The AI acts as a bridge that accelerates the transition from computational design to experimental verification
2Reliability
If CAR T-cells are designed separately for each individual patient, then personalized treatment efficacy is improved, but the cost for each treatment becomes very high
Solution Approach 1:
The system creates computational models and in-silico copies of patient-specific biological data to simulate and predict treatment outcomes. By using AI-based virtual modeling of patient tumors and immune systems, the system can evaluate multiple personalized treatment scenarios computationally before implementing actual therapies, reducing the need for expensive trial-and-error approaches while maintaining personalized treatment benefits
Solution Approach 2:
The AI model optimizes multiple parameters simultaneously including nucleotide sequence composition, binding affinity strength, off-target effect thresholds, and patient-specific biological variables. By systematically varying and optimizing these parameters through machine learning, the system identifies cost-effective personalized sequences that achieve desired therapeutic outcomes without requiring expensive iterative experimental testing for each patient
3Reliability
If multiple preclinical studies and clinical trials are conducted without optimization of target cell nucleotide sequence selection, then comprehensive safety and efficacy testing is achieved, but the failure rate increases and time and resources are lost
Solution Approach 1:
The AI system performs preliminary optimization and filtering of candidate nucleotide sequences before preclinical and clinical studies begin. By pre-screening sequences for potential safety issues, predicted efficacy, and compatibility with patient characteristics, the system reduces the likelihood of failures during expensive clinical trials. This preliminary computational optimization ensures that only the most promising candidates advance to resource-intensive testing phases
Solution Approach 2:
The system incorporates feedback loops where AI predictions are continuously refined based on results from preclinical studies and clinical trials. Performance data from actual experiments feeds back into the machine learning models, improving their predictive accuracy for future sequence selections. This feedback mechanism reduces failure rates by learning from past successes and failures, making subsequent trial selections more reliable and efficient
Data Source
AI summary
The present disclosure relates to field of cell nucleotide sequence designing and discloses method and system for designing cell nucleotide sequences. The sequence designing system receives historical data related to results of procedures related to analysis of cell nucleotide sequences from databases. Further, the sequence designing system executes an Artificial Intelligence (AI) based prediction model using vectorized data corresponding to the historical data. Thereafter, the sequence designing system predicts a plurality of cell nucleotide sequences having values of cell characteristics within a predefined threshold values of the cell characteristics for a target cell nucleotide sequence using the AI based prediction model. Furthermore, the sequence designing system identifies feasible cell nucleotide sequences among the plurality of cell nucleotide sequences based on predefined reference information. Finally, the sequence designing system generates an explanation for the ranked list of the feasible cell nucleotide sequences, thereby designing the cell nucleotide sequences.