--- name: car-t-design-optimizer-agent description: "---name: cart-design-optimizer-agent description: AI-guided CAR-T cell design for solid tumors using antigen prioritization, safety-by-design architectures, and exhaustion-resistant engineering. license: MIT metadata: author: AI Group version: \"1.0.0\" created: \"2026-01-19\" compatibility:" --- --- name: 'cart-design-optimizer-agent' description: 'AI-guided CAR-T cell design for solid tumors using antigen prioritization, safety-by-design architectures, and exhaustion-resistant engineering.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- # CAR-T Design Optimizer Agent The **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, and computational modeling of CAR-T kinetics for optimized therapeutic design. ## When to Use This Skill * When designing CAR-T therapies for solid tumors with limited target antigens. * To optimize CAR construct sequences for reduced exhaustion and self-activation. * For selecting safety-by-design architectures (logic-gated, modular, armored). * When predicting CAR-T expansion, persistence, and efficacy. * To engineer exhaustion-resistant CAR-T cells via gene editing strategies. ## Core Capabilities 1. **Antigen Prioritization**: AI-driven ranking of target antigens based on tumor specificity, expression levels, and safety profiles. 2. **CARMSeD Prediction**: Predictive model forecasting CAR constructs prone to tonic signaling, self-activation, and dysfunction. 3. **Safety Architecture Design**: Logic-gated (synNotch), ON/OFF switches, armored designs for solid tumor safety. 4. **Exhaustion Resistance**: CRISPR target selection (TOX, NR4A, PD-1 knockouts) and PD-1 locus integration strategies. 5. **Pharmacokinetic Modeling**: Multi-population models predicting CAR-T expansion, distribution, and persistence. 6. **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 1. **Antigen Selection**: Analyze tumor expression data to prioritize targets. 2. **Safety Assessment**: Evaluate off-tumor expression in normal tissues. 3. **CAR Design**: Generate construct sequences with selected domains. 4. **CARMSeD Screening**: Predict self-activation and exhaustion propensity. 5. **Architecture Selection**: Match patient/tumor to optimal CAR design. 6. **Gene Editing Design**: Select CRISPR targets for enhanced function. 7. **Output**: Optimized CAR sequence, predicted performance, manufacturing specs. ## Example Usage **User**: "Design an optimized CAR-T construct targeting HER2 for breast cancer with minimized exhaustion." **Agent Action**: ```bash python3 Skills/Immunology_Vaccines/CART_Design_Optimizer_Agent/cart_designer.py \ --target HER2 \ --tumor_type breast_cancer \ --expression_data tumor_rnaseq.tsv \ --normal_tissues gtex_expression.tsv \ --architecture synnotch_armored \ --exhaustion_engineering tox_knockout \ --model carmsed_v2 \ --output cart_design_report/ ``` ## CARMSeD Model Details **Prediction Targets**: - Tonic signaling propensity - Self-activation risk - Exhaustion trajectory - Proliferative capacity **Input Features**: - scFv binding affinity - Hinge/spacer length - Costimulatory domain - Transmembrane sequence - Expression system **Validated Performance**: - AUC > 0.85 for dysfunction prediction - In vitro to in vivo correlation ## Anti-Exhaustion Engineering Strategies | Target | Method | Effect | |--------|--------|--------| | TOX | CRISPR KO | Prevents exhaustion program | | NR4A1-3 | Triple KO | Blocks exhaustion TFs | | PD-1 locus | CAR integration | TME-responsive expression | | c-Jun | Overexpression | Overcomes AP-1 imbalance | | DNMT3A | KO | Epigenetic reprogramming | ## Computational Pharmacokinetics **Lotka-Volterra Model**: ``` dC/dt = r*C*(1 - C/K) - k*C*T # CAR-T expansion dT/dt = -α*C*T # Tumor killing ``` **Multi-Population Extensions**: - Memory vs. effector subsets - Exhaustion state transitions - Cytokine-mediated effects - Checkpoint interactions ## Prerequisites * Python 3.10+ * PyTorch for ML models * CRISPRscan for guide design * Protein structure tools (optional) ## Related Skills * TCell_Exhaustion_Analysis_Agent - For exhaustion profiling * Neoantigen_Vaccine_Agent - For antigen identification * CRISPR_Design_Agent - For gene editing optimization ## Clinical Considerations 1. **Cytokine Release Syndrome**: Risk assessment and mitigation designs 2. **ICANS Neurotoxicity**: CNS penetration modeling 3. **Manufacturing**: Transduction efficiency predictions 4. **Persistence**: Memory phenotype engineering ## Author AI Group - Biomedical AI Platform --- Source attribution: mdbabumiamssm Original project: https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-/blob/ef3c40b235ba23083e67263a921759784f0dd251/Skills/Immunology_Vaccines/CART_Design_Optimizer_Agent/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Dave AI. ## Upstream license notice MIT License Copyright (c) 2025 MD BABU MIA , PHD Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Dave AI resource metadata Creator / publisher: mdbabumiamssm Original project: https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-/blob/ef3c40b235ba23083e67263a921759784f0dd251/Skills/Immunology_Vaccines/CART_Design_Optimizer_Agent/SKILL.md Attribution basis: Recovered original package and original project license at a pinned revision Fields of work: Education & Research, Healthcare & Life Sciences, Product Management Roles: Clinical Research, Medical Writer, Product Manager, Researcher Specialties: API Tools: Python Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 6fe79af7305e12571141b8fba689a258717077da4a2abc94497b7b7fb86d6544 Metadata reviewed: 2026-10-27 This metadata supersedes earlier Dave AI catalog attribution. Original instruction and supporting files are preserved.