ATG1B Antibody

What is the structural design of ATG-101 antibody?

ATG-101 is engineered as a tetravalent "2+2" PD-L1×4-1BB bispecific antibody. The structure contains binding domains for both PD-L1 and 4-1BB receptors, with a greater binding affinity for PD-L1. This design enables concurrent binding to both targets, with the antibody structure derived from sequences detailed in patents WO2019196309A1, WO 2010/077634 A1, US8137667B2, and US2012/0237498A1 . The tetravalent design is critical for its function, as it shows significantly higher 4-1BB activation compared to bivalent PD-L1×4-1BB bispecific antibodies that incorporate Fabs from the same parental monoclonal antibodies .

How is ATG-101 binding capability measured in laboratory settings?

Researchers measure ATG-101 binding capability using ForteBio technology with a multi-step protocol:

• Biotin labeling of ATG-101 followed by purification with a desalting column

• Measurement of biotinylated protein concentration using a bicinchoninic acid kit

• Capture of biotinylated ATG-101 (100 nmol/L) on Streptavidin (SA) biosensor

• Sequential loading of target proteins (hPD-L1-Fc or h4-1BB-Fc) at 100 nmol/L for 5 minutes each

• Measurement of binding signals after each protein addition, using IgG-Fc as control

This method confirms ATG-101's ability to simultaneously bind both PD-L1 and 4-1BB, demonstrating the formation of ATG-101–4-1BB complexes followed by successful binding to PD-L1 .

What cell-based assays are used to evaluate 4-1BB activation by ATG-101?

Several cell-based assays are employed to establish the agonistic characteristics of ATG-101 toward 4-1BB:

• HEK293T cells expressing h4-1BB integrated with NFκB fluorescence reporter (293T-h41BB-NFκB-Luc) are co-cultured with CHO cells overexpressing human (CHO-hPDL1) or mouse PD-L1 (CHO-mPDL1)

• Mock-transfected CHO cells serve as negative controls

• The EC50 values (0.17 nmol/L for human and 1.23 nmol/L for mouse PD-L1) are calculated from dose-response curves

• Comparative analysis between tetravalent and bivalent formats is performed to assess the advantage of the tetravalent design

• Additional validation using murine colon carcinoma cell line MC38 with elevated PD-L1 expression upon mIFNγ stimulation

These assays demonstrate that ATG-101 activates 4-1BB signaling specifically in the presence of PD-L1-expressing cells, highlighting its conditional activation mechanism .

How does ATG-101 overcome the hepatotoxicity limitations of conventional 4-1BB agonists?

The hepatotoxicity observed with conventional 4-1BB agonists primarily results from non-specific activation of 4-1BB-positive liver-infiltrating T cells. ATG-101 addresses this limitation through its innovative mechanism:

• Conditional activation: ATG-101 activates 4-1BB signaling only when cross-linked with PD-L1-positive cells, localizing T-cell activation primarily to the tumor microenvironment (TME) rather than healthy tissues

• Reduced off-tumor toxicity: By requiring PD-L1 cross-linking for 4-1BB activation, the antibody minimizes systemic immune activation, as demonstrated in non-human primate studies where ATG-101 showed no evidence of hepatotoxicity or cytokine release syndrome (CRS)

• Tumor-specific targeting: The higher affinity for PD-L1 ensures preferential localization to PD-L1-rich microenvironments (typically tumors), further restricting 4-1BB activation to these sites

This mechanism represents a significant advancement over conventional 4-1BB agonists, making ATG-101 potentially safer for clinical applications while maintaining therapeutic efficacy.

What computational approaches are used to optimize antibody design similar to ATG-101?

