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Elucidating Novel Targets for Ovarian Cancer Antibody–Drug Conjugate Development: Integrating In Silico Prediction and Surface Plasmon Resonance to Identify Targets with Enhanced Antibody Internalization Capacity

Emenike Kenechi Onyido Orcid Logo, David James Orcid Logo, Jezabel Garcia-Parra Orcid Logo, John Sinfield, Anna Moberg, Zoe Coombes Orcid Logo, Jenny Worthington, Nicole Williams, Lewis Webb Francis, Steve Conlan Orcid Logo, Deya Gonzalez Orcid Logo

Antibodies, Volume: 12, Issue: 4, Start page: 65

Swansea University Authors: Steve Conlan Orcid Logo, Deya Gonzalez Orcid Logo

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DOI (Published version): 10.3390/antib12040065

Abstract

Antibody–drug conjugates (ADCs) constitute a rapidly expanding category of biopharmaceuticals that are reshaping the landscape of targeted chemotherapy. The meticulous process of selecting therapeutic targets, aided by specific monoclonal antibodies’ high specificity for binding to designated antige...

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Published in: Antibodies
ISSN: 2073-4468
Published: MDPI AG 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa65401
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The meticulous process of selecting therapeutic targets, aided by specific monoclonal antibodies’ high specificity for binding to designated antigenic epitopes, is pivotal in ADC research and development. Despite ADCs’ intrinsic ability to differentiate between healthy and cancerous cells, developmental challenges persist. In this study, we present a rationalized pipeline encompassing the initial phases of the ADC development, including target identification and validation. Leveraging an in-house, computationally constructed ADC target database, termed ADC Target Vault, we identified a set of novel ovarian cancer targets. We effectively demonstrate the efficacy of Surface Plasmon Resonance (SPR) technology and in vitro models as predictive tools, expediting the selection and validation of targets as ADC candidates for ovarian cancer therapy. Our analysis reveals three novel robust antibody/target pairs with strong binding and favourable antibody internalization rates in both wild-type and cisplatin-resistant ovarian cancer cell lines. This approach enhances ADC development and offers a comprehensive method for assessing target/antibody combinations and pre-payload conjugation biological activity. 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spelling v2 65401 2024-01-04 Elucidating Novel Targets for Ovarian Cancer Antibody–Drug Conjugate Development: Integrating In Silico Prediction and Surface Plasmon Resonance to Identify Targets with Enhanced Antibody Internalization Capacity 0bb6bd247e32fb4249de62c0013b51cb 0000-0002-2562-3461 Steve Conlan Steve Conlan true false bafdf635eb81280304eedf4b18e65d4e 0000-0002-1838-6752 Deya Gonzalez Deya Gonzalez true false 2024-01-04 BMS Antibody–drug conjugates (ADCs) constitute a rapidly expanding category of biopharmaceuticals that are reshaping the landscape of targeted chemotherapy. The meticulous process of selecting therapeutic targets, aided by specific monoclonal antibodies’ high specificity for binding to designated antigenic epitopes, is pivotal in ADC research and development. Despite ADCs’ intrinsic ability to differentiate between healthy and cancerous cells, developmental challenges persist. In this study, we present a rationalized pipeline encompassing the initial phases of the ADC development, including target identification and validation. Leveraging an in-house, computationally constructed ADC target database, termed ADC Target Vault, we identified a set of novel ovarian cancer targets. We effectively demonstrate the efficacy of Surface Plasmon Resonance (SPR) technology and in vitro models as predictive tools, expediting the selection and validation of targets as ADC candidates for ovarian cancer therapy. Our analysis reveals three novel robust antibody/target pairs with strong binding and favourable antibody internalization rates in both wild-type and cisplatin-resistant ovarian cancer cell lines. This approach enhances ADC development and offers a comprehensive method for assessing target/antibody combinations and pre-payload conjugation biological activity. Additionally, the strategy establishes a robust platform for high-throughput screening of potential ovarian cancer ADC targets, an approach that is equally applicable to other cancer types. Journal Article Antibodies 12 4 65 MDPI AG 2073-4468 ovarian cancer; antibody–drug conjugates; bioinformatics; in silico; biomarkers; therapeutics; internalization; SPR; Biacore 16 10 2023 2023-10-16 10.3390/antib12040065 COLLEGE NANME Biomedical Sciences COLLEGE CODE BMS Swansea University Another institution paid the OA fee This research was funded by the Welsh Government through the SMARTExpertise Programme, grant Cluster for Epigenomic and ADC Therapeutics (CEAT) project (2017/COL/004). 