/armored-cart-design-agent
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/armored-cart-design-agent
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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.
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--- name: 'armored-cart-design-agent' description: 'AI-powered design of armored CAR-T cells with cytokine/chemokine expression for enhanced solid tumor efficacy, including IL-12, IL-15, IL-18, and IL-7 armoring strategies.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:
- read_file
- run_shell_command
---
Armored CAR-T Design Agent
The **Armored CAR-T Design Agent** provides AI-assisted design of next-generation armored CAR-T cells engineered to express cytokines, chemokines, or other enhancing factors. These armored T cells overcome solid tumor challenges including immunosuppressive TME, poor trafficking, and T cell exhaustion, with recent clinical success in lymphoma (IL-18) and ongoing trials with IL-12, IL-15, and IL-7.
When to Use This Skill
- When designing CAR-T cells for solid tumor applications.
- For selecting optimal armoring payloads (cytokines, chemokines).
- To optimize cytokine expression levels and regulation.
- When engineering safety switches for armored constructs.
- For predicting armored CAR-T efficacy and safety profiles.
Core Capabilities
1. **Armoring Payload Selection**: Choose optimal cytokines for tumor type.
2. **Expression Level Optimization**: Balance efficacy vs toxicity.
3. **Inducible System Design**: Engineer regulated expression systems.
4. **Safety Switch Integration**: Design kill switches and controls.
5. **Construct Optimization**: Optimize transgene configuration.
6. **Efficacy Prediction**: Predict enhanced tumor killing.
Armoring Strategies
| Cytokine | Mechanism | Clinical Status | Tumor Types | |----------|-----------|-----------------|-------------| | IL-12 | Th1 polarization, IFN-gamma | Phase I/II | Solid tumors | | IL-15 | T/NK persistence | Phase I/II | Hematologic, solid | | IL-18 | Inflammasome, IFN-gamma | Phase I (promising) | Lymphoma | | IL-7 | T cell survival | Phase I | Multiple | | IL-21 | T cell proliferation | Preclinical | Multiple | | CCL19/21 | T cell trafficking | Preclinical | Solid tumors |
Construct Architecture Options
| Component | Options | Consideration | |-----------|---------|---------------| | Promoter | EF1a, PGK, CAG, NFAT-inducible | Expression level/timing | | Signal Peptide | Native, IL-2ss, IgK | Secretion efficiency | | Cytokine | Membrane-bound vs secreted | Local vs systemic | | Linker | T2A, P2A, IRES | Co-expression efficiency | | Kill Switch | iCasp9, HSV-TK, CD20 | Safety control | | Position | Before/after CAR | Expression balance |
Workflow
1. **Input**: Target tumor type, TME characteristics, CAR design.
2. **Payload Selection**: Rank armoring strategies for tumor context.
3. **Expression Design**: Optimize promoter, levels, regulation.
4. **Safety Engineering**: Add appropriate control switches.
5. **Construct Assembly**: Generate optimized DNA sequence.
6. **Efficacy Prediction**: Model enhanced killing and persistence.
7. **Output**: Optimized armored CAR construct with annotations.
Example Usage
**User**: "Design an armored CAR-T for pancreatic cancer targeting mesothelin with IL-12 armoring for TME remodeling."
**Agent Action**:
python3 Skills/Immunology_Vaccines/Armored_CART_Design_Agent/design_armored_cart.py \
--car_target mesothelin \
--tumor_type pancreatic \
--armoring_payload IL-12 \
--expression_system NFAT_inducible \
--safety_switch iCasp9 \
--backbone lentiviral \
--optimize_codon human \
--output armored_cart_design/Output Components
| Output | Description | Format | |--------|-------------|--------| | Construct Sequence | Full transgene DNA | .fasta, .gb | | Construct Map | Annotated visualization | .png, .pdf | | Expression Model | Predicted levels | .json | | Safety Analysis | Risk assessment | .json | | Manufacturing Guide | Production recommendations | .md | | Predicted Efficacy | Tumor killing model | .json |
IL-12 Armoring Details
| Aspect | Design Choice | Rationale | |--------|---------------|-----------| | Configuration | Tethered IL-12 (p70) | Localized, reduced toxicity | | Expression | NFAT-inducible | Activation-dependent | | Dose | Low-level expression | Safety optimization | | Combination | With PD-1 knockout | Enhanced activity |
IL-18 Armoring Details
| Aspect | Design Choice | Rationale | |--------|---------------|-----------| | Configuration | Secreted mature IL-18 | Enhanced IFN-gamma | | Expression | Constitutive or inducible | Context-dependent | | Clinical Results | Lymphoma responses | Validated approach | | Combination | With IL-21 | Synergistic |
IL-15 Armoring Details
| Aspect | Design Choice | Rationale | |--------|---------------|-----------| | Configuration | Membrane-tethered IL-15/IL-15Ra | Cis-presentation | | Expression | Constitutive moderate | Persistence without toxicity | | Benefit | Reduced IL-2 dependence | Manufacturing advantage | | Safety | Lower CRS risk | Clinical benefit |
AI/ML Components
**Payload Selection**:
- TME profiling to match cytokine needs
- Multi-objective optimization
- Clinical outcome modeling
**Expression Optimization**:
- Promoter strength prediction
- Codon optimization
- mRNA stability modeling
**Safety Prediction**:
- CRS/ICANS risk modeling
- Off-tumor activity prediction
- Systemic cytokine levels
Safety Considerations
| Risk | Mitigation | Implementation | |------|------------|----------------| | Cytokine storm | Inducible expression | NFAT promoter | | Systemic toxicity | Membrane tethering | Localized effect | | Uncontrolled proliferation | Kill switch | iCasp9 | | On-target off-tumo
Read more
<!--
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: 'armored-cart-design-agent' description: 'AI-powered design of armored CAR-T cells with cytokine/chemokine expression for enhanced solid tumor efficacy, including IL-12, IL-15, IL-18, and IL-7 armoring strategies.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:
- read_file
- run_shell_command
---
Armored CAR-T Design Agent
The **Armored CAR-T Design Agent** provides AI-assisted design of next-generation armored CAR-T cells engineered to express cytokines, chemokines, or other enhancing factors. These armored T cells overcome solid tumor challenges including immunosuppressive TME, poor trafficking, and T cell exhaustion, with recent clinical success in lymphoma (IL-18) and ongoing trials with IL-12, IL-15, and IL-7.
