yehS Antibody

What is the YehS approach to antibody development?

The YehS approach refers to biomolecular engineering and chemical biology methodologies pioneered by the Yeh laboratory to develop engineered biologics, including antibodies, proteins, peptidomimetics, and Natural Product (NP)-like macrocycles as therapeutics and diagnostics. This approach focuses on creating molecules that act on signal transduction machinery to abrogate pathological cell signaling . The methodology emphasizes:

• Molecular engineering to target specific disease-related proteins

• Creating antibodies that modulate the interplay between cancer cells and tumor microenvironment

• Developing therapeutic inhibitors targeting oncogenic receptors, transcription factors, and extracellular matrix proteins

The Yeh laboratory has established robust pipelines for antibody development, including phage display technologies as evidenced by Johannes Yeh's role as Director of CSHL Antibody and Phage Display Shared Resource .

How are humanized antibodies developed using the YehS methodology?

The development of humanized antibodies using YehS methodology involves several structured steps, as demonstrated in recent research on antibodies targeting extracellular HSP90α (eHSP90α) :

• Target identification and validation: Identifying disease-relevant proteins (like eHSP90α in cancer) that show altered expression in pathological conditions

• Complementarity-determining region (CDR) design: Creating novel CDRs that exhibit high binding affinity toward the target protein

• Epitope mapping: Identifying critical amino acid residues involved in antibody-target interaction (e.g., residues E237, E239, D240, K241, E253, and K255 in HSP90α )

• Functional testing: Evaluating the antibody's ability to suppress pathological activities (such as invasive and spheroid-forming activities in cancer cell lines)

• Mechanism validation: Confirming the antibody's mechanism of action (e.g., blocking target protein's ligation with cell-surface receptors like CD91 )

• In vivo efficacy assessment: Testing efficacy in appropriate animal models (e.g., mouse models for PDAC)

What characterization methods are essential for newly developed antibodies?

Comprehensive characterization of newly developed antibodies requires multiple analytical approaches to assess various properties:

• Binding affinity measurements: Using techniques like biolayer interferometry to determine KD values

• Epitope mapping: Identifying the precise binding sites through techniques like peptide arrays or mutational analysis

• Specificity testing: Assessing cross-reactivity with related proteins to ensure specificity

• Functional assays: Evaluating the antibody's ability to modulate biological functions relevant to the target

• Biophysical characterization: Assessment of properties including:

  • Aggregation propensity

  • Water solubility

  • Half-life in relevant biological matrices

  • Thermal stability (Tm measurements)

• Toxicity evaluation: Testing potential cytotoxicity in non-target cells and assessing organ toxicity in animal models

Data from the HH01 antibody characterization exemplifies this approach, showing high binding affinity toward HSP90α, a half-life time of >18 days in mouse blood, low aggregation propensity, and no obvious toxicity in mouse organs .

How can bispecific antibodies be designed to target conserved and variable regions of viral proteins?

Designing bispecific antibodies (bsAbs) to target both conserved and variable regions of viral proteins represents an advanced strategy for addressing viral escape mutants, as demonstrated in SARS-CoV-2 research . The methodological approach involves:

• Epitope selection strategy:

  • Identify a conserved domain that shows minimal variation across viral variants (e.g., N-terminal domain)

  • Select a second domain that may be more variable but is functionally critical (e.g., receptor binding domain)

• Antibody component development:

  • Generate antibodies against each epitope independently

  • Characterize binding properties and neutralizing activities of individual antibodies

• Bispecific format selection:

  • Employ knobs-into-holes Fc construct technology to ensure correct heavy chain pairing

  • Use half-IgG expression followed by in vitro assembly to prevent light chain mismatch pairing

• Validation of biepitopic binding:

  • Confirm simultaneous binding to both epitopes using techniques like BLI

  • Verify that the bispecific format doesn't alter affinity to individual epitopes

• Neutralization assessment against variant panel:

  • Test against diverse viral variants to confirm broad neutralization capacity

  • Compare with monospecific antibodies to demonstrate improved breadth

This approach has been shown to generate antibodies capable of neutralizing diverse SARS-CoV-2 variants in vitro and providing protection in animal models when administered prophylactically .