2024-03-21T15:21:09.9083120 2024-01-04T10:23:25.2259128 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Biomedical Science Emenike Kenechi Onyido 0000-0001-9586-6437 1 David James 0000-0003-3951-9187 2 Jezabel Garcia-Parra 0000-0002-4235-4427 3 John Sinfield 4 Anna Moberg 5 Zoe Coombes 0000-0002-9614-8127 6 Jenny Worthington 7 Nicole Williams 8 Lewis Webb Francis 9 Steve Conlan 0000-0002-2562-3461 10 Deya Gonzalez 0000-0002-1838-6752 11 65401__29403__f5700b24a8c449c6816098e158308d58.pdf 65401.pdf 2024-01-08T11:12:58.3714527 Output 8837799 application/pdf Version of Record true © 2023 by the authors.This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. true eng https://creativecommons.org/licenses/by/4.0/
title Elucidating Novel Targets for Ovarian Cancer Antibody–Drug Conjugate Development: Integrating In Silico Prediction and Surface Plasmon Resonance to Identify Targets with Enhanced Antibody Internalization Capacity
spellingShingle Elucidating Novel Targets for Ovarian Cancer Antibody–Drug Conjugate Development: Integrating In Silico Prediction and Surface Plasmon Resonance to Identify Targets with Enhanced Antibody Internalization Capacity
Steve Conlan
Deya Gonzalez
title_short Elucidating Novel Targets for Ovarian Cancer Antibody–Drug Conjugate Development: Integrating In Silico Prediction and Surface Plasmon Resonance to Identify Targets with Enhanced Antibody Internalization Capacity
title_full Elucidating Novel Targets for Ovarian Cancer Antibody–Drug Conjugate Development: Integrating In Silico Prediction and Surface Plasmon Resonance to Identify Targets with Enhanced Antibody Internalization Capacity
title_fullStr Elucidating Novel Targets for Ovarian Cancer Antibody–Drug Conjugate Development: Integrating In Silico Prediction and Surface Plasmon Resonance to Identify Targets with Enhanced Antibody Internalization Capacity
title_full_unstemmed Elucidating Novel Targets for Ovarian Cancer Antibody–Drug Conjugate Development: Integrating In Silico Prediction and Surface Plasmon Resonance to Identify Targets with Enhanced Antibody Internalization Capacity
title_sort Elucidating Novel Targets for Ovarian Cancer Antibody–Drug Conjugate Development: Integrating In Silico Prediction and Surface Plasmon Resonance to Identify Targets with Enhanced Antibody Internalization Capacity
author_id_str_mv 0bb6bd247e32fb4249de62c0013b51cb
bafdf635eb81280304eedf4b18e65d4e
author_id_fullname_str_mv 0bb6bd247e32fb4249de62c0013b51cb_***_Steve Conlan
bafdf635eb81280304eedf4b18e65d4e_***_Deya Gonzalez
author Steve Conlan
Deya Gonzalez
author2 Emenike Kenechi Onyido
David James
Jezabel Garcia-Parra
John Sinfield
Anna Moberg
Zoe Coombes
Jenny Worthington
Nicole Williams
Lewis Webb Francis
Steve Conlan
Deya Gonzalez
format Journal article
container_title Antibodies
container_volume 12
container_issue 4
container_start_page 65
publishDate 2023
institution Swansea University
issn 2073-4468
doi_str_mv 10.3390/antib12040065
publisher MDPI AG
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Biomedical Science{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Biomedical Science
document_store_str 1
active_str 0
description Antibody–drug conjugates (ADCs) constitute a rapidly expanding category of biopharmaceuticals that are reshaping the landscape of targeted chemotherapy. The meticulous process of selecting therapeutic targets, aided by specific monoclonal antibodies’ high specificity for binding to designated antigenic epitopes, is pivotal in ADC research and development. Despite ADCs’ intrinsic ability to differentiate between healthy and cancerous cells, developmental challenges persist. In this study, we present a rationalized pipeline encompassing the initial phases of the ADC development, including target identification and validation. Leveraging an in-house, computationally constructed ADC target database, termed ADC Target Vault, we identified a set of novel ovarian cancer targets. We effectively demonstrate the efficacy of Surface Plasmon Resonance (SPR) technology and in vitro models as predictive tools, expediting the selection and validation of targets as ADC candidates for ovarian cancer therapy. Our analysis reveals three novel robust antibody/target pairs with strong binding and favourable antibody internalization rates in both wild-type and cisplatin-resistant ovarian cancer cell lines. This approach enhances ADC development and offers a comprehensive method for assessing target/antibody combinations and pre-payload conjugation biological activity. Additionally, the strategy establishes a robust platform for high-throughput screening of potential ovarian cancer ADC targets, an approach that is equally applicable to other cancer types.
published_date 2023-10-16T15:21:10Z
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