CML17 Antibody

What is CCL17 and how do CCL17 antibodies function in experimental systems?

CCL17 (Thymus and Activation Regulated Chemokine, TARC) is a homeostatic chemokine that mediates immune cell recruitment by binding to its receptor CCR4. CCL17 has been implicated in various inflammatory conditions, including asthma, atopic dermatitis, idiopathic pulmonary fibrosis, and ulcerative colitis. CCL17 antibodies are monoclonal antibodies designed specifically to neutralize CCL17, thereby blocking its interaction with CCR4-expressing cells such as Th2 lymphocytes and dendritic cells .

The functional mechanism involves selective binding to epitopes on CCL17, preventing receptor engagement without affecting other chemokines that signal through CCR4, such as CCL22. Most characterized CCL17 antibodies, including the well-studied M116, employ an induced-fit binding mechanism that involves conformational changes in complementarity-determining regions (CDRs) . This selective neutralization allows these antibodies to modulate inflammatory responses while maintaining beneficial innate immune effects mediated by CCL22 on CCR4 .

What are the key structural features of CCL17-targeting antibodies?

The crystal structure of CCL17 in complex with neutralizing antibodies provides critical insights into their binding mechanisms. Taking M116 as an example, this antibody is derived from human germlines IGHV5-51 (for VH) and IGKV4-1 (for VL) and binds to a conformational epitope on CCL17 . The key structural features include:

The unique binding pattern of M116 involves an unusual induced-fit mechanism that includes cis-trans isomerization in two complementarity-determining regions . This structural arrangement enables highly selective neutralization by binding to an epitope distinct from the receptor-binding site, which contributes to the antibody's specificity and efficacy in blocking CCL17-mediated signaling.

How can researchers differentiate between CCL17 antibodies and anti-CCR4 antibodies in experimental design?

Differentiating between CCL17 antibodies and anti-CCR4 antibodies is critical for experimental design, particularly when studying specific immune pathway contributions. The primary distinction lies in their target selectivity and consequent biological effects.

CCL17 antibodies selectively neutralize CCL17-CCR4 interactions without affecting CCL22-CCR4 signaling. This selectivity provides an important experimental advantage when researchers need to isolate the specific contribution of CCL17 in inflammatory processes. Anti-CCL17 monoclonal antibodies may provide an improved safety profile compared to anti-CCR4 mAbs by selectively blocking CCL17 without interacting with CCR4-expressing platelets . Additionally, CCL17-specific antibodies will not block the beneficial innate immune effects of CCL22 on CCR4 .

For validation experiments, researchers should assess the specificity of CCL17 antibodies by confirming their lack of cross-reactivity with homologous chemokines, particularly CCL22, which shares the CCR4 receptor with CCL17. Control experiments should include both CCL17 and CCL22 stimulation assays to verify the selective blocking of CCL17-mediated effects.

What research models are best suited for evaluating CCL17 antibody efficacy?

Several experimental models have demonstrated utility in evaluating CCL17 antibody efficacy across different disease contexts:

In myocardial infarction models, Ccl17 deletion reduced left ventricular remodeling by 40% and increased regulatory T cell (Treg) recruitment by 2.5-fold, improving cardiac function. These knockout models provide valuable comparison data for antibody neutralization studies.

For in vitro assessment, chemotaxis assays using CCR4-expressing cells represent the gold standard for determining the neutralizing dose (ND50) of anti-CCL17 antibodies. For GSK3858279, an ND50 of 0.75–3.0 μg/mL was established in preclinical chemotaxis assays.

How does the conformational binding mechanism of CCL17 antibodies influence experimental reproducibility?

The induced-fit binding mechanism of antibodies like M116 to CCL17 introduces important considerations for experimental reproducibility. The binding involves cis-trans isomerization in complementarity-determining regions (CDRs), creating a dynamic binding process rather than a rigid lock-and-key interaction . This mechanism results in conformational changes in both the antibody CDRs and the CCL17 epitope that may be influenced by experimental conditions.

