In the expanding frontier of cancer immunotherapy, bispecific natural killer cell engagers (NKCEs) have emerged as powerful tools for directing the immune system to attack tumors. But engineering these complex therapeutics is far from straightforward—especially when targeting highly homologous immune receptors like CD16a, which shares over 98% sequence identity with its near-twin, CD16b.

AvantGen’s latest white paper details how their Germliner™ Library Collection and proprietary yeast display platform are transforming the speed and precision of antibody discovery, enabling the development of selective, potent, and highly developable CD16a-targeting antibodies in record time.

 

The Challenge: Precision Targeting in a Crowded Molecular Landscape

CD16a is a prime NK cell target for bispecific antibody design due to its role in mediating antibody-dependent cellular cytotoxicity (ADCC) and cytokine production. However, CD16a’s high similarity to CD16b—a receptor on neutrophils known to suppress ADCC—has long posed a barrier to therapeutic development. Cross-reactivity risks are compounded by the need for antibodies to bind multiple CD16a alleles (176V and 176F) and to show activity in preclinical models like cynomolgus monkeys.

Conventional immunization or phage display platforms struggle with this level of precision, often producing candidates with weak specificity, low stability, or poor manufacturability.

 

AvantGen’s Solution: Germliner™ + AvantSabre = Next-Gen Antibody Discovery

AvantGen’s breakthrough lies in combining the Germliner™ Library Collection—a rationally designed, fully human Fab library of over 100 billion clones—with the AvantSabre yeast display platform, which mimics mammalian protein folding and expression more faithfully than bacterial systems.

Key innovations include:

• Natural Sequence Biasing: Library design informed by >500 human antibody repertoires ensures high developability and low immunogenicity.

• Yeast-Based Selection: Enables robust screening with high biological relevance, overcoming common pitfalls of phage display.

• Streamlined Workflow: From 100 billion clones to 5 clinical-grade antibody candidates in under 4 months.

 

Case Study: Selective CD16a mAbs Identified in Weeks

Using the Germliner™ platform, AvantGen’s team successfully isolated five anti-CD16a monoclonal antibodies with:

• High affinity for CD16a-176V, CD16a-176F, and cyno-CD16a

• No detectable binding to CD16b alleles (NA1, NA2)

• Low immunogenicity, high thermal stability, and favorable expression in CHO cells

• Single-residue specificity via interaction with tyrosine at position 158

These antibodies outperformed a clinical-stage reference antibody in binding strength and resilience under physiological IgG concentrations.

 

Functionality: Bispecific Format Proves Potency

The selected antibodies were formatted into bispecific constructs targeting both CD16a and CD19. In both tandem scFv and bivalent tandem scFv-IgG4 formats, clones A2-1 and A2-3 demonstrated:

• Superior activation of CD16a-expressing Jurkat cells in the presence of CD19+ cancer cells

• High potency under physiological IgG levels, where competitor constructs failed

• EC50 values as low as 5 pM in NK cell cytotoxicity assays, significantly outperforming reference molecules

 

Conclusion: A Paradigm Shift in Antibody Discovery

The results from AvantGen’s Germliner™ approach not only validate the platform’s utility for high-risk targets like CD16a but also highlight its potential for broader application in bispecific and multispecific therapeutic design.

By compressing the discovery timeline to mere months and pre-optimizing candidates for clinical translation, AvantGen is setting a new standard in antibody engineering.

For organizations seeking to accelerate antibody development against complex targets, Germliner™ offers a compelling path forward—where speed, specificity, and scalability converge.

📩 To learn more or collaborate, contact: partner@avantgen.com
🔗 Watch the full presentation: Webinar Recording
🌐 Visit: www.avantgen.com

 

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