armored-cart-design-agent

SkillMedia

Gives your agent a library of medical AI skills to answer health questions with.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the armored-cart-design-agent skill

About this capability

The largest open-source medical AI skills library for OpenClaw🦞.

What this skill tells your AI

The instructions your AI receives, as published by freedomintelligence/openclaw-medical-skills in skills/armored-cart-design-agent/SKILL.md and read by ahel’s review.


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

CytokineMechanismClinical StatusTumor Types
IL-12Th1 polarization, IFN-gammaPhase I/IISolid tumors
IL-15T/NK persistencePhase I/IIHematologic, solid
IL-18Inflammasome, IFN-gammaPhase I (promising)Lymphoma
IL-7T cell survivalPhase IMultiple
IL-21T cell proliferationPreclinicalMultiple
CCL19/21T cell traffickingPreclinicalSolid tumors

Construct Architecture Options

ComponentOptionsConsideration
PromoterEF1a, PGK, CAG, NFAT-inducibleExpression level/timing
Signal PeptideNative, IL-2ss, IgKSecretion efficiency
CytokineMembrane-bound vs secretedLocal vs systemic
LinkerT2A, P2A, IRESCo-expression efficiency
Kill SwitchiCasp9, HSV-TK, CD20Safety control
PositionBefore/after CARExpression 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

OutputDescriptionFormat
Construct SequenceFull transgene DNA.fasta, .gb
Construct MapAnnotated visualization.png, .pdf
Expression ModelPredicted levels.json
Safety AnalysisRisk assessment.json
Manufacturing GuideProduction recommendations.md
Predicted EfficacyTumor killing model.json

IL-12 Armoring Details

AspectDesign ChoiceRationale
ConfigurationTethered IL-12 (p70)Localized, reduced toxicity
ExpressionNFAT-inducibleActivation-dependent
DoseLow-level expressionSafety optimization
CombinationWith PD-1 knockoutEnhanced activity

IL-18 Armoring Details

AspectDesign ChoiceRationale
ConfigurationSecreted mature IL-18Enhanced IFN-gamma
ExpressionConstitutive or inducibleContext-dependent
Clinical ResultsLymphoma responsesValidated approach
CombinationWith IL-21Synergistic

IL-15 Armoring Details

AspectDesign ChoiceRationale
ConfigurationMembrane-tethered IL-15/IL-15RaCis-presentation
ExpressionConstitutive moderatePersistence without toxicity
BenefitReduced IL-2 dependenceManufacturing advantage
SafetyLower CRS riskClinical 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

RiskMitigationImplementation
Cytokine stormInducible expressionNFAT promoter
Systemic toxicityMembrane tetheringLocalized effect
Uncontrolled proliferationKill switchiCasp9
On-target off-tumorRegulatable CARLogic gates

Clinical Trials (2025-2026)

TrialArmoringTargetCancerStatus
NCT03721068IL-18CD19LymphomaPhase I (positive)
NCT04119024IL-12GD2NeuroblastomaPhase I
NCT03932565IL-15/21CD19B-ALLPhase I
MultipleIL-7/CCL19VariousSolidPreclinical

Prerequisites

  • Python 3.10+
  • Biopython for sequence handling
  • CAR design databases
  • Codon optimization tools
  • Structure prediction (optional)

Related Skills

  • CART_Design_Optimizer_Agent - Base CAR optimization
  • NK_Cell_Therapy_Agent - NK cell engineering
  • Cytokine_Storm_Analysis_Agent - Safety analysis
  • TCell_Exhaustion_Analysis_Agent - Exhaustion prevention

Manufacturing Considerations

AspectArmored CAR ChallengeSolution
Vector SizeLarger transgeneOptimize construct
TransductionLower efficiencyIncrease MOI
ExpansionCytokine effectsTune expression
CharacterizationComplex phenotypeEnhanced QC

Special Considerations

  1. Tumor Type Matching: Different tumors need different armoring
  2. Expression Timing: Constitutive vs inducible tradeoffs
  3. Dose Finding: Balance efficacy vs toxicity
  4. Combination: Consider with checkpoint knockout
  5. Manufacturing: Larger constructs affect production

Efficacy Enhancement Mechanisms

MechanismCytokineEffect
PersistenceIL-15, IL-7Longer survival
TME RemodelingIL-12M2→M1, DC activation
Bystander KillingIL-18Enhanced IFN-gamma
TraffickingCCL19/21T cell recruitment
Anti-exhaustionIL-21Stem-like maintenance

Author

AI Group - Biomedical AI Platform

Signals

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Last commit
Jul 2026
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skill
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armored-cart-design-agent
Source
github.com/freedomintelligence/openclaw-medical-skills