When to Use This Skill
- When designing CAR-T cells for solid tumor applications.
- For selecting optimal armoring payloads (cytokines, chemokines).
- To optimize cytokine expression levels and regulation.
- When engineering safety switches for armored constructs.
- For predicting armored CAR-T efficacy and safety profiles.
Core Capabilities
1. **Armoring Payload Selection**: Choose optimal cytokines for tumor type.
2. **Expression Level Optimization**: Balance efficacy vs toxicity.
3. **Inducible System Design**: Engineer regulated expression systems.
4. **Safety Switch Integration**: Design kill switches and controls.
5. **Construct Optimization**: Optimize transgene configuration.
6. **Efficacy Prediction**: Predict enhanced tumor killing.
Armoring Strategies
| Cytokine | Mechanism | Clinical Status | Tumor Types | |----------|-----------|-----------------|-------------| | IL-12 | Th1 polarization, IFN-gamma | Phase I/II | Solid tumors | | IL-15 | T/NK persistence | Phase I/II | Hematologic, solid | | IL-18 | Inflammasome, IFN-gamma | Phase I (promising) | Lymphoma | | IL-7 | T cell survival | Phase I | Multiple | | IL-21 | T cell proliferation | Preclinical | Multiple | | CCL19/21 | T cell trafficking | Preclinical | Solid tumors |
Construct Architecture Options
| Component | Options | Consideration | |-----------|---------|---------------| | Promoter | EF1a, PGK, CAG, NFAT-inducible | Expression level/timing | | Signal Peptide | Native, IL-2ss, IgK | Secretion efficiency | | Cytokine | Membrane-bound vs secreted | Local vs systemic | | Linker | T2A, P2A, IRES | Co-expression efficiency | | Kill Switch | iCasp9, HSV-TK, CD20 | Safety control | | Position | Before/after CAR | Expression balance |
Workflow
1. **Input**: Target tumor type, TME characteristics, CAR design.
2. **Payload Selection**: Rank armoring strategies for tumor context.
3. **Expression Design**: Optimize promoter, levels, regulation.
4. **Safety Engineering**: Add appropriate control switches.
5. **Construct Assembly**: Generate optimized DNA sequence.
6. **Efficacy Prediction**: Model enhanced killing and persistence.
7. **Output**: Optimized armored CAR construct with annotations.
Example Usage
**User**: "Design an armored CAR-T for pancreatic cancer targeting mesothelin with IL-12 armoring for TME remodeling."
**Agent Action**:
python3 Skills/Immunology_Vaccines/Armored_CART_Design_Agent/design_armored_cart.py \
--car_target mesothelin \
--tumor_type pancreatic \
--armoring_payload IL-12 \
--expression_system NFAT_inducible \
--safety_switch iCasp9 \
--backbone lentiviral \
--optimize_codon human \
--output armored_cart_design/Output Components
| Output | Description | Format | |--------|-------------|--------| | Construct Sequence | Full transgene DNA | .fasta, .gb | | Construct Map | Annotated visualization | .png, .pdf | | Expression Model | Predicted levels | .json | | Safety Analysis | Risk assessment | .json | | Manufacturing Guide | Production recommendations | .md | | Predicted Efficacy | Tumor killing model | .json |
IL-12 Armoring Details
| Aspect | Design Choice | Rationale | |--------|---------------|-----------| | Configuration | Tethered IL-12 (p70) | Localized, reduced toxicity | | Expression | NFAT-inducible | Activation-dependent | | Dose | Low-level expression | Safety optimization | | Combination | With PD-1 knockout | Enhanced activity |
IL-18 Armoring Details
| Aspect | Design Choice | Rationale | |--------|---------------|-----------| | Configuration | Secreted mature IL-18 | Enhanced IFN-gamma | | Expression | Constitutive or inducible | Context-dependent | | Clinical Results | Lymphoma responses | Validated approach | | Combination | With IL-21 | Synergistic |
IL-15 Armoring Details
| Aspect | Design Choice | Rationale | |--------|---------------|-----------| | Configuration | Membrane-tethered IL-15/IL-15Ra | Cis-presentation | | Expression | Constitutive moderate | Persistence without toxicity | | Benefit | Reduced IL-2 dependence | Manufacturing advantage | | Safety | Lower CRS risk | Clinical benefit |
AI/ML Components
**Payload Selection**:
- TME profiling to match cytokine needs
- Multi-objective optimization
- Clinical outcome modeling
**Expression Optimization**:
- Promoter strength prediction
- Codon optimization
- mRNA stability modeling
**Safety Prediction**:
- CRS/ICANS risk modeling
- Off-tumor activity prediction
- Systemic cytokine levels
Safety Considerations
| Risk | Mitigation | Implementation | |------|------------|----------------| | Cytokine storm | Inducible expression | NFAT promoter | | Systemic toxicity | Membrane tethering | Localized effect | | Uncontrolled proliferation | Kill switch | iCasp9 | | On-target off-tumo
The largest open-source medical AI skill library for OpenClaw.
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