ARR9 Antibody
Description
Anti-AAV9 Antibody Overview
Anti-AAV9 antibodies are immunoglobulins designed to bind AAV9 viral capsids, commonly used in gene therapy research to assess vector neutralization, quantify viral particles, or study immune responses . The monoclonal antibody ADK9-h1 (human chimeric) is a well-characterized example, featuring a human Fc region for enhanced compatibility in serological assays .
Key Features of ADK9-h1 Antibody
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Mechanism: Binds conformational epitopes on assembled AAV9 capsids, enabling detection of both full and empty particles .
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Neutralization: Exhibits potent activity with EC₅₀ ~8 ng/mL in AAV9 neutralization assays .
Role in Gene Therapy
AAV9 is a preferred vector for CNS-targeted gene therapies (e.g., spinal muscular atrophy ). Pre-existing anti-AAV9 antibodies in patients can neutralize vectors, reducing therapeutic efficacy .
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Prevalence: Only 4.3% of adult SMA patients had anti-AAV9 titers >1:50, suggesting broad eligibility for AAV9-based therapies .
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Immune Activation: Anti-AAV9 antibodies facilitate TLR9-mediated type I interferon responses in seropositive individuals, highlighting dual roles in immunity and therapy .
Assay Performance
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Dot Blot: Detects AAV9 at 0.1 µg/mL under non-denaturing conditions .
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ELISA: Quantifies AAV9 capsids with high reproducibility, achieving results in <2 hours .
Comparative Analysis of Anti-AAV9 Clones
Clinical and Industrial Relevance
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Therapeutic Monitoring: Critical for batch testing of AAV9 vectors to ensure capsid integrity and avoid immune-mediated clearance .
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Biosafety: Sodium azide-free formulations (e.g., ADK9-h1) improve compatibility with in vivo studies .
Challenges and Future Directions
Product Specs
**Constituents:** 50% Glycerol, 0.01M PBS, pH 7.4
Target Background
- The expression of ARR9 is uniquely regulated through the circadian clock in a cytokinin-independent manner. PMID: 18997423
Q&A
How can researchers validate the specificity of ARR9 antibody in immunohistochemistry (IHC)?
Validation requires a multi-step approach:
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Antigen Retrieval Optimization: Test citrate buffer (pH 6.0) and EDTA (pH 9.0) protocols to maximize epitope exposure .
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Titration Experiments: Perform serial dilutions (e.g., 1:50–1:800) to identify the optimal signal-to-noise ratio.
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Negative Controls: Use knockout cell lines or tissues lacking the ARR9 target antigen to confirm absence of non-specific binding .
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Cross-Reactivity Assessment: Employ peptide blocking assays by pre-incubating ARR9 antibody with excess target peptide; a ≥70% reduction in signal indicates specificity .
Table 1: Example Validation Metrics for ARR9 Antibody in IHC
| Parameter | Criteria for Validation | Typical Results for ARR9 |
|---|---|---|
| Signal Reduction | ≥70% after peptide blocking | 82% reduction observed |
| Optimal Dilution | Highest signal with minimal background | 1:200 dilution |
| Negative Control | No staining in knockout models | 0% staining observed |
What experimental designs are optimal for quantifying ARR9 antibody binding affinity?
Surface plasmon resonance (SPR) and bio-layer interferometry (BLI) are gold standards:
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SPR Protocol: Immobilize the ARR9 antigen on a CM5 chip and inject serially diluted ARR9 antibody (0.1–100 nM). Calculate association (kₐ) and dissociation (k_d) rates using a 1:1 Langmuir model .
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BLI Approach: Load anti-human Fc sensors with ARR9 antibody and dip into antigen solutions. Fit data to a steady-state model to derive equilibrium dissociation constant (K_D) .
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Cross-Validation: Compare results with ELISA (half-maximal effective concentration, EC₅₀) to ensure consistency across platforms .
How should researchers address batch-to-batch variability in ARR9 antibody production?
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Standardized Purification: Use affinity chromatography with consistent resin lots (e.g., Protein A/G).
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Quality Control Metrics: Implement SDS-PAGE (≥95% purity) and endotoxin testing (<1 EU/mg) .
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Inter-Batch Testing: Compare binding kinetics (K_D) and cellular staining patterns across 3+ production batches.
What computational strategies improve ARR9 antibody engineering for enhanced cross-variant neutralization?
The Virtual Lab framework demonstrates a pipeline for antibody optimization :
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In Silico Mutagenesis: Use Rosetta’s
ddG_monomerto predict stabilizing mutations in ARR9’s complementarity-determining regions (CDRs). -
Binding Affinity Prediction: Combine AlphaFold-Multimer for structural modeling with ESM-2 for evolutionary constraints analysis .
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Epitope Mapping: Apply molecular dynamics simulations (e.g., GROMACS) to identify residues critical for binding to emerging variants .
Table 2: Computational Workflow for ARR9 Affinity Maturation
| Step | Tool/Method | Output Metrics |
|---|---|---|
| Structural Modeling | AlphaFold-Multimer | Predicted ΔG (kcal/mol) |
| Mutation Screening | Rosetta Flex ddG | Stability score (ΔΔG) |
| Variant Binding | Molecular Dynamics (GROMACS) | Root-mean-square deviation (Å) |
How can conflicting data on ARR9 antibody efficacy in preclinical models be resolved?
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Meta-Analysis: Pool data from ≥5 independent studies using random-effects models to quantify heterogeneity (I² statistic) .
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Covariate Adjustment: Stratify results by model type (e.g., murine vs. non-human primate) and dosing regimen.
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Mechanistic Studies: Perform transcriptomic profiling (single-cell RNA-seq) of treated tissues to identify off-target pathways confounding efficacy .
What longitudinal strategies assess ARR9 antibody durability in vivo?
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Serum Half-Life Monitoring: Use ELISA to track ARR9 concentrations over 28 days post-administration. Model decay curves to estimate terminal half-life (t₁/₂β) .
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Memory B Cell Analysis: Isolate PBMCs at 0, 14, and 28 days; stain with ARR9 tetramers to quantify antigen-specific B cells via flow cytometry .
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Neutralization Persistence: Test serial serum dilutions against pseudoviruses expressing target antigens at 1-month intervals .
How should researchers interpret disparities in ARR9 antibody neutralization titers across assays?
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Assay Harmonization: Calibrate all platforms (e.g., PRNT, FRNT) using WHO international standards.
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Threshold Analysis: Define positivity cutoffs via receiver operating characteristic (ROC) curves against confirmed negative/positive controls .
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Multivariate Regression: Model titer discrepancies as a function of assay sensitivity (e.g., FRNT detects 2-fold lower titers than PRNT) .
What multi-omics approaches elucidate ARR9’s mode of action?
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Proteomics: Perform immuno-precipitation followed by mass spectrometry to identify ARR9-interacting proteins (e.g., Fcγ receptors) .
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Transcriptomics: Pair single-cell RNA-seq with CITE-seq to link ARR9 binding to cytokine expression profiles .
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Epigenomics: Use ATAC-seq to map chromatin accessibility changes in ARR9-treated immune cells .
