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基于人工智能的抗体发现平台识别出针对多种新冠病毒毒株的新型、多样且具有药理活性的治疗性抗体。

AI-based antibody discovery platform identifies novel, diverse, and pharmacologically active therapeutic antibodies against multiple SARS-CoV-2 strains.

作者信息

Loomis Cristina Moldovan, Lahlali Thomas, Van Citters Danielle, Sprague Megan, Neveu Gregory, Somody Laurence, Siska Christine C, Deming Derrick, Asakawa Andrew J, Amimeur Tileli, Shaver Jeremy M, Carbonelle Caroline, Ketchem Randal R, Alam Antoine, Clark Rutilio H

机构信息

Department of Discovery & Molecular Design, Just-Evotec Biologics Inc., 401 Terry Avenue N., Seattle, WA 98109, USA.

Department of Virology, Evotec ID, 40, Avenue Tony Garnier, 69007 Lyon, France.

出版信息

Antib Ther. 2024 Sep 26;7(4):307-323. doi: 10.1093/abt/tbae025. eCollection 2024 Oct.

DOI:10.1093/abt/tbae025
PMID:39381135
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11456866/
Abstract

BACKGROUND

We are entering a new era of antibody discovery and optimization where machine learning (ML) processes will become indispensable for the design and development of therapeutics.

METHODS

We have constructed a Humanoid Antibody Library for the discovery of therapeutics that is an initial step towards leveraging the utility of artificial intelligence and ML. We describe how we began our validation of the library for antibody discovery by isolating antibodies against a target of pandemic concern, SARS-CoV-2. The two main antibody quality aspects that we focused on were functional and biophysical characterization.

RESULTS

The applicability of our platform for effective therapeutic antibody discovery is demonstrated here with the identification of a panel of human monoclonal antibodies that are novel, diverse, and pharmacologically active.

CONCLUSIONS

These first-generation antibodies, without the need for affinity maturation, exhibited neutralization of SARS-CoV-2 viral infectivity across multiple strains and indicated high developability potential.

摘要

背景

我们正进入抗体发现与优化的新时代，在此时代，机器学习（ML）过程对于治疗药物的设计与开发将变得不可或缺。

方法

我们构建了一个用于治疗药物发现的类人抗体库，这是利用人工智能和机器学习效用的第一步。我们描述了我们如何通过分离针对大流行关注靶点严重急性呼吸综合征冠状病毒2（SARS-CoV-2）的抗体来开始对该库进行抗体发现验证。我们关注的两个主要抗体质量方面是功能和生物物理特性。

结果

通过鉴定一组新型、多样且具有药理活性的人单克隆抗体，证明了我们的平台在有效治疗性抗体发现中的适用性。

结论

这些第一代抗体无需亲和力成熟，对多种毒株均表现出对SARS-CoV-2病毒感染性的中和作用，并显示出很高的开发潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c936/11456866/78a93a14ab2e/tbae025f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c936/11456866/e340703a6858/tbae025f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c936/11456866/d5ed8f2d5b99/tbae025f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c936/11456866/ba17501c5475/tbae025f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c936/11456866/0aec8f3207c0/tbae025f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c936/11456866/1cd9c0942136/tbae025f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c936/11456866/73f6e31af5a7/tbae025f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c936/11456866/78a93a14ab2e/tbae025f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c936/11456866/e340703a6858/tbae025f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c936/11456866/d5ed8f2d5b99/tbae025f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c936/11456866/ba17501c5475/tbae025f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c936/11456866/0aec8f3207c0/tbae025f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c936/11456866/1cd9c0942136/tbae025f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c936/11456866/73f6e31af5a7/tbae025f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c936/11456866/78a93a14ab2e/tbae025f7.jpg

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