For reproducible results, researchers should consider:

• Buffer composition and pH: These factors can influence the energetics of the cis-trans isomerization process

• Incubation time: Allow sufficient equilibration time for the induced-fit binding to reach maximum occupancy

• Temperature consistency: Maintain consistent temperature during binding experiments as the isomerization rate is temperature-dependent

• Protein preparation methods: Ensure consistent folding of both antibody and CCL17

When designing binding assays with CCL17 antibodies, researchers should implement rigorous controls and standardize experimental conditions to account for the dynamic nature of this binding mechanism. Additionally, time-course experiments may reveal important kinetic parameters that influence neutralization efficacy.

What are the critical considerations for antibody engineering when modifying CCL17 antibodies?

When engineering CCL17 antibodies for research applications, several critical parameters must be considered to maintain functionality while enhancing desired properties:

Species, Isotype, and Subtype Selection:
Species switching involves reformatting the variable regions to an antibody backbone of a different species, which can increase compatibility with secondary antibodies, enable easier co-labeling studies, and prevent unwanted antibody interactions in serological assays . For in vivo research using animal models, species-matched antibodies offer advantages including reduced immunogenicity and increased potency .

Fc Domain Engineering:
The Fc domain significantly impacts antibody function beyond providing structural support. For CCL17 antibodies intended for in vivo research:

• If engagement with the immune system is required to activate ADCC and/or CDC, an Fc domain capable of engaging with Fc receptors (typically human IgG1) is essential

• If immune system engagement is undesirable, the molecule must either contain no Fc domain or an Fc domain with minimal binding to Fc receptors

• Fc silencing mutations (such as Absolute Antibody's Fc Silent™ mutation) can abolish binding to Fc receptors and eliminate ADCC effector function

Expression and Manufacturability:
Humanization can dramatically improve expression yields and reduce aggregation. In one study, 25 humanized variants showed enhanced titers by as much as 30-fold compared to the chimeric antibody, with 15 showing a 10-fold or greater increase in expression level and 12 showing minimal aggregation (>99.5% monomer) .

How can active learning approaches improve experimental efficiency in developing CCL17 antibodies?

Active learning methodologies represent a sophisticated approach to optimizing experimental resources in antibody research. For CCL17 antibody development, these approaches can significantly reduce experimental costs while accelerating development timelines.

Recent research has demonstrated that active learning strategies can optimize the prediction of antibody-antigen binding in library-on-library settings . These approaches are particularly valuable when dealing with out-of-distribution prediction scenarios, where test antibodies and antigens are not represented in the training data .

The implementation of active learning follows this methodology:

• Begin with a small labeled subset of antibody-antigen binding data

• Apply machine learning to predict interactions

• Identify the most informative experiments to perform next

• Update the model with new experimental data

• Repeat until satisfactory predictive performance is achieved

In a recent study, three novel active learning algorithms significantly outperformed random data selection, reducing the number of required antigen mutant variants by up to 35% and accelerating the learning process by 28 steps compared to the random baseline . For CCL17 antibody research, this approach could dramatically improve experimental efficiency by prioritizing the most informative binding assays.

What methodological approaches should be used to characterize CCL17 antibody specificity?

Characterizing the specificity of CCL17 antibodies requires multifaceted approaches to ensure they selectively neutralize CCL17 without cross-reactivity with homologous chemokines like CCL22. A comprehensive specificity assessment should include:

Binding Specificity Assays:

• ELISA competition assays with structurally related chemokines, particularly CCL22

• Surface plasmon resonance (SPR) to determine binding kinetics and affinity constants for CCL17 versus related chemokines

• Epitope mapping to confirm binding to the unique conformational epitope spanning residues 21–23, 44–45, and 60–68 of CCL17

Functional Specificity Assessment:

• Chemotaxis inhibition assays using CCR4-expressing cells stimulated with either CCL17 or CCL22

• Calcium flux assays to measure selective inhibition of CCL17-induced signaling

• Receptor occupancy assays on cells expressing CCR4 to confirm antibody prevents CCL17 but not CCL22 binding

Structural Characterization:
X-ray crystallography or cryo-EM studies of the antibody-CCL17 complex can provide definitive evidence of binding specificity by revealing the precise molecular interactions. The crystal structure of M116 Fab in complex with CCL17 revealed that this antibody binds to a unique epitope distinct from the receptor-binding site, enabling selective neutralization .

