/aav-vector-design-agent

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--- name: 'aav-vector-design-agent' description: 'AI-powered adeno-associated virus (AAV) vector design for gene therapy including capsid engineering, promoter selection, and tropism optimization.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:

• read_file
• run_shell_command

---

AAV Vector Design Agent

The **AAV Vector Design Agent** provides AI-driven design of adeno-associated virus vectors for gene therapy applications. It covers capsid selection and engineering, promoter/enhancer design, transgene optimization, and manufacturing considerations.

When to Use This Skill

• When selecting optimal AAV serotype for tissue-specific targeting.
• To design novel capsid variants with enhanced properties.
• For optimizing transgene expression cassettes.
• When predicting immunogenicity and neutralizing antibody escape.
• To design liver-detargeted or CNS-tropic vectors.

Core Capabilities

1. **Capsid Selection**: Match AAV serotype to target tissue based on tropism profiles.

2. **Capsid Engineering**: Design modified capsids for enhanced transduction or immune evasion.

3. **Promoter Design**: Select and optimize tissue-specific or ubiquitous promoters.

4. **Transgene Optimization**: Codon optimization and regulatory element design.

5. **Immunogenicity Prediction**: Predict NAb binding and T-cell epitopes.

6. **Manufacturing Assessment**: Evaluate producibility and purification considerations.

AAV Serotype Tropism

| Serotype | Primary Tropism | Clinical Use | |----------|-----------------|--------------| | AAV1 | Muscle, CNS | Glybera (muscle) | | AAV2 | Broad (liver, muscle) | Luxturna (retina) | | AAV5 | CNS, liver, retina | Hemgenix (liver) | | AAV8 | Liver, muscle | Multiple trials | | AAV9 | CNS, cardiac, liver | Zolgensma (CNS) | | AAVrh10 | CNS, liver | CNS trials | | AAVrh74 | Muscle | Elevidys (muscle) | | AAV-PHP.eB | CNS (mouse) | Research |

Workflow

1. **Input**: Target tissue, therapeutic gene, patient population characteristics.

2. **Capsid Selection**: Rank serotypes by tropism profile match.

3. **Capsid Engineering**: Design modifications if needed (peptide insertion, point mutations).

4. **Cassette Design**: Optimize ITR-to-ITR expression cassette.

5. **Immunogenicity Analysis**: Predict NAb prevalence and T-cell epitopes.

6. **Manufacturing Review**: Assess production feasibility.

7. **Output**: Complete vector design with rationale.

Example Usage

**User**: "Design an AAV vector for liver-directed gene therapy in hemophilia B with low immunogenicity."

**Agent Action**:

python3 Skills/Gene_Therapy/AAV_Vector_Design_Agent/aav_designer.py \
    --target_tissue liver \
    --therapeutic_gene F9 \
    --indication hemophilia_b \
    --minimize_immunogenicity true \
    --nab_escape true \
    --promoter liver_specific \
    --output aav_design/

Expression Cassette Components

5' ITR - [Promoter] - [5' UTR] - [Transgene] - [WPRE] - [PolyA] - 3' ITR

Packaging limit: ~4.7 kb between ITRs

**Promoter Options**: | Promoter | Type | Size | Application | |----------|------|------|-------------| | CAG | Ubiquitous | 1.7 kb | Strong expression | | EF1α | Ubiquitous | 1.2 kb | Constitutive | | LP1 | Liver-specific | 0.5 kb | Hepatocyte targeting | | hSyn | Neuron-specific | 0.5 kb | CNS applications | | MCK | Muscle-specific | 0.6 kb | Myopathies | | CMV | Ubiquitous | 0.6 kb | High initial (silenced) |

Capsid Engineering Strategies

**Directed Evolution**:

• Error-prone PCR libraries
• DNA shuffling
• Selection in target tissue

**Rational Design**:

• Peptide display (insertion in variable loops)
• Point mutations for receptor targeting
• Tyrosine-to-phenylalanine for stability

**Machine Learning**:

• Sequence-function models
• Generative models for novel capsids
• Tropism prediction

Immunogenicity Considerations

**Pre-existing NAbs**: | Serotype | NAb Prevalence | |----------|----------------| | AAV2 | 30-60% | | AAV5 | 15-30% | | AAV8 | 15-25% | | AAV9 | 20-35% |

**Mitigation Strategies**:

• Serotype selection based on patient screening
• Engineered NAb-evading capsids
• Immunosuppression protocols
• Plasmapheresis

AI/ML Components

**Tropism Prediction**:

• CNN on capsid sequence
• Cell-type specific transduction
• Cross-species translation

**Immunogenicity Modeling**:

• MHC binding prediction
• T-cell epitope mapping
• NAb epitope prediction

**Expression Optimization**:

• Codon optimization algorithms
• RNA structure prediction
• miRNA target site avoidance

Manufacturing Considerations

| Factor | Impact | Optimization | |--------|--------|--------------| | Capsid yield | Production cost | Sequence modifications | | Empty/full ratio | Potency | Purification method | | Aggregation | Stability | Formulation | | DNA packaging | Transgene size | Cassette design |

Prerequisites

• Python 3.10+
• Sequence analysis tools
• Immunoinformatics packages
• Structural biology tools

Related Skills

• CRISPR_Design_Agent - For gene editing payloads
• Protein_Engineering - For capsid design
• RNA_Therapeutics - For alternative modalities

Regulatory Considerations

1. **Biodistribution**: Required for IND 2. **Shedding**: Vector in bodily fluids 3. **Germline transmission**: Gonadal presence 4. **Integration risk**: Random vs site-specific 5. **Immunogenicity**: Pre-existing and induced

Author

AI Group - Biomedical AI Platform

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