Contemporary antibody engineering utilizes several computational approaches that could be applicable to bispecific antibodies like ATG-101:

• Observed Antibody Space (OAS): Analysis of paired and unpaired sequences from OAS datasets identifies antibody candidates within specific edit distances from starting antibodies

• Inverse Folding Models (e.g., AbMPNN): These generate new antibody sequences that maintain structural features compatible with binding to target antigens while optimizing for other properties

• Protein language models (e.g., ESM): Guide mutation of sequences to retain or improve binding affinity while enhancing developability characteristics

• Developability assessment: Rosetta scoring evaluates antibody stability and interface energetics, complemented by thermal aggregation prediction (TAP)

• GearBind framework: Graph-based architecture trained in a contrastive fashion to predict the effect of mutations on antibody-antigen complexes

Researchers typically validate computational designs using experimental methods such as size-exclusion chromatography (SEC) for aggregation propensity and differential scanning fluorimetry (DSF) for thermal stability .

How can researchers evaluate the efficacy of ATG-101 in immune checkpoint inhibitor (ICI)-resistant tumor models?

Evaluating ATG-101 in ICI-resistant models requires a multi-dimensional approach:

• Model selection: Utilize tumor models with documented resistance to anti-PD-(L)1 therapy, including both innate and acquired resistance models

• TME characterization: Before treatment, comprehensively profile the tumor microenvironment to establish baseline immune cell composition, particularly noting CD8+ T cell infiltration and the CD8+/regulatory T cell ratio

• Treatment protocol: Administer ATG-101 at varying doses, with optimal biological dosing around 2 mg/kg based on computational semimechanistic pharmacology modeling that maximizes both 4-1BB/ATG-101/PD-L1 trimer formation and PD-L1 receptor occupancy

• Response metrics:

  • Monitor tumor volume changes

  • Evaluate T cell proliferation and infiltration

  • Measure transformation of "cold" to "hot" tumor immunological status

  • Assess changes in exhausted T cell phenotypes

• Single-cell RNA sequencing: Apply scRNA-seq technology to characterize alterations in the TME landscape at the transcriptome level, providing insights into the comprehensive immune response mechanisms

This methodological approach enables researchers to determine whether ATG-101 can overcome resistance mechanisms and reactivate anti-tumor immunity in previously non-responsive models.

What experimental approaches should be used to characterize the binding specificity of antibodies like ATG-101?

Comprehensive binding specificity characterization requires multiple complementary techniques:

• Surface Plasmon Resonance (SPR):

  • Measure association/dissociation rates (ka/kd) and binding affinities (KD)

  • Determine relative affinities for each target (PD-L1 and 4-1BB)

  • Analyze binding under varying pH and ionic strength conditions

• Bio-Layer Interferometry:

  • Capture biotinylated antibody on streptavidin biosensors

  • Sequentially load target proteins to confirm simultaneous binding

  • Measure signal changes following each protein addition

• Competitive binding assays:

  • Pre-incubate with unlabeled competitors

  • Determine if binding is blocked by specific receptor antagonists

  • Map epitopes through competitive binding with characterized antibodies

• Cross-reactivity assessment:

  • Test binding to homologous proteins across species

  • Evaluate potential off-target binding using protein arrays

  • Screen against tissue panels to identify potential unwanted interactions

• Functional validation:

  • Cell-based reporter assays like the 293T-h41BB-NFκB-Luc system

  • Conditional activation assays using PD-L1-positive and negative cell lines

  • Flow cytometry to confirm binding to native receptors on primary cells

This multi-modal approach provides a comprehensive understanding of binding specificity, crucial for predicting in vivo behavior and potential off-target effects.

How can researchers distinguish between autoantibodies and therapeutic antibodies in experimental models?

