Fulfilling the promise of antibody drugs
Grateful for support from...
Expanding the reach of antibodies to previously inaccessible targets
Antibodies are great drugs. Antibodies have many safety and efficacy advantages as drugs. Seven of the top ten selling drugs are antibodies, bringing in an median of $7B a year each.
Previously antibodies were hard to find for promising membrane protein targets. Finding antibodies that can bind GPCRs and ion channels is in itself a difficult task. Of the rare binders, finding antibodies that do more than block signal is harder.
We find functional antibodies for tough targets. We go beyond just blocking signals and find antibodies that activate targets (agonists) or turn down target activity (inverse agonists).
New disease treatments made possible. These functional antibodies affect cellular function in ways previously unachievable with other antibodies and small molecules, enabling treatments for diseases with unique modes of action.
Using Nature's search algorithm- survival of the fittest
Burn the haystack. How do we find "needle in the haystack" rare antibodies? Only the antibodies that we're interested in survive the biological enrichment process.
How? We create libraries of yeast cells expressing billions of antibody variants. We engineer the cells to grow better if they carry an antibody variant that activates or inhibits a drug target of interest expressed in the same cells.
We functionally interrogate up to a billion antibodies for function in a single experiment, orders of magnitude higher throughput than any other method.
Computational hit selection
We follow the evolution of antibody populations to find the best hits, using next-generation DNA sequencing. Computational algorithms select the best hits.
We then make recombinant protein versions of the hits and confirm activity in human cell assays.
Our platform also supercharges traditional antibody hit optimization by finding variants that improve function, not just binding affinity. Like conventional "affinity maturation", only better.
It gets better every experiment
With every experiment, we link data on thousands of antibody sequences to cellular functions.
Every experiment helps us attack the next drug target better and faster.
Using our platform, we are developing antibody drugs for pain, inflammatory diseases, rare cancer, and rare kidney disease. Our first lead for treatment of peripheral neuropathy is going into animal studies now.
Richard Yu / CEO
Ph.D., Molecular Biophysics and Biochemistry, Yale (2000)
BA, Molecular Cell Biology & Computer Science, UC Berkeley (1993)
This is Rich’s second startup with Gustavo. His systems and synthetic biology work as an Investigator at The Molecular Sciences Institute (MSI) underlies Abalone's platform and was published in Nature.
Rich also learned a lot about growing successful therapeutics startups as Scientific and Operations Director at MBC Biolabs, San Francisco's leading biotech incubator, and a Principal at Mission Bay Capital.
Gustavo Pesce / CSO
Ph.D., MSc in Genetics and Molecular Biology, University of Buenos Aires (1996)
After Gustavo did his postdoctoral studies at UCSF in Genetics and Cell Biology, he studied the systems and synthetic biology of signaling pathways as a Sr. Research Fellow at MSI. This work, published in Nature and Molecular Systems Biology, also underlies Abalone’s platform.
Gustavo has experience in industrial biotech and high throughput screening from his time with Amyris and Bolt Threads.
How'd they meet?
Rich and Gustavo met at Sydney Brenner’s MSI, the birthplace of modern synthetic biology, in 2003, where they spent ten years engineering and studying signal transduction in yeast. This work, published in multiple articles in Nature and MSB, underlies their core technology. Abalone Bio is their second startup in 17 years.
Why this startup?
A timely combination of:
Pharma need for drugs that only our platform can find
Uniquely appropriate combined technical and startup executive/ops skillsets
Convergence of technological advances in synthetic biology, ultra high-throughput DNA sequencing, and machine learning