Totient Comes Out of Stealth with Novel COVID-19 Program and $10M in Seed Funding for AI-Driven Drug Discovery Platform

CAMBRIDGE, Mass., Sept. 10, 2020 /PRNewswire/ — Today, Totient, an AI-driven drug discovery company based in Cambridge, MA, emerged from stealth with $10M in seed funding and a strategic discovery partnership for COVID-19.

The company was founded by CEO Deniz Kural, PhD and CBO James Sietstra. Kural and Sietstra spun the platform out of leading biomedical data company Seven Bridges, which they also co-founded together. Their mission is to leverage their deep bioinformatics expertise to translate insights on natural immune responses and cancer heterogeneity into meaningful patient outcomes.

The Totient platform leverages tertiary lymphoid structures (TLSs) to identify novel tissue-specific antigens and develop matching high-affinity antibody therapeutics. Totient reconstructs antibodies from tissues affected by autoimmunity, infections, and cancer, collected from patients experiencing exceptional immune responses. Beginning with a population of over 50,000 samples, they have reconstructed antibodies against both known and novel extracellular oncology targets: GAGE1, GAGE2A, ANXA1, C4BPB, IL14A, BIRC7, HCLS1, GPR83, and others undisclosed. Totient’s human-derived antibodies are high affinity and highly specific to tissue-specific antigens, and are well suited for cell therapies, ADCs, and bispecifics.

“TLSs are a largely untapped source of naturally evolved, fully-human antibodies. Totient’s technology enables successful assembly of antibodies from TLSs across thousands of samples,” said Daniele Biasci, Totient VP of Immunology, on whose hypothesis the program was built.

Now, as the broader scientific community mobilizes to address the coronavirus pandemic, Totient has adapted its platform and approach to aid in the effort to discover COVID-19 antibodies, which could be used to prevent and treat the virus. They recently announced a collaboration with Ginkgo Bioworks to reconstruct anti-SARS-CoV-2 antibodies from bronchoalveolar lavage fluid (BALF) samples. Existing efforts focus on blood-derived antibodies, so focusing on BALF samples will enable Totient and Ginkgo to mine a previously unexplored source of therapeutic candidates.

Totient has attracted a diverse syndicate of investors, including Mission BioCapital, Sands Capital, Viva Biotech, Kaitai Capital, Tau Ventures, Jonathan Milner, and more.

“Totient is addressing an important challenge of novel tissue-specific target discovery,” said Steve Tregay, Managing Partner of Mission BioCapital. “They are doing so by combining the natural learning and evolution happening in the TLSs in the human body with machine learning techniques. One can imagine many applications for their validated technology beyond oncology and COVID-19.”

According to Jonathan Milner, co-founder and Deputy Chairman of Abcam, “Totient has demonstrated an ability to correctly assemble antibodies from bulk patient RNA sequencing data, without the need for specialized single-cell or antibody sequencing. The approach enables Totient to express and screen antibodies from large and noisy archival datasets, without prior target knowledge.”

Totient intends to advance a subset of their proprietary antibodies into the clinic while pursuing a broad and flexible partnering strategy for other candidates in parallel.

About Totient
Totient is an AI-driven biotechnology company that leverages tertiary lymphoid structures (TLSs) to identify novel tissue-specific antigens and develop matching high-affinity antibody therapeutics. Totient uses machine learning and immunoinformatics technology to pull critical insights from large, complex datasets, to quickly and accurately assemble the most potent antibody candidates. The unique applicability of the Totient platform allows the company to remain flexible and develop therapeutics for cancer, autoimmune disease, infections, and viruses. For more information, visit www.totient.bio.

Contact:
James Sietstra
james@totient.bio

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SOURCE Ginkgo Bioworks

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