Nk cell therapy agent
Skill bg-szy/TOP-SKILLS/skills/awesome-skills/nk-cell-therapy-agent
全球最大的 Claude Code 技能聚合库 · 收录 3900+ 来自 12+ 来源的技能,提供在线搜索与趋势分析看板 / The world's largest Claude Code skill aggregation hub — 3900+ skills from 12+ sources with online search and trend dashboard
npx -y skills add bg-szy/TOP-SKILLS --skill nk-cell-therapy-agentAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
2 things to look at
- no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
- 4 stars4 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
AI-powered NK cell therapy design for cancer immunotherapy including CAR-NK engineering, memory-like NK generation, and KIR/HLA matching optimization.
SKILL.md
5.5 KB, as published. Nobody here has run it
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
-
CAR-NK Design: Design chimeric antigen receptors optimized for NK cell biology (NK-specific signaling domains).
-
KIR/HLA Matching: Predict KIR-HLA interactions for donor selection in allogeneic therapy.
-
Memory-Like NK Generation: Optimize CIML protocol with IL-12/15/18 cytokine preactivation.
-
Expansion Optimization: ML models for feeder-free NK expansion conditions.
-
Tumor Target Prediction: Match NK receptor profiles to tumor ligand expression.
-
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
-
Input: Target antigen, tumor type, NK source (PB, UCB, iPSC, cell line).
-
CAR Design: Generate optimized CAR-NK construct sequence.
-
KIR Analysis: Determine KIR genotype and HLA matching for donors.
-
Activation Protocol: Optimize cytokine cocktail for desired phenotype.
-
Expansion: Design feeder-based or feeder-free expansion protocol.
-
Quality Prediction: Predict NK product functionality.
-
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
<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->