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name: nk-cell-therapy-agent description: AI-powered NK cell therapy design for cancer immunotherapy including CAR-NK engineering, memory-like NK generation, and KIR/HLA matching optimization. license: MIT metadata: author: AI Group version: "1.0.0" created: "2026-01-19" compatibility:

• system: Python 3.10+ allowed-tools:
• run_shell_command
• read_file
• write_file

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NK Cell Therapy Agent

The NK Cell Therapy Agent provides AI-driven design and optimization of natural killer cell therapies for cancer treatment. It covers CAR-NK engineering, cytokine-induced memory-like (CIML) NK generation, KIR/HLA matching, and NK cell expansion optimization.

When to Use This Skill

• When designing CAR-NK constructs for tumor targeting.
• To optimize KIR/HLA mismatch for allogeneic NK therapy.
• For generating memory-like NK cells with enhanced persistence.
• When predicting NK cell activation against specific tumor types.
• To analyze NK cell receptor repertoires and function.

Core Capabilities

1. CAR-NK Design: Design chimeric antigen receptors optimized for NK cell biology (NK-specific signaling domains).

2. KIR/HLA Matching: Predict KIR-HLA interactions for donor selection in allogeneic therapy.

3. Memory-Like NK Generation: Optimize CIML protocol with IL-12/15/18 cytokine preactivation.

4. Expansion Optimization: ML models for feeder-free NK expansion conditions.

5. Tumor Target Prediction: Match NK receptor profiles to tumor ligand expression.

6. Persistence Enhancement: Engineering strategies for improved in vivo survival.

NK Cell Advantages Over T Cells

| Feature | NK Cells | T Cells | |---------|----------|---------| | MHC requirement | No | Yes | | Allogeneic use | Yes (no GVHD) | Limited (GVHD risk) | | CRS risk | Lower | Higher | | Off-the-shelf | Yes | Autologous typical | | Antigen escape | Multiple receptors | Single CAR | | Persistence | Shorter | Longer |

CAR-NK Architecture

[scFv] - [Hinge] - [Transmembrane] - [Costimulatory] - [Signaling]

NK-Optimized Domains:
- Transmembrane: NKG2D, CD8α, or CD28
- Costimulatory: 2B4, DAP10, or CD28
- Signaling: CD3ζ (with NK-specific adaptations)
- Additional: Cytokine secretion (IL-15), suicide switch

Workflow

1. Input: Target antigen, tumor type, NK source (PB, UCB, iPSC, cell line).

2. CAR Design: Generate optimized CAR-NK construct sequence.

3. KIR Analysis: Determine KIR genotype and HLA matching for donors.

4. Activation Protocol: Optimize cytokine cocktail for desired phenotype.

5. Expansion: Design feeder-based or feeder-free expansion protocol.

6. Quality Prediction: Predict NK product functionality.

7. Output: CAR sequence, donor recommendations, expansion protocol, QC metrics.

Example Usage

User: "Design a CAR-NK targeting CD19 for B-cell malignancies with enhanced persistence."

Agent Action:

python3 Skills/Immunology_Vaccines/NK_Cell_Therapy_Agent/nk_designer.py \
    --target CD19 \
    --tumor_type b_cell_lymphoma \
    --nk_source ucb \
    --persistence_strategy il15_secretion \
    --costimulatory 2B4_DAP10 \
    --donors donor_hla_kir.json \
    --output carnk_design/

NK Receptor-Ligand Interactions

Activating Receptors:

| Receptor | Ligands | Tumor Expression | |----------|---------|------------------| | NKG2D | MICA/B, ULBPs | Stress-induced | | DNAM-1 | CD155, CD112 | Broadly expressed | | NKp30 | B7-H6, BAG6 | Tumor-specific | | NKp46 | Unknown tumor | Variable | | CD16 | IgG Fc | ADCC trigger |

Inhibitory Receptors:

| Receptor | Ligands | Function | |----------|---------|----------| | KIR2DL1 | HLA-C2 | Self tolerance | | KIR2DL2/3 | HLA-C1 | Self tolerance | | KIR3DL1 | HLA-Bw4 | Self tolerance | | NKG2A | HLA-E | Checkpoint |

Memory-Like NK (CIML) Protocol

Cytokine Preactivation:

• IL-12 (10 ng/mL) + IL-15 (50 ng/mL) + IL-18 (50 ng/mL)
• 16-18 hour stimulation
• Enhanced IFN-γ, cytotoxicity upon restimulation
• Improved in vivo persistence

Clinical Evidence: Effective in relapsed/refractory AML

KIR/HLA Matching Optimization

Missing-Self Recognition:

• Donor KIR + / Patient HLA -
• Enhanced NK cytotoxicity
• Important for allo-HSCT

Prediction Model:

• Input: Donor KIR genotype, patient HLA
• Output: Predicted NK alloreactivity score
• Validated in transplant outcomes

AI/ML Components

CAR-NK Optimization:

• Adapted CARMSeD for NK biology
• NK-specific signaling domain preferences
• Tonic signaling prediction

Expansion Prediction:

• Fold-expansion from culture conditions
• Phenotype shift modeling
• Exhaustion marker prediction

Prerequisites

• Python 3.10+
• HLA/KIR databases
• NK receptor databases
• Flow cytometry analysis tools

Related Skills

• CART_Design_Optimizer_Agent - For CAR engineering principles
• Epitope_Prediction_Agent - For target selection
• Flow_Cytometry_AI - For NK phenotyping

Clinical Development

Current CAR-NK Programs:

• CD19 CAR-NK (MD Anderson - AML, lymphoma)
• NKG2D CAR-NK (various solid tumors)
• CD70 CAR-NK (renal cell carcinoma)
• HER2 CAR-NK (breast cancer)

Author

AI Group - Biomedical AI Platform