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Biologics developability data analysis using hierarchical clustering accelerates candidate lead selection, optimization, and preformulation screening

delete2025-05-10
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OA
AI
K
Kevin J. Metcalf
G
Galen Wo
J
Jan Paulo T. Zaragoza
F
Fahimeh Raoufi
J
Jeanne Baker
D
Daoyang Chen
M
M.G. Derebe
J
Jason M. Hogan
A
Amy Hsu
E
Esther Kofman
D
David A. Leigh
M
Mandy Li *
D
Dan Malashock
C
Cate Mann
S
Soha Motlagh
J
Jihea Park
K
Karthik Sathiyamoorthy *
M
Madhura Shidhore
Y
Yinyan Tang
K
Kevin Teng
K
Katharine Williams
A
Andrew B. Waight
S
Sultan Yilmaz *
张
张帆 (Fan Zhang)
H
Huimin Zhong
L
Laurence Fayadat‐Dilman *
M
Marc Bailly *
DOI：10.1080/19420862.2025.2502127delete
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摘要

摘要

En 中文
Identification of an optimal single protein sequence at the discovery stage for preclinical and clinical development is critical to the rapid development and overall success of a biologic drug. High throughput developability assessments at the discovery stage are used to rank potent molecules by their biophysical properties, deprioritize suboptimal molecules, or trigger additional rounds of protein engineering. Due to the amount of data acquired for these molecules, manual analysis methods to rank molecules are error prone and time-consuming. Here, we present applications of hierarchical clustering analysis for data-driven lead selection of biologics and preformulation screening using high throughput developability data. Hierarchical clustering analysis was applied here for prioritization of three different antibody modalities, including format and chain pairing of bispecific antibodies, sequence-optimized monoclonal antibodies from affinity maturation, preformulation screening of bispecific scFv-Fab fusion molecules, and monoclonal antibodies from an immunization campaign. This high-throughput method for ranking molecules by their developability characteristics and preformulation properties can substantially simplify, streamline, and accelerate biologics discovery and early development.
Keyword:
Antibody discovery
antibody screening
biologics
biophysical properties
CMC
data-driven decision making
developability
hierarchical-clustering analysis
lead selection
manufacturability
monoclonal antibodies
preformulation
protein engineering
statistical analysis
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1.8K
被引数:
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Xu Y, Wang D, Mason B, Rossomando T, Li N, Liu D, Cheung JK, Xu W, Raghava S, Katiyar A, et al. Structure, heterogeneity and developability assessment of therapeutic antibodies. Mabs-austin. 2019;11(2):239–264. doi: 10.1080/19420862.2018.1553476. (Open in a new window)PubMed (Open in a new window)Web of Science ®(Open in a new window)Google Scholar徐毅, 王德, 梅森 B, 罗索曼诺 T, 李宁, 刘东, 张敬坤, 许伟, 拉格瓦 S, 卡蒂尔 A, 等. 治疗性抗体的结构、异质性和可开发性评估. 抗体药物-austin. 2019;11(2):239–264. doi: 10.1080/19420862.2018.1553476.
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Koscher BA, Canty RB, McDonald MA, Greenman KP, McGill CJ, Bilodeau CL, Jin W, Wu H, Vermeire FH, Jin B, et al. Autonomous, multiproperty-driven molecular discovery: from predictions to measurements and back. Science. 2023;382(6677):eadi1407. doi: 10.1126/science.adi1407. (Open in a new window)PubMed (Open in a new window)Web of Science ®(Open in a new window)Google Scholar科舍尔·BA、坎蒂·RB、麦克唐纳·MA、格林曼·KP、麦克吉尔·CJ、比洛杜·CL、金·W、吴·H、费尔梅雷·FH、金·B等。自主的、多属性驱动的分子发现：从预测到测量及再反馈。科学。2023；382（6677）：eadi1407。doi：10.1126/science.adi1407.（在新窗口中打开）PubMed（在新窗口中打开）Web of Science ®（在新窗口中打开）谷歌学术