What are antibody-based PROTACs (AbTACs) and how can they be developed for targeting membrane proteins?

Antibody-based PROTACs (AbTACs) represent an innovative approach to targeted protein degradation using fully recombinant bispecific antibodies. The methodological workflow for developing AbTACs against membrane proteins includes :

• Selection of E3 ligase target:

  • Identify membrane-bound E3 ligases (e.g., RNF43) accessible for antibody binding

  • Generate recombinant antibodies against the ectodomain of the selected E3 ligase using phage display

  • Validate binding using in vitro assays (e.g., Fab-phage ELISA) and cell-based methods

• Selection of target protein:

  • Choose membrane proteins with small or no known small-molecule ligands (e.g., PD-L1)

  • Utilize existing antibodies with validated binding properties

• Bispecific antibody construction:

  • Employ knobs-into-holes Fc technology to ensure correct heavy chain pairing

  • Express half-IgGs followed by in vitro assembly

  • Introduce His-tag on one half for purification of correctly assembled bispecifics

• Functional validation:

  • Verify simultaneous binding to both targets using BLI

  • Confirm binding affinities remain similar to parent antibodies

• Degradation assessment:

  • Measure reduction in target protein levels using flow cytometry

  • Determine degradation kinetics and maximal degradation capacity (DMax)

  • Evaluate factors affecting steady-state degradation including binding properties, cell-surface levels, E3-target stoichiometry, and endocytosis kinetics

This approach has demonstrated successful degradation of PD-L1 with a DMax of 63%, representing an innovative strategy for targeting membrane proteins that may be challenging for conventional small-molecule PROTACs .

How can antibodies be developed to resolve cross-reactivity issues between related pathogens?

Addressing cross-reactivity between related pathogens represents a significant challenge in antibody development, as demonstrated by SARS-CoV-2 and dengue virus cross-reactivity research . A methodological approach includes:

• Cross-reactivity characterization:

  • Confirm cross-reactivity using serological testing of patient samples

  • Identify specific antibody subtypes involved (e.g., anti-S1-RBD IgG cross-reacting with DENV E and NS1 proteins )

• Epitope mapping of cross-reactive antibodies:

  • Identify potential epitopes using phage-displayed random peptide libraries

  • Determine structural similarities between seemingly unrelated proteins from different pathogens

• Functional impact assessment:

  • Evaluate whether cross-reactive antibodies enhance or inhibit pathogen infectivity

  • Test cross-neutralization capacity in vitro and in animal models

• Epitope engineering approaches:

  • Design antibodies targeting unique, non-cross-reactive epitopes

  • Develop antibodies with enhanced specificity through affinity maturation against divergent epitopes

• Diagnostic strategy development:

  • Develop testing algorithms to differentiate between true and false-positive results

  • Design diagnostic assays targeting pathogen-specific antigens

This methodological approach not only addresses diagnostic challenges caused by cross-reactivity but can potentially uncover unexpected therapeutic benefits, as seen in the case where anti-SARS-CoV-2 S1-RBD antibodies inhibited DENV infection and NS1-induced endothelial hyperpermeability .

What basket trial design approaches are optimal for antibody therapeutics targeting diseases with shared molecular mechanisms?

Basket trial designs represent an innovative approach for evaluating single interventions across multiple diseases or disease subtypes that share molecular mechanisms. For antibody therapeutics, optimal methodological approaches include :

• Target selection criteria:

  • Identify diseases with shared molecular pathways but distinct clinical presentations

  • Select antibody targets that operate within common pathogenic mechanisms

  • Example: Different autoantibody subtypes in autoimmune encephalitis (AIE) that have distinct epidemiologic characteristics but share pathological mechanisms

• Master protocol development:

  • Create a unified protocol framework that accommodates multiple disease arms

  • Implement disease-specific arms that can be analyzed independently

  • Customize outcome measures for each disease subtype while maintaining protocol consistency

• Patient stratification strategy:

  • Group patients based on molecular biomarkers rather than traditional clinical diagnoses

  • Incorporate biomarker testing into eligibility criteria

• Adaptive design elements:

  • Allow for early stopping of ineffective arms

  • Permit sample size adjustments based on interim analyses

  • Enable addition of new disease arms as scientific understanding evolves

• Endpoint selection considerations:

  • Include both shared endpoints to enable cross-arm comparisons

  • Incorporate disease-specific endpoints to capture unique aspects of each condition

  • Balance surrogate biomarker endpoints with clinically meaningful outcomes

This approach improves interpretability and reliability of study data while leveraging shared infrastructure and efficiencies of the master protocol, making it particularly suitable for evaluating antibody therapeutics targeting rare diseases with common molecular underpinnings .

