cart-design-optimizer-agent

<!--

COPYRIGHT NOTICE

This file is part of the "Universal Biomedical Skills" project.

Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>

All Rights Reserved.

#

This code is proprietary and confidential.

Unauthorized copying of this file, via any medium is strictly prohibited.

#

Provenance: Authenticated by MD BABU MIA

-->

---

name: 'cart-design-optimizer-agent'

description: 'AI-guided CAR-T cell design for solid tumors using antigen prioritization, safety-by-design architectures, and exhaustion-resistant engineering.'

measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.

allowed-tools:

• read_file
• run_shell_command

---

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.

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

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

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

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

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

CAR Architecture Options

| Architecture | Mechanism | Best For |

|--------------|-----------|----------|

| Standard 2nd Gen | CD28 or 4-1BB costimulation | Hematological malignancies |

| Logic-Gated (AND) | Requires 2 antigens for activation | Solid tumors, safety |

| synNotch Priming | TME signal triggers CAR expression | Local activation |

| Armored CAR | Cytokine secretion (IL-15, IL-21) | Hostile TME |

| Universal/SUPRA | Adaptable targeting via adaptor | Multi-antigen, flexibility |

| PD-1 Knock-in | CAR in PD-1 locus | Exhaustion resistance |

Workflow

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

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

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

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

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

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

1. 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

| Target | Method | Effect |

|--------|--------|--------|

| TOX | CRISPR KO | Prevents exhaustion program |

| NR4A1-3 | Triple KO | Blocks exhaustion TFs |

| PD-1 locus | CAR integration | TME-responsive expression |

| c-Jun | Overexpression | Overcomes AP-1 imbalance |

| DNMT3A | KO | Epigenetic reprogramming |

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

<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->