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Intelligent in silico antibody library design and optimization for therapeutic use

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 6R43GM140499-02
Agency Tracking Number: R43GM140499
Amount: $244,049.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: 400
Solicitation Number: PA19-272
Timeline
Solicitation Year: 2019
Award Year: 2021
Award Start Date (Proposal Award Date): 2021-02-01
Award End Date (Contract End Date): 2022-07-31
Small Business Information
3030 BUNKER HILL ST STE 218
San Diego, CA 92109-5754
United States
DUNS: 031092123
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 NATALIE CASTELLANA
 (619) 752-4821
 natalie@digitalproteomics.com
Business Contact
 NATALIE CASTELLANA
Phone: (888) 416-9305
Email: natalie@abterrabio.com
Research Institution
N/A
Abstract

Antibody discovery technologies have delivered life saving therapies over the past three
decades, however, many challenging targets remain undrugged due to limitations of
these technologies. Phage display technology presents a solution to discovering antibodies to
targets that do not elicit a strong immune response in animals, are toxic, or are difficult to
produce in large quantities in the correct conformation. Synthetic libraries, in which antibodies
are designed in silico, are a promising approach that does not rely on animal immunizations or
donors. However, most synthetic libraries in use today were constructed by fixing certain
regions of the antibody while generating random sequences from position-specific amino acid
frequencies for antigen-binding regions. This departure from the space of natural antibodies
results in antibodies with poor biophysical characteristics such as low melting temperature,
aggregation propensity, or general ‘stickiness’.Digital Proteomics has developed a computational workflow for natural antibody
repertoire analysis, harnessing the throughput of next-generation sequencing of immune cells,
that will be used to design a better synthetic library for therapeutic antibody discovery. Rules of
natural antibody development will be incorporated into the design of a synthetic antibody library,
thereby retaining the biophysical property profile of natural antibodies. Antibody genes develop
through a sequential process involving site-specific genome recombination and somatic
hypermutation, which will be modeled in silico. While in conventional antibodies, this process
occurs at two independent loci (a heavy and light chain that must interact), the proof-of-concept
will be applied to the single chain antibodies produced by camelids. Using an improved
synthetic library framework, the computational design of an antibody library optimized for
therapeutic discovery will contain antibodies that display superior biophysical properties and
reduced sequence liabilities. The library will be an invaluable tool for rapid discovery of
therapeutic antibodies to a wide range of diseases.Mining large antibody libraries enables the rapid discovery of therapeutics against challenging
targets, however, current libraries have had limited success in delivering antibody therapeutics
due to poor biophysical characteristics such as low melting temperatures, aggregation
propensity, or general ‘stickiness’. We propose the computational design of a better library that
uses an in silico model of natural antibody development. Combined with computational filters to
remove liabilities that preclude development as a therapeutic, our synthetic library will deliver
antibodies to treat a myriad of conditions including cancer, infectious diseases, and autoimmune
diseases.

* Information listed above is at the time of submission. *

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