What high-throughput screening approaches can be used for agonist antibody discovery?

Several innovative high-throughput methodological approaches have been developed for agonist antibody discovery, including :

• Autocrine function-based screening:

  • Create libraries of surface-displayed antibody variants using lentiviral transfer cassettes

  • Express antibodies on mammalian reporter cells (typically one antibody per cell)

  • Select clones that activate reporter cells through a selectable phenotype

  • Recover and sequence antibody genes from positive clones

  • Advantages: Reduced stringency for antibody affinity may promote identification of rare clones with desirable biological properties

• Microencapsulation systems:

  • Co-encapsulate B cells and reporter cells in agarose-based microdroplets

  • Isolate cells with functional antibodies based on fluorescence patterns

  • Example: Identification of agonist antibodies against DR4 and DR5 using primary B cells from immunized chickens

• Co-culture selection systems:

  • Combine phage display with function-based screening using phage-producing bacteria co-encapsulated with mammalian reporter cells

  • Evaluate cellular activation in picoliter-sized droplet systems

  • Potential for FACS-based screening using microdroplets stable in aqueous phase

• Yeast-mammalian co-culture systems:

  • Culture yeast-displayed libraries with mammalian cells expressing target receptors

  • Screen yeast-mammalian cell complexes by FACS

  • Potential adaptation of yeast reporter cells engineered to report on receptor activation

These methodologies provide complementary approaches for identifying antibodies with desired functional properties, enabling the discovery of agonist antibodies that might be missed through traditional affinity-based screening methods.

How can researchers track and analyze the development of therapeutic antibodies across the industry?

Researchers can employ several methodological approaches to track and analyze therapeutic antibody development across the industry, with YAbS (The Antibody Society's Antibody Therapeutics Database) providing a comprehensive resource :

• Utilizing specialized databases:

  • Access YAbS database (https://db.antibodysociety.org) which catalogs over 2,900 commercially sponsored investigational antibody candidates

  • Filter data using parameters such as:

    • Molecular format

    • Targeted antigen

    • Development status

    • Indications studied

    • Geographic region of sponsors

• Conducting structured trend analyses:

  • Stratify molecules by development status (e.g., active clinical development, approved, discontinued)

  • Analyze distribution by clinical phase to assess pipeline maturity

  • Examine therapeutic area focus to identify hot areas of development

  • Evaluate geographic distribution of sponsoring companies

• Assessing success rates methodologically:

  • Calculate transition probabilities between development phases

  • Compare success rates across different antibody formats

  • Analyze success rates by therapeutic area

  • Evaluate timelines of clinical development

• Performing molecular characteristics analysis:

  • Examine trends in antibody formats (e.g., monospecific vs. bispecific)

  • Track evolution of novel formats like antibody-drug conjugates

  • Analyze targeting strategies and epitope selection approaches

This methodological approach provides researchers with comprehensive insights into the antibody therapeutics landscape, supporting informed decision-making and strategic planning in research and development efforts .

What in vivo models are most appropriate for evaluating antibody-prodrug systems?

Selecting appropriate in vivo models for evaluating antibody-prodrug systems requires careful methodological consideration, as demonstrated in catalytic antibody-prodrug activation research :

• Model selection criteria:

  • Disease relevance: Choose models that recapitulate human pathology

  • Pharmacokinetic considerations: Select models with appropriate drug metabolism

  • Target expression: Ensure relevant target expression in the model

  • Immune status: Consider immunocompetent models for comprehensive evaluation

• Syngeneic tumor model approach:

  • Utilize mouse syngeneic models (e.g., murine NXS2 neuroblastoma model )

  • Advantages:

