Phenotypic antibody discovery and mining of complex antibody libraries
(2019)- Abstract
- Therapeutic antibodies are the fastest growing class of drugs but suffer from a crowded target-space where many antibodies, against a few targets, are developed in parallel. The aim of this thesis is to find methods to enable discovery of novel antibody-target combinations and thereby increase the target-space. A strategy to achieve this is to use phage display antibody libraries and selection on whole cells in a phenotypic antibody discovery process, where antibodies with the desired function are isolated without knowing the target. Thereafter the targets for the functionally interesting antibodies are determined. In theory, this strategy allows discovery of antibodies against all receptors on the cell surface (potential targets).... (More)
- Therapeutic antibodies are the fastest growing class of drugs but suffer from a crowded target-space where many antibodies, against a few targets, are developed in parallel. The aim of this thesis is to find methods to enable discovery of novel antibody-target combinations and thereby increase the target-space. A strategy to achieve this is to use phage display antibody libraries and selection on whole cells in a phenotypic antibody discovery process, where antibodies with the desired function are isolated without knowing the target. Thereafter the targets for the functionally interesting antibodies are determined. In theory, this strategy allows discovery of antibodies against all receptors on the cell surface (potential targets). However, typically only the most abundant antibodies binding a few, commonly highly expressed, targets are identified. Methods to mine the generated antibody pools to allow discovery also of rare antibodies are therefore needed.
In this thesis, I establish a phenotypic antibody discovery platform and apply it on primary cancer cells from patients with chronic lymphocytic leukemia, CLL. Novel antibody-target combinations, with enhanced cytotoxicity, both in vitro and in vivo, compared to the standard of care were discovered. Furthermore, I showed that by applying various deep mining methods on the generated antibody pool, additional antibodies could be discovered. These rare antibodies bound new epitopes on the target cells, either on previously discovered, or on entirely new targets. In addition, I demonstrate through analysis by next generation sequencing, NGS, that the number of receptors on the cell surface has a major impact on antibody enrichment in the selection process. By following clonal enrichment with NGS, both abundant and rare antibodies were discovered. This allows the conventional immunochemical screening of a phage display antibody discovery process to be by-passed.
The discovered antibodies can be used in many different applications. For some of these applications, bispecific antibodies, designed to bind two different antigens, can have improved efficacy through e.g. increased specificity compared to conventional monospecific antibodies. However, many of the current bispecific formats, especially those similar to a conventional IgG, are difficult to construct. In this thesis, I create a new type of bispecific antibody, the Tetra-VH IgG. This antibody format contains four variable heavy (VH) domains, is tetravalent, and binds two antigens simultaneously on each Fab arm. The Tetra-VH IgGs potentially show enhanced binding and functional properties compared to conventional bivalent IgGs.
In summary, the technologies I describe in this thesis will be important in future antibody discovery efforts as they enable a broad repertoire of antibodies against novel targets, of both high and low receptor density, to be discovered and used for treatment of various diseases.
(Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/5794c8b8-eb52-44c4-9e19-f5c20163a3a6
- author
- Ljungars, Anne LU
- supervisor
-
- Mats Ohlin LU
- opponent
-
- Sir. Winter, Gregory., University of Cambridge, UK.
- organization
- publishing date
- 2019-12-19
- type
- Thesis
- publication status
- published
- subject
- keywords
- Antibody discovery, Phage display, Phenotypic screening, Cell selection, Deep mining, Complex phage pools
- pages
- 205 pages
- publisher
- Department of Immunotechnology, Lund University
- defense location
- Lecture hall MA2, Matteannexet, Sölvegatan 20, Faculty of Engineering LTH, Lund University, Lund.
- defense date
- 2020-01-24 09:15:00
- ISBN
- 978-91-7895-349-3
- 978-91-7895-348-6
- language
- English
- LU publication?
- yes
- id
- 5794c8b8-eb52-44c4-9e19-f5c20163a3a6
- date added to LUP
- 2019-12-12 19:27:05
- date last changed
- 2024-02-13 10:53:09
@phdthesis{5794c8b8-eb52-44c4-9e19-f5c20163a3a6,
abstract = {{Therapeutic antibodies are the fastest growing class of drugs but suffer from a crowded target-space where many antibodies, against a few targets, are developed in parallel. The aim of this thesis is to find methods to enable discovery of novel antibody-target combinations and thereby increase the target-space. A strategy to achieve this is to use phage display antibody libraries and selection on whole cells in a phenotypic antibody discovery process, where antibodies with the desired function are isolated without knowing the target. Thereafter the targets for the functionally interesting antibodies are determined. In theory, this strategy allows discovery of antibodies against all receptors on the cell surface (potential targets). However, typically only the most abundant antibodies binding a few, commonly highly expressed, targets are identified. Methods to mine the generated antibody pools to allow discovery also of rare antibodies are therefore needed.<br/>In this thesis, I establish a phenotypic antibody discovery platform and apply it on primary cancer cells from patients with chronic lymphocytic leukemia, CLL. Novel antibody-target combinations, with enhanced cytotoxicity, both in vitro and in vivo, compared to the standard of care were discovered. Furthermore, I showed that by applying various deep mining methods on the generated antibody pool, additional antibodies could be discovered. These rare antibodies bound new epitopes on the target cells, either on previously discovered, or on entirely new targets. In addition, I demonstrate through analysis by next generation sequencing, NGS, that the number of receptors on the cell surface has a major impact on antibody enrichment in the selection process. By following clonal enrichment with NGS, both abundant and rare antibodies were discovered. This allows the conventional immunochemical screening of a phage display antibody discovery process to be by-passed.<br/>The discovered antibodies can be used in many different applications. For some of these applications, bispecific antibodies, designed to bind two different antigens, can have improved efficacy through e.g. increased specificity compared to conventional monospecific antibodies. However, many of the current bispecific formats, especially those similar to a conventional IgG, are difficult to construct. In this thesis, I create a new type of bispecific antibody, the Tetra-VH IgG. This antibody format contains four variable heavy (VH) domains, is tetravalent, and binds two antigens simultaneously on each Fab arm. The Tetra-VH IgGs potentially show enhanced binding and functional properties compared to conventional bivalent IgGs.<br/>In summary, the technologies I describe in this thesis will be important in future antibody discovery efforts as they enable a broad repertoire of antibodies against novel targets, of both high and low receptor density, to be discovered and used for treatment of various diseases.<br/>}},
author = {{Ljungars, Anne}},
isbn = {{978-91-7895-349-3}},
keywords = {{Antibody discovery; Phage display; Phenotypic screening; Cell selection; Deep mining; Complex phage pools}},
language = {{eng}},
month = {{12}},
publisher = {{Department of Immunotechnology, Lund University}},
school = {{Lund University}},
title = {{Phenotypic antibody discovery and mining of complex antibody libraries}},
year = {{2019}},
}