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维护者 FreedomIntelligence · 最近更新 2026年4月1日
Design armored CAR-T cells with cytokine payloads and resistance mechanisms.
原始来源
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/armored-cart-design-agent
技能摘要
原始文档
Armoring Payload Selection: Choose optimal cytokines for tumor type.
Expression Level Optimization: Balance efficacy vs toxicity.
Inducible System Design: Engineer regulated expression systems.
Safety Switch Integration: Design kill switches and controls.
Construct Optimization: Optimize transgene configuration.
Efficacy Prediction: Predict enhanced tumor killing.
| 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 |
| 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 |
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