Parental Fab liabilities can dictate the fate of final bispecific assembly, often guided by the “Rule of Additivity.” This webinar explores how parental mAb profiling and a systematic bispecific library can clarify which attributes transfer to bsAbs and which liabilities emerge after assembly.
Using a diversity-maximizing approach and profiling parental mAbs, researchers rationally designed and expressed a 160-member bispecific library. By applying an antibody design and analysis platform, they generated a high-dimensional dataset to identify where parental inheritance holds versus where it breaks down.
The findings shed light on the limits of linear additivity, characterizing emergent liabilities that escape standard predictions. This webinar will highlight key correlations and significant decouplings between Fab-arm and bsAb attributes, supporting a more nuanced roadmap for de-risking complex biologics.
Register for this webinar to learn how bsAb developability data can help predict pairing risk, identify emergent liabilities and de-risk complex biologics earlier.
Speaker
Ammar Arsiwala, Director, Antibody Developability, Ginkgo Datapoints
Dr. Ammar Arsiwala is the Architect and Technical Lead of Ginkgo Bioworks’ high-throughput antibody developability platform, where he leads the integration of reconfigurable automation and machine learning to revolutionize antibody production and characterization. With over a decade of experience bridging early-stage research and late-stage CMC, he currently drives the development of “lab-in-the-loop” infrastructures that power generative AI design engines. Ammar holds a PhD from the Ravi Kane Lab, Georgia Tech, specializing in Vaccine and Antibody Therapeutics Engineering.
Message PresenterWho Should Attend?
This webinar will appeal to:
- Scientists and Engineers at biotech or pharma companies actively designing or optimizing bsAb or multispecific programs
- VPs or Heads of Antibody Sciences at mid-to-large pharma companies
- ML Engineers and Computational Scientists building predictive development models
What You Will Learn
Attendees will gain insight into:
- How parental Fab liabilities can influence final bispecific assembly and bsAb developability
- Where the Rule of Additivity supports prediction and where it breaks down
- How a 160-member bispecific library provides empirical insight into parental screening reliability
- How systematic developability data can help predict pairings, flag risks and de-risk complex biologics earlier
Xtalks Partner
Ginkgo Datapoints
Ginkgo Datapoints is an outsourced data-generation business built on Ginkgo Bioworks’ automation infrastructure as a purpose-built experimental data provider for AI-driven drug discovery. We generate experimental data for drug discovery programs at the throughput, structure, and turnaround times that modern, iterative teams actually need. To date, we’ve generated over 2.5 billion omics datapoints and more than 100,000 antibody datapoints. Built by robots, not by hand, our capabilities span antibody developability, perturbation response profiling, specialized high-throughput screening, and small molecule ADME, delivering structured, ML-ready data returns from validated, automation-driven methods.
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