When working with therapeutic antibodies like ATG-101 in models where autoantibodies may be present (e.g., in autoimmune disease models), researchers need robust methods to differentiate between them:

• Isotype-specific detection:

  • Use secondary antibodies specific to the therapeutic antibody's isotype

  • Apply species-specific anti-Fc reagents if the therapeutic antibody is from a different species than the host

• Epitope mapping:

  • Compare binding patterns of autoantibodies versus therapeutic antibodies

  • In Guillain-Barré syndrome research, specific patterns of reactivity against peripheral nerve components distinguish pathogenic autoantibodies from non-specific antibodies

• Functional characterization:

  • Autoantibodies typically exhibit different functional properties than engineered therapeutic antibodies

  • In GBS, anti-ganglioside antibodies show specific reactivity patterns that differ from non-specific antibodies

• Immunohistochemistry patterns:

  • Perform immunohistochemistry on tissue sections (e.g., peripheral nerve in GBS studies)

  • Analyze specific staining patterns, such as strong reactivity against Schwann cells in GBS patients

• Pre-treatment baseline:

  • Collect samples before therapeutic antibody administration to establish baseline autoantibody profiles

  • Use longitudinal sampling to track changes in antibody patterns after treatment

This methodological approach enables researchers to accurately distinguish therapeutic effects from endogenous autoantibody responses, particularly important in models with pre-existing immune dysregulation.

What are the optimal experimental controls when evaluating ATG-101 efficacy?

When designing experiments to evaluate ATG-101 efficacy, researchers should implement the following controls:

• Isotype controls:

  • IgG1 isotype control with wild-type Fc for most experiments

  • IgG1 isotype control with LALA mutation at Fc domain for single-cell sequencing studies

• Component antibody controls:

  • Parental anti-4-1BB monoclonal antibody alone

  • Parental anti-PD-L1 monoclonal antibody alone

  • Combination of parental anti-4-1BB and anti-PD-L1 antibodies

• Format controls:

  • Bivalent PD-L1×4-1BB bispecific antibody (1+1 format) to compare with the tetravalent (2+2) format

• Cellular controls:

  • PD-L1 negative cells (mock-transfected) to confirm conditional activation

  • IFNγ-stimulated versus non-stimulated cells to demonstrate PD-L1 dependence

• In vivo controls:

  • Vehicle control group

  • Standard-of-care treatment group (e.g., approved anti-PD-1 antibody)

  • Anti-PD-L1 monotherapy group

  • Anti-4-1BB monotherapy group

This comprehensive set of controls enables proper interpretation of ATG-101's specific contributions to observed effects and helps distinguish its unique mechanism from those of its component parts or alternative formats.

How can researchers address inconsistent results in ATG-101 activation assays?

When facing inconsistent results in ATG-101 activation assays, researchers should systematically evaluate:

• PD-L1 expression levels:

  • Confirm PD-L1 expression on target cells via flow cytometry

  • Consider that IFNγ pre-treatment may be necessary to upregulate PD-L1 expression, as demonstrated with MC38 cells

  • Standardize IFNγ concentration and treatment duration

• Cell culture conditions:

  • Maintain consistent cell passage numbers

  • Standardize cell density in assays

  • Control for confluency effects on receptor expression

• Assay readout optimization:

  • Calibrate luminescence or fluorescence detection systems

  • Establish appropriate signal-to-background ratios

  • Determine optimal incubation times for signal development

• Antibody quality control:

  • Verify antibody integrity via SEC analysis

  • Confirm activity of each new antibody batch

  • Implement proper storage conditions to prevent degradation

• Experimental timing:

  • 4-1BB signaling kinetics may vary in different experimental systems

  • Establish time-course experiments to determine optimal measurement points

By systematically addressing these factors, researchers can significantly improve the consistency and reproducibility of ATG-101 activation assays, leading to more reliable experimental outcomes.

What are the key considerations when adapting antibody engineering approaches from other models to ATG-101-like bispecific antibodies?

