Design armored CAR-T cells with cytokine payloads and resistance mechanisms.
cart-design-optimizer-agent
维护者 FreedomIntelligence · 最近更新 2026年4月1日
Optimize CAR-T cell construct design: scFv selection, linker, co-stimulatory domain.
原始来源
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/cart-design-optimizer-agent
- 维护者
- FreedomIntelligence
- 许可
- MIT
- 最近更新
- 2026年4月1日
技能摘要
来自 SKILL.md 的关键信息
核心说明
- CAR-T Design Optimizer Agent provides end-to-end AI-guided design of chimeric antigen receptor T-cells. It integrates antigen prioritization,safety-constrained CAR architectures,exhaustion resistance engineering,、 computational modeling of CAR-T kinetics ,用于 optimized therapeutic design。
- When designing CAR-T therapies ,用于 solid tumors ,支持 limited target antigens。
- To optimize CAR construct sequences ,用于 reduced exhaustion 、 self-activation。
- 用于 selecting safety-by-design architectures (logic-gated,modular,armored)。
- When predicting CAR-T expansion,persistence,、 efficacy。
原始文档
SKILL.md 摘录
Core Capabilities
-
Antigen Prioritization: AI-driven ranking of target antigens based on tumor specificity, expression levels, and safety profiles.
-
CARMSeD Prediction: Predictive model forecasting CAR constructs prone to tonic signaling, self-activation, and dysfunction.
-
Safety Architecture Design: Logic-gated (synNotch), ON/OFF switches, armored designs for solid tumor safety.
-
Exhaustion Resistance: CRISPR target selection (TOX, NR4A, PD-1 knockouts) and PD-1 locus integration strategies.
-
Pharmacokinetic Modeling: Multi-population models predicting CAR-T expansion, distribution, and persistence.
-
LLM-Assisted Design: Constrained large language model reasoning for evidence synthesis and design justification.
CAR Architecture Options
| Architecture | Mechanism | Best For |
|---|---|---|
| Standard 2nd Gen | CD28 or 4-1BB costimulation | Hematological malignancies |
| Logic-Gated (AND) | Requires 2 antigens for activation | Solid tumors, safety |
| synNotch Priming | TME signal triggers CAR expression | Local activation |
| Armored CAR | Cytokine secretion (IL-15, IL-21) | Hostile TME |
| Universal/SUPRA | Adaptable targeting via adaptor | Multi-antigen, flexibility |
| PD-1 Knock-in | CAR in PD-1 locus | Exhaustion resistance |
Workflow
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Antigen Selection: Analyze tumor expression data to prioritize targets.
-
Safety Assessment: Evaluate off-tumor expression in normal tissues.
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CAR Design: Generate construct sequences with selected domains.
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CARMSeD Screening: Predict self-activation and exhaustion propensity.
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Architecture Selection: Match patient/tumor to optimal CAR design.
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Gene Editing Design: Select CRISPR targets for enhanced function.
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Output: Optimized CAR sequence, predicted performance, manufacturing specs.
适用场景
- When designing CAR-T therapies ,用于 solid tumors ,支持 limited target antigens。
不适用场景
- Do not rely on this catalog entry alone ,用于 installation 或 maintenance details。
上游相关技能
- TCell_Exhaustion_Analysis_Agent - For exhaustion profiling
- Neoantigen_Vaccine_Agent - For antigen identification
- CRISPR_Design_Agent - For gene editing optimization
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