    • Intact immune system for complete drug interaction assessment

    • Appropriate for evaluation of immune-mediated effects

    • Better representation of tumor microenvironment

• Intratumoral administration methodology:

  • Direct injection of catalytic antibody (e.g., antibody 38C2) into established tumors

  • Followed by systemic administration of prodrug

  • Assessment of localized activation through:

    • Tumor growth inhibition measurements

    • HPLC analysis of prodrug conversion

    • Histological evaluation of tumor tissue

• Comparative assessment approach:

  • Compare prodrug alone vs. prodrug with antibody

  • Include direct administration of active drug as positive control

  • Assess relative toxicity profiles between active drug and prodrug

• Prodrug design considerations:

  • Engineer prodrugs to be significantly less toxic than parent compounds

  • Example: Etoposide prodrug designed to be >2-fold less toxic than parental etoposide

  • Ensure efficient activation by the selected antibody

This methodological approach has demonstrated successful antitumor activity through localized activation of prodrugs in animal models, providing a foundation for translating antibody-prodrug systems to clinical applications .

How might antibody engineering approaches evolve to address challenges in targeting intracellular proteins?

Emerging methodological approaches for antibody-based targeting of intracellular proteins represent a frontier in therapeutic development, with several innovative strategies showing promise:

• Advanced antibody-based PROTAC development:

  • Expand AbTAC technology to target additional membrane E3 ligases

  • Optimize bispecific formats to improve cellular penetration and degradation efficacy

  • Develop AbTACs that can trigger degradation of proteins with minimal cytoplasmic domains

• Nanobody and single-domain antibody approaches:

  • Engineer smaller antibody formats with enhanced cellular penetration

  • Develop delivery systems for intracellular targeting of nanobodies

  • Create fusion proteins combining cell-penetrating peptides with antibody fragments

• Innovative antibody conjugate systems:

  • Design antibody-oligonucleotide conjugates for targeted delivery of gene-editing machinery

  • Develop antibody-peptide conjugates that can disrupt intracellular protein-protein interactions

  • Create antibody-small molecule hybrids with enhanced cellular penetration

• Catalytic antibody applications:

  • Expand catalytic antibody-prodrug systems to intracellular targets

  • Develop antibodies with enzymatic activities capable of modifying intracellular proteins

  • Engineer antibodies that can catalyze reactions within cellular compartments

• Cell-type specific delivery approaches:

  • Target cell surface receptors that undergo internalization

  • Exploit receptor-mediated endocytosis for antibody delivery

  • Develop tissue-specific targeting strategies to enhance therapeutic index

These methodological approaches represent potential pathways to expand the application of antibody therapeutics beyond traditional extracellular targets, opening new possibilities for addressing previously "undruggable" intracellular proteins involved in disease pathogenesis.

What approaches can enhance the immunomodulatory effects of therapeutic antibodies in the tumor microenvironment?

Advanced methodological approaches to enhance immunomodulatory effects of therapeutic antibodies in the tumor microenvironment include:

• Dual-targeting antibody strategies:

  • Develop bispecific antibodies targeting both tumor cells and immune checkpoints

  • Create antibodies targeting both tumor antigens and immunosuppressive cells (e.g., M2 macrophages)

  • Engineer antibodies that simultaneously engage effector cells and block inhibitory signals

• Microenvironment-responsive antibody designs:

  • Create antibodies with activation dependent on tumor microenvironment conditions (pH, hypoxia)

  • Develop antibodies that respond to tumor-specific proteases

  • Design masked antibodies that become fully active only in the tumor microenvironment

• M2 macrophage modulation approaches:

  • Target antibodies to reduce M2 macrophage population, as demonstrated with HH01 antibody

  • Develop antibodies that reprogram tumor-associated macrophages from M2 to M1 phenotype

  • Create combination strategies targeting both macrophages and T-cell exhaustion

• Tumor immunity reinvigoration methods:

  • Design antibodies that simultaneously block immune checkpoints and stimulate co-stimulatory receptors

  • Develop antibodies targeting tumor-derived extracellular factors like eHSP90α that promote immunosuppression

  • Create strategies to enhance infiltration of effector immune cells into tumors

• Endothelial-mesenchymal transition targeting:

  • Target cancer-associated fibroblasts derived from endothelial-mesenchymal transition

  • Develop antibodies that modify tumor vasculature to enhance immune cell infiltration

  • Create combination approaches simultaneously targeting tumor cells and supporting stromal cells

These methodological approaches leverage recent advances in understanding the complex interactions within the tumor microenvironment, offering potential strategies to enhance the efficacy of immunotherapeutic antibodies across multiple cancer types.

