BigHat Biosciences Completes First Stage of Research Collaboration With Amgen

BigHat’s AI-enabled experimental platform speeds the antibody engineering process to advance design and development of novel protein therapeutics 

SAN MATEO, CA - BigHat Biosciences, Inc., a biotechnology company with an artificial intelligence/machine learning -guided antibody discovery and development platform, today announced the successful completion of the first stage of a previously undisclosed research collaboration and licensing agreement with Amgen applying BigHat’s platform for multi-objective optimization of a next-generation antibody. 

BigHat’s antibody design platform integrates a high-speed characterization with AI/ML technologies to engineer antibodies with more complex functions and better biophysical properties. This approach reduces the difficulty of designing antibodies and other therapeutic proteins to tackle conditions ranging from chronic illness to life-threatening disease. BigHat’s experimental platform massively speeds up candidate discovery and validation.  

“This is an important milestone for BigHat, and the AI/ML biologics drug discovery field more broadly, as it demonstrates the ability of their platform to quickly and significantly optimize next-generation antibodies,” said Steve Doberstein, BigHat Independent Board Member and former Chief R&D Officer at Nektar Therapeutics, Inc.

Achievement of this first milestone, shows that BigHat’s platform has the potential to effectively and efficiently design high-quality therapeutic antibodies. Its platform can synthesize, express, purify, and characterize antibodies in a fraction of the time compared to traditional labs to guide the search for better molecules.   

“BigHat's platform for data-driven antibody design generated several antibodies significantly better than the starter molecules found using traditional technologies.,” added Vineeta Agarwala, MD, PhD, General Partner at Andreessen Horowitz and BigHat Board Director. “Now that Amgen has validated the capabilities of BigHat's unique approach to antibody development, we're excited to continue working with them towards a lead antibody for their discovery research.”

This successful milestone triggers the initiation of work to create a lead panel of VHH antibodies for patients in need. “We are excited to show the power of our platform to rapidly improve biophysical characteristics and function by directing and learning from each cycle of our AI/ML-enabled experimental platform. We are looking forward to continuing our productive collaboration” said Peyton Greenside, BigHat’s CSO and co-founder. 


JULIE BISHOP

julie@walkercomms.com
Cell: 631-291-3121

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Nice Insight

Nice Insight, established in 2010, is the research division of That’s Nice, A Science Agency, providing data and analysis from proprietary annual surveys, custom primary qualitative and quantitative research as well as extensive secondary research. Current annual surveys include The Nice Insight Contract Development & Manufacturing (CDMO/CMO), Survey The Nice Insight Contract Research - Preclinical and Clinical (CRO) Survey, The Nice Insight Pharmaceutical Equipment Survey, and The Nice Insight Pharmaceutical Excipients Survey.

BigHat Biosciences, Inc.

BigHat Biosciences is reimagining antibody discovery and engineering with an AI-first experimental platform that integrates a high-speed wet lab with machine learning to create better antibodies faster and undertake novel designs far beyond what’s possible today. BigHat applies these design capabilities to develop new generations of safer and more effective treatments for patients suffering from today’s most challenging diseases. BigHat is a Series A stage biotechnology company based in the San Francisco Bay Area with a team-oriented, inclusive, and family-friendly culture. BigHat is backed by a16z, 8VC, AME Cloud Ventures, and Innovation Endeavors and counts among its employees and advisors a Nobel Laureate, a serial life-science entrepreneur, and leaders in fields from antibody development to machine learning.

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