Agent skill

nk-cell-therapy-agent

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Install this agent skill to your Project

npx add-skill https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/nk-cell-therapy-agent

SKILL.md


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.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:

  • read_file
  • run_shell_command

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:

bash
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

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