How can computational approaches be integrated into antibody design to predict and enhance therapeutic properties?

Integrating computational approaches into antibody design represents a cutting-edge methodology for enhancing therapeutic properties:

• Structure-based antibody design:

  • Utilize computational modeling to predict antibody-antigen interactions

  • Apply molecular dynamics simulations to optimize binding interfaces

  • Design complementarity-determining regions (CDRs) with improved binding properties

  • Identify critical amino acid residues for epitope binding (similar to identification of E237, E239, D240, K241, E253, and K255 in HSP90α binding )

• Machine learning integration:

  • Develop models to predict antibody developability properties

  • Create algorithms to optimize antibody sequences for reduced immunogenicity

  • Build predictive models for antibody stability and solubility

  • Design networks to forecast cross-reactivity with related antigens

• High-throughput virtual screening:

  • Screen virtual antibody libraries against target epitopes

  • Identify potential hits for experimental validation

  • Prioritize candidates based on predicted binding affinities and specificity

  • Reduce experimental burden through in silico pre-screening

• Pharmacokinetic property prediction:

  • Model antibody clearance mechanisms

  • Predict half-life based on sequence and structural features

  • Optimize sequences for extended circulation time

  • Forecast tissue distribution based on molecular properties

• AI-driven epitope mapping:

  • Identify conserved epitopes across variant targets

  • Predict conformational epitopes from protein structures

  • Discover cryptic epitopes not apparent in static structures

  • Map epitopes with potential for broad neutralization capacity (particularly relevant for viral targets )

These computational approaches can significantly accelerate antibody development by reducing experimental iterations, optimizing molecular properties before synthesis, and identifying promising candidates for advanced testing, ultimately enhancing the efficiency of therapeutic antibody discovery and development.

What strategies can overcome limitations in antibody expression and stability?

Methodological approaches to enhance antibody expression and stability include:

• Expression system optimization:

  • Select appropriate expression systems based on antibody format (E. coli for smaller fragments, mammalian systems for full IgGs)

  • Optimize codon usage for the selected expression system

  • Implement temperature-shift strategies during production

  • Fine-tune media composition and feeding strategies for improved yields

  • Example: Fab formats with good expression in E. coli (~5 mg/L) and high stability (Tm~80°C)

• Sequence-based stability engineering:

  • Identify and mutate aggregation-prone regions

  • Introduce stabilizing mutations at framework regions

  • Optimize CDR sequences to reduce hydrophobicity while maintaining binding

  • Apply computational tools to predict stability-enhancing mutations

  • Create libraries with stability-enhancing framework mutations

• Formulation optimization approaches:

  • Screen buffer compositions systematically

  • Test stabilizing excipients (sugars, amino acids, surfactants)

  • Optimize pH and ionic strength conditions

  • Develop lyophilization strategies for problematic antibodies

  • Implement high-throughput formulation screening methods

• Post-translational modification control:

  • Engineer glycosylation sites for improved stability

  • Develop strategies to reduce heterogeneity in glycoforms

  • Control oxidation-prone sites through sequence engineering

  • Minimize deamidation by avoiding NG sequences

  • Address lysine variants through process optimization

• Biophysical characterization-guided optimization:

  • Apply differential scanning calorimetry to identify thermal transition temperatures

  • Use size exclusion chromatography to monitor aggregation propensity

  • Implement light scattering techniques to assess colloidal stability

  • Develop accelerated stability studies to predict long-term stability

  • Apply these methods systematically as demonstrated in the development of antibodies with low aggregation propensity and high water solubility

These methodological approaches provide researchers with a systematic framework to address expression and stability challenges, ultimately improving the developability of therapeutic antibodies.

How can researchers effectively validate antibody specificity and minimize cross-reactivity?