How do different isotypes and subtypes of CCL17 antibodies influence their experimental applications?

The isotype and subtype of CCL17 antibodies significantly impact their functional properties and experimental utility. Antibody engineering allows researchers to alter the isotype or subtype of any antibody to optimize specific research applications .

Isotype Selection Considerations:
Converting between isotypes (e.g., IgG to IgM) can serve distinct research purposes. For example, an IgG antibody, the major antibody of the secondary immune response, can be reformatted to an IgM antibody, the predominant antibody of the primary immune response, to aid in infectious disease research and diagnostic assay development .

Subtype Effects on Function:
Altering the IgG subtype can have a profound impact on the experimental outcomes. Although not specific to CCL17 antibodies, studies with other therapeutic antibodies have shown:

• Converting a mouse IgG2b subtype to a mouse IgG2a subtype greatly increased anti-tumor activity of an anti-CTLA-4 antibody in mouse models

• Switching a mouse anti-TIGIT antibody from an IgG1 subtype to the IgG2a version boosted the antibody's anti-tumor potency

These findings suggest that similar engineering approaches could optimize CCL17 antibodies for specific experimental contexts, particularly in in vivo models studying inflammatory diseases.

Half-Life Considerations:
The half-life requirements of an antibody depend on the experimental timeline. Different isotypes exhibit varying half-lives, which must be matched to the duration of the planned experiments .

What are the optimal methods for validating CCL17 antibody neutralization in vitro?

Validating CCL17 antibody neutralization capacity requires systematic functional assays that directly measure the inhibition of CCL17-mediated effects. A comprehensive validation protocol should include:

Primary Functional Assays:

• Chemotaxis Inhibition Assay: The gold standard for determining neutralizing capacity of CCL17 antibodies. This method measures the ability of antibodies to prevent CCL17-induced migration of CCR4-expressing cells in a transwell system. The neutralizing dose (ND50) for antibodies like GSK3858279 is typically determined using this assay, with effective ranges of 0.75–3.0 μg/mL in preclinical models.

• CCR4 Signaling Assays: Measure inhibition of downstream signaling events such as:

  • Calcium flux using fluorescent calcium indicators

  • Phosphorylation of signaling proteins by western blot or flow cytometry

  • Reporter gene assays in CCR4-transfected cell lines

Comparative Analysis:
To confirm specificity, parallel assays should be performed with both CCL17 and CCL22 to demonstrate selective inhibition of CCL17-mediated effects without impacting CCL22 signaling through CCR4.

Dose-Response Characterization:
Generate complete dose-response curves with the antibody to determine:

• IC50 values for functional inhibition

• Maximum inhibitory effect (efficacy)

• Hill slope (cooperativity of binding)

These parameters provide crucial information about the potency and mechanism of neutralization that simple endpoint measurements cannot capture.

What technical challenges must be addressed when using CCL17 antibodies in complex immunological assays?

Implementing CCL17 antibodies in complex immunological assays presents several technical challenges that must be systematically addressed:

Endogenous CCL17 Interference:
Endogenous CCL17 in biological samples (serum, tissue culture supernatants) may compete with experimental manipulations. Researchers should:

• Quantify baseline CCL17 levels in experimental systems

• Consider washout periods or media exchanges to minimize interference

• Include appropriate isotype controls at equivalent concentrations

Multi-Parameter Flow Cytometry Considerations:
When incorporating CCL17 antibodies into multi-parameter flow cytometry panels:

• Carefully select fluorophore conjugates to minimize spectral overlap

• Perform antibody titration to determine optimal concentrations

• Include fluorescence-minus-one (FMO) controls to set accurate gates

• Consider species switching to enable easier co-labeling studies and prevent unwanted antibody interactions in complex panels

Ex Vivo Tissue Analysis:
For analyzing CCL17-antibody effects in tissues:

• Optimize tissue digestion protocols to preserve CCR4 expression

• Standardize timing between tissue collection and analysis

• Include positive controls (known CCR4+ populations) to verify staining efficacy

• Consider tissue-specific autofluorescence and implement appropriate compensation strategies

How should researchers analyze and interpret data from CCL17 antibody therapeutic studies?