When adapting antibody engineering approaches to develop bispecific antibodies similar to ATG-101, researchers should consider:

• Computational model selection:

  • Success rates of models vary significantly; for example, RFDiffusion may show poor success rates without antibody-specific fine-tuning

  • Even with constraints (e.g., known epitope residues), success rates for high-quality antibody-antigen complex predictions remain below 10%

• Computational resource requirements:

  • Model training can be extremely resource-intensive—up to 30 days on 128 A100 GPUs

  • Consider collaborative approaches or cloud computing solutions for resource-intensive modeling

• Format-specific constraints:

  • Tetravalent formats require different optimization than bivalent formats

  • Domain orientation and linker design significantly impact bispecific antibody function

• Cross-species reactivity:

  • When designing dual-reactive antibodies (like ATG-101's cross-reactivity with mouse PD-L1), consider sequence conservation at binding interfaces

  • Validate binding to orthologs experimentally as computational predictions may be less reliable for cross-species interactions

• Benchmarking considerations:

  • Different generative models (MEAN, dyMEAN, IgBLEND, Ablang, Ablang2, AntiBerty, ESM, Antifold, ESM-IF, AbX, Diffab) show varying performance on different datasets

  • Select appropriate benchmarking datasets based on your specific engineering goals

By addressing these considerations systematically, researchers can more effectively adapt current antibody engineering approaches to develop novel tetravalent bispecific antibodies with optimized properties.

How might the ATG-101 mechanism be applied to other immune checkpoint combinations?

The conditional activation mechanism employed by ATG-101 offers a template for developing other immune checkpoint-targeting bispecific antibodies:

• Alternative immune stimulatory receptor targeting:

  • Apply the PD-L1-dependent activation concept to other immune stimulatory receptors beyond 4-1BB, such as OX40, GITR, or CD40

  • Design tetravalent bispecific antibodies that activate these receptors only in PD-L1-rich environments

• Alternative checkpoint anchoring:

  • Replace PD-L1 targeting with other checkpoint proteins expressed in the tumor microenvironment (TME), such as CTLA-4 ligands, LAG-3 ligands, or TIM-3 ligands

  • Select checkpoint targets based on their expression pattern in specific tumor types

• Trispecific approaches:

  • Develop molecules targeting PD-L1, 4-1BB, and a third target to enhance specificity or expand functionality

  • Consider adding tumor-associated antigen (TAA) targeting to further restrict activation to the tumor vicinity

• Affinity engineering:

  • Systematically vary the relative affinities for each target to optimize the conditional activation mechanism

  • Explore how differences in affinity ratios affect safety and efficacy profiles

• Computational modeling:

  • Apply advanced protein design models like those referenced in the research to predict optimal configurations for novel checkpoint combinations

  • Utilize structure-based approaches to optimize domain orientation and linker design

This translational approach could potentially address limitations of current checkpoint therapies while expanding the repertoire of immunotherapeutic options for cancer treatment.

What research questions remain unanswered regarding the long-term effects of conditional 4-1BB activation?

Several critical research questions remain regarding the long-term consequences of conditional 4-1BB activation as employed by ATG-101:

• Memory T cell formation and persistence:

  • Does conditional 4-1BB activation support durable memory T cell formation?

  • What is the longevity of anti-tumor immune responses after treatment discontinuation?

• Resistance mechanisms:

  • Can tumors develop resistance through PD-L1 downregulation or 4-1BB signaling pathway alterations?

  • What biomarkers might predict development of resistance to conditional 4-1BB activation?

• Impact on immune homeostasis:

  • Are there delayed immune-related adverse events that might emerge with extended treatment?

  • Does repeated conditional 4-1BB activation alter T cell receptor repertoire diversity?

• Combination therapy considerations:

  • How does prior or concurrent treatment with conventional checkpoint inhibitors affect ATG-101 efficacy?

  • Which treatment sequences optimize the benefits of conditional 4-1BB activation?

• Predictive biomarkers:

  • Beyond PD-L1 expression, what tumor or immune parameters predict response to conditional 4-1BB activation?

  • Can single-cell analyses of pre-treatment samples predict which patients will benefit most?

Addressing these questions will require long-term follow-up studies and comprehensive immune monitoring in both preclinical models and clinical trials, potentially including approaches similar to the autoantibody screening methodologies described in Guillain-Barré syndrome research .