Comprehensive methodological approaches for validating antibody specificity and minimizing cross-reactivity include:

• Multi-platform specificity testing:

  • Evaluate binding against panels of related proteins through ELISA

  • Perform surface plasmon resonance with multiple related antigens

  • Conduct immunofluorescence across multiple cell lines expressing related targets

  • Implement competitive binding assays with known ligands

  • Example: Immunofluorescence in six cell lines for 270 transcription factor antigens revealed ~70% cytosolic and ~20% nuclear staining patterns

• Epitope-focused engineering approach:

  • Map precise epitopes using peptide arrays or mutagenesis

  • Engineer antibodies to target unique, non-conserved epitopes

  • Use structural information to guide specificity optimization

  • Employ computational approaches to identify distinctive epitope regions

  • Apply alanine scanning to identify critical binding residues

• Negative selection strategies:

  • Implement subtractive panning approaches in phage display

  • Include closely related proteins as competitors during selection

  • Perform differential screening against target versus related proteins

  • Apply counter-selection steps to remove cross-reactive clones

  • Incorporate multiple rounds of negative selection

• Cross-reactivity characterization methods:

  • Test against comprehensive protein arrays

  • Evaluate binding to tissues from multiple species

  • Assess reactivity across different isoforms and splice variants

  • Implement high-throughput cross-reactivity screening panels

  • Characterize binding to post-translationally modified variants

• Application-specific validation:

  • Validate in the specific assay format intended for use

  • Implement knockout or knockdown controls to confirm specificity

  • Use competitive inhibition with recombinant proteins

  • Evaluate specificity under varied experimental conditions

  • Perform reciprocal validation with multiple antibodies to the same target

These methodological approaches provide a systematic framework for comprehensive specificity validation, ensuring that antibodies exhibit the required selectivity for their intended research and therapeutic applications.

What analytical methods can detect and characterize antibody aggregation during development?

Comprehensive analytical methodology for detecting and characterizing antibody aggregation includes:

• Size-based separation and detection:

  • Size Exclusion Chromatography (SEC): Separate aggregates based on hydrodynamic radius

  • Analytical Ultracentrifugation (AUC): Differentiate species based on sedimentation coefficient

  • Field Flow Fractionation (FFF): Separate aggregates in an open channel under laminar flow

  • Capillary Electrophoresis (CE): Separate based on size-to-charge ratio

  • Nanoparticle Tracking Analysis (NTA): Track Brownian motion of individual particles

• Light scattering techniques:

  • Dynamic Light Scattering (DLS): Measure hydrodynamic radius distribution

  • Static Light Scattering (SLS): Determine absolute molecular weight

  • Multi-Angle Light Scattering (MALS): Characterize size and conformation

  • Resonant Mass Measurement (RMM): Detect subvisible particles

  • These techniques can identify antibodies with low aggregation propensity, as demonstrated in HH01 characterization

• Spectroscopic methods:

  • Circular Dichroism (CD): Monitor secondary structure changes

  • Fourier Transform Infrared Spectroscopy (FTIR): Detect changes in protein backbone

  • Intrinsic/Extrinsic Fluorescence: Assess changes in tertiary structure

  • Raman Spectroscopy: Evaluate molecular vibrations sensitive to conformation

  • UV-Visible Spectroscopy: Monitor turbidity for large aggregates

• Thermal stability assessment:

  • Differential Scanning Calorimetry (DSC): Determine thermal transition temperatures

  • Differential Scanning Fluorimetry (DSF): Monitor protein unfolding using dyes

  • nano-DSF: Label-free thermal unfolding analysis based on intrinsic fluorescence

  • Isothermal Chemical Denaturation: Assess stability under constant temperature

  • Temperature ramping coupled with DLS: Monitor size changes with temperature

• Imaging and microscopy:

  • Transmission Electron Microscopy (TEM): Visualize aggregate morphology

  • Atomic Force Microscopy (AFM): Generate topographical images of aggregates

  • Flow Imaging Microscopy (FIM): Count and characterize subvisible particles

  • Microflow Imaging (MFI): Differentiate protein particles from other particulates

  • Confocal Microscopy with Fluorescent Dyes: Visualize protein aggregates in solution