Data analysis and interpretation for CCL17 antibody therapeutic studies require rigorous statistical approaches and consideration of multiple outcome measures:

Statistical Analysis Framework:

• Determine appropriate sample sizes using power calculations based on expected effect sizes from preliminary data

• Apply appropriate statistical tests based on data distribution (parametric vs. non-parametric)

• Correct for multiple comparisons when assessing multiple endpoints

• Consider using Bayesian analysis for probability assessment (as used in GSK3858279 trials, which reported 90% Bayesian probability for pain reduction)

Endpoint Analysis Recommendations:
For inflammatory disease models, standardized endpoints should include:

In the GSK3858279 trial for osteoarthritis pain, key endpoints included average pain reduction (-1.3 points on NRS scale) and WOMAC Pain Improvement (-15.2 points), both showing statistical significance compared to placebo.

Translating Preclinical to Clinical Data:
When interpreting animal model data, researchers should consider species differences in:

• CCL17 expression patterns and levels

• CCR4 distribution on immune cell subsets

• Background inflammation in disease models

• Antibody half-life and tissue distribution

How can CCL17 antibodies be implemented in cardiovascular disease research?

CCL17 antibodies offer unique opportunities for investigating inflammatory mechanisms in cardiovascular pathologies. Implementation strategies should be guided by existing research demonstrating CCL17's role in myocardial injury and atherosclerosis.

Experimental Models:
In myocardial infarction models, Ccl17 deletion reduced left ventricular remodeling by 40% and increased regulatory T cell (Treg) recruitment by 2.5-fold, improving cardiac function. These findings suggest several experimental approaches for CCL17 antibody implementation:

• Acute Myocardial Injury Models:

  • Administer CCL17 antibodies before or after experimental infarction

  • Monitor left ventricular remodeling through echocardiography and histological analysis

  • Quantify immune cell infiltration, particularly CCR2+ macrophages and Tregs

  • Measure cardiac function parameters (ejection fraction, fractional shortening)

• Atherosclerosis Progression Models:

  • Implement long-term CCL17 antibody treatment in ApoE-/- or LDLR-/- mice

  • Assess plaque development, stability, and composition

  • Characterize changes in circulating and plaque-resident immune cells

Technical Implementation:
When designing cardiovascular experiments with CCL17 antibodies:

• Consider antibody delivery route (systemic vs. local) based on research questions

• Establish appropriate dosing schedules based on half-life and cardiac tissue penetration

• Include both preventive (pre-injury) and therapeutic (post-injury) treatment protocols to distinguish between different mechanisms

Outcome Assessment:
A comprehensive evaluation should include:

• Cardiac function parameters (ejection fraction, fractional shortening)

• Tissue remodeling markers (collagen deposition, myofibroblast activation)

• Inflammatory cell infiltration (flow cytometry and immunohistochemistry)

• Local and systemic cytokine/chemokine profiles

What are the most promising research applications for CCL17 antibodies beyond established disease models?

While CCL17 antibodies have been extensively studied in atopic dermatitis, myocardial injury, and osteoarthritis, emerging research suggests several promising new applications worthy of investigation:

Neuroinflammatory Conditions:
The CCL17-CCR4 axis contributes to neuroinflammation in multiple contexts. Research applications could include:

• Experimental autoimmune encephalomyelitis (EAE) models of multiple sclerosis

• Traumatic brain injury models examining the role of infiltrating T cells

• Neuropathic pain models, building on the analgesic effects observed in osteoarthritis studies

Fibrotic Disorders:
CCL17's role in recruiting pro-fibrotic immune cells suggests applications in:

• Liver fibrosis models (carbon tetrachloride or bile duct ligation)

• Pulmonary fibrosis (bleomycin-induced or radiation-induced)

• Renal fibrosis following acute kidney injury

Cancer Immunotherapy Research:
The complex role of CCR4-expressing cells in the tumor microenvironment presents opportunities for:

• Combination therapy studies with checkpoint inhibitors

• Investigation of regulatory T cell modulation in tumors

• Analysis of CCL17 blockade effects on dendritic cell function in cancer models

Metabolic Inflammation:
Emerging connections between CCL17 and metabolic disorders suggest applications in:

• Diet-induced obesity models examining adipose tissue inflammation

• Non-alcoholic steatohepatitis models focusing on hepatic immune cell recruitment

• Insulin resistance studies examining tissue-specific inflammatory signatures

For these novel applications, researchers should consider employing the active learning approaches described earlier to optimize experimental designs and resource allocation .

How might combining computational prediction with experimental validation accelerate CCL17 antibody research?

The integration of computational prediction with experimental validation represents a powerful approach to accelerating CCL17 antibody research. Recent advances in machine learning for antibody-antigen binding prediction offer a framework for this integration.

Implementation Strategy:

• Establish Predictive Models: Utilize library-on-library screening approaches where many antigens are probed against many antibodies to identify specific interacting pairs. These data can train machine learning models to predict target binding by analyzing many-to-many relationships between antibodies and antigens .

• Apply Active Learning: Implement active learning strategies to optimize experimental resource allocation. Start with a small labeled subset of data and iteratively expand based on model predictions. This approach has been shown to reduce the number of required antigen mutant variants by up to 35% and accelerate the learning process compared to random data selection .

• Conduct Focused Validation: Use computational predictions to prioritize the most promising antibody candidates or epitope regions for experimental validation, focusing resources on high-confidence predictions.

Technical Considerations:

• Address out-of-distribution prediction challenges when test antibodies and antigens are not represented in training data

• Implement appropriate cross-validation strategies to assess model generalizability

• Incorporate structural data from crystallography studies to improve binding predictions

The combination of computational and experimental approaches can accelerate development timelines, reduce resource requirements, and enable more systematic exploration of the antibody-antigen binding landscape.

What innovations in antibody engineering could enhance the specificity and functionality of next-generation CCL17 antibodies?

Several emerging innovations in antibody engineering hold promise for developing next-generation CCL17 antibodies with enhanced specificity and functionality:

Fc Engineering for Optimal Effector Function:
Advanced Fc engineering approaches can precisely tune the interaction of CCL17 antibodies with the immune system:

• Enhanced binding to specific Fc receptors to promote particular effector functions

• Completely silenced Fc domains using platforms like the STR Fc silencing platform for applications requiring no immune engagement

• Half-life extension through Fc modifications to reduce dosing frequency in chronic models

Avidity Optimization:
Careful engineering of antibody avidity can enhance binding characteristics for specific applications:

• Modifying the "arms" of the antibody for optimal binding to each antigen

• Engineering structural arrangements to maximize the strength of antibody-antigen complexes

• Balancing affinity and valency to achieve desired pharmacodynamic properties

Humanization for Improved Manufacturability:
Advanced humanization technologies like the Prometheus™ humanization technology can dramatically improve antibody properties:

• Enhanced expression yields (up to 30-fold increase compared to chimeric antibodies)

• Improved stability with minimal aggregation (>99.5% monomer)

• Optimized framework selections to maintain binding properties while improving biophysical characteristics

Bispecific Formats:
Developing bispecific antibodies that target CCL17 and a second relevant target could address complex disease mechanisms:

• Anti-CCL17/anti-IL-13 bispecifics for enhanced efficacy in atopic dermatitis

• Anti-CCL17/anti-TNF combinations for synergistic effects in inflammatory conditions

• CCL17/CCL22 dual-targeting approaches to comprehensively block CCR4 signaling

These innovations could significantly expand the research applications and therapeutic potential of CCL17 antibodies across multiple disease areas.