CAR-T Design Optimizer Agent

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CAR-T Design Optimizer Agent

The CAR-T Design Optimizer Agent provides end-to-end AI-guided design of chimeric antigen receptor T-cells. It integrates antigen prioritization, safety-constrained CAR architectures, exhaustion resistance engineering, and computational modeling of CAR-T kinetics for optimized therapeutic design.

When to Use This Skill

• When designing CAR-T therapies for solid tumors with limited target antigens.
• To optimize CAR construct sequences for reduced exhaustion and self-activation.
• For selecting safety-by-design architectures (logic-gated, modular, armored).
• When predicting CAR-T expansion, persistence, and efficacy.
• To engineer exhaustion-resistant CAR-T cells via gene editing strategies.

Core Capabilities

1. Antigen Prioritization: AI-driven ranking of target antigens based on tumor specificity, expression levels, and safety profiles.

2. CARMSeD Prediction: Predictive model forecasting CAR constructs prone to tonic signaling, self-activation, and dysfunction.

3. Safety Architecture Design: Logic-gated (synNotch), ON/​OFF switches, armored designs for solid tumor safety.

4. Exhaustion Resistance: CRISPR target selection (TOX, NR4A, PD-1 knockouts) and PD-1 locus integration strategies.

5. Pharmacokinetic Modeling: Multi-population models predicting CAR-T expansion, distribution, and persistence.

6. LLM-Assisted Design: Constrained large language model reasoning for evidence synthesis and design justification.

CAR Architecture Options

Workflow

1. Antigen Selection: Analyze tumor expression data to prioritize targets.

2. Safety Assessment: Evaluate off-tumor expression in normal tissues.

3. CAR Design: Generate construct sequences with selected domains.

4. CARMSeD Screening: Predict self-activation and exhaustion propensity.

5. Architecture Selection: Match patient/​tumor to optimal CAR design.

6. Gene Editing Design: Select CRISPR targets for enhanced function.

7. Output: Optimized CAR sequence, predicted performance, manufacturing specs.

Example Usage

User: "Design an optimized CAR-T construct targeting HER2 for breast cancer with minimized exhaustion."

Agent Action:

python3 Skills/​Immunology_Vaccines/​CART_Design_Optimizer_Agent/​cart_designer.py \
    --target HER2 \
    --tumor_type breast_cancer \
    --expression_data tumor_rnaseq.tsv \
    --normal_tissues gtex_expression.tsv \
    --architecture synnotch_armored \
    --exhaustion_engineering tox_knockout \
    --model carmsed_v2 \
    --output cart_design_report/

CARMSeD Model Details

Prediction Targets:

• Tonic signaling propensity
• Self-activation risk
• Exhaustion trajectory
• Proliferative capacity

Input Features:

• scFv binding affinity
• Hinge/​spacer length
• Costimulatory domain
• Transmembrane sequence
• Expression system

Validated Performance:

• AUC > 0.85 for dysfunction prediction
• In vitro to in vivo correlation

Anti-Exhaustion Engineering Strategies

Computational Pharmacokinetics

Lotka-Volterra Model:

dC/​dt = r*C*(1 - C/​K) - k*C*T  # CAR-T expansion
dT/​dt = -α*C*T                   # Tumor killing

Multi-Population Extensions:

• Memory vs. effector subsets
• Exhaustion state transitions
• Cytokine-mediated effects
• Checkpoint interactions

Prerequisites

• Python 3.10+
• PyTorch for ML models
• CRISPRscan for guide design
• Protein structure tools (optional)

Related Skills

• TCell_Exhaustion_Analysis_Agent - For exhaustion profiling
• Neoantigen_Vaccine_Agent - For antigen identification
• CRISPR_Design_Agent - For gene editing optimization

Clinical Considerations

1. Cytokine Release Syndrome: Risk assessment and mitigation designs
2. ICANS Neurotoxicity: CNS penetration modeling
3. Manufacturing: Transduction efficiency predictions
4. Persistence: Memory phenotype engineering

Author

AI Group - Biomedical AI Platform