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Georgiou G, Ippolito GC, Beausang J, Busse CE, Wardemann H, Quake SR. The promise and challenge of high-throughput sequencing of the antibody repertoire. Nat Biotechnol. 2014;32(2):158–168. doi: 10.1038/nbt.2782. (Open in a new window)PubMed (Open in a new window)Web of Science ®(Open in a new window)Google ScholarGeorgiou G, Ippolito GC, Beausang J, Busse CE, Wardemann H, Quake SR. 抗体库高通量测序的潜力和挑战。Nat Biotechnol. 2014;32(2):158–168. doi: 10.1038/nbt.2782. (在新窗口中打开)PubMed (在新窗口中打开)Web of Science ®(在新窗口中打开)Google Scholar
err
IF0
err
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PREAI
err
err分享
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Raybould MIJ, Marks C, Krawczyk K, Taddese B, Nowak J, Lewis AP, Bujotzek A, Shi J, Deane CM. Five computational developability guidelines for therapeutic antibody profiling. Proc Natl Acad Sci USA. 2019;116(10):4025–4030. doi: 10.1073/pnas.1810576116. (Open in a new window)PubMed (Open in a new window)Web of Science ®(Open in a new window)Google ScholarRaybould MIJ，Marks C，Krawczyk K，Taddese B，Nowak J，Lewis AP，Bujotzek A，Shi J，Deane CM. 五条计算可开发性指导原则用于治疗性抗体分析。美国国家科学院院刊. 2019;116(10):4025–4030. doi: 10.1073/pnas.1810576116. (在新窗口中打开)PubMed (在新窗口中打开)Web of Science ®(在新窗口中打开)Google Scholar
err
IF0
err
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PREAI
err
err分享
err收藏
Mieczkowski C, Zhang X, Lee D, Nguyen K, Lv W, Wang Y, Zhang Y, Way J, Gries J-M. Blueprint for antibody biologics developability. Mabs-austin. 2023;15(1):2185924. doi: 10.1080/19420862.2023.2185924. (Open in a new window)PubMed (Open in a new window)Web of Science ®(Open in a new window)Google ScholarMieczkowski C，张X，李D，Nguyen K，吕W，王Y，张Y，Way J，Gries J-M. 抗体生物制剂开发性蓝图。Mabs-austin. 2023;15(1):2185924. doi: 10.1080/19420862.2023.2185924. (在新窗口中打开)PubMed (在新窗口中打开)Web of Science ®(在新窗口中打开)Google Scholar
err
IF0
err
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PREAI
err
err分享
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Segall M. Multi-parameter optimization: identifying high quality compounds with a balance of properties. Curr Pharm Des. 2012;18(9):1292–1310. doi: 10.2174/138161212799436430. (Open in a new window)PubMed (Open in a new window)Web of Science ®(Open in a new window)Google ScholarSegall M. 多参数优化：识别具有平衡性质的高质量化合物。Curr Pharm Des. 2012;18(9):1292–1310. doi: 10.2174/138161212799436430. (在新窗口中打开)PubMed (在新窗口中打开)Web of Science ®(在新窗口中打开)Google Scholar
err
IF0
err
err0
PREAI
err
err分享
err收藏
Yang ZY, He JH, Lu AP, Hou TJ, Cao DS. Application of negative design to design a more desirable virtual screening library. J Med Chem. 2020;63:4411–4429. doi: 10.1021/acs.jmedchem.9b01476. (Open in a new window)PubMed (Open in a new window)Web of Science ®(Open in a new window)Google Scholar杨志勇，何建辉，陆安平，侯太军，曹东升. 负设计在构建更优虚拟筛选库中的应用. 药物化学杂志. 2020;63:4411–4429. doi: 10.1021/acs.jmedchem.9b01476. (在新窗口中打开)PubMed (在新窗口中打开)Web of Science ®(在新窗口中打开)Google Scholar
err
IF0
err
err0
PREAI
err
err分享
err收藏
Jain T, Prinz B, Marker A, Michel A, Reichel K, Czepczor V, Klieber S, Sun W, Kathuria S, Oezguer Bruederle S, et al. Assessment and incorporation of in vitro correlates to pharmacokinetic outcomes in antibody developability workflows. MAbs. 2024;16(1):2384104. doi: 10.1080/19420862.2024.2384104. (Open in a new window)PubMed (Open in a new window)Web of Science ®(Open in a new window)Google ScholarJain T, Prinz B, Marker A, Michel A, Reichel K, Czepczor V, Klieber S, Sun W, Kathuria S, Oezguer Bruederle S, 等. 体外相关性评估及其在抗体可开发性工作流程中对药代动力学结局的整合. 单克隆抗体杂志. 2024;16(1):2384104. doi: 10.1080/19420862.2024.2384104.
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IF0
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PREAI
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Notin P, Rollins N, Gal Y, Sander C, Marks D. Machine learning for functional protein design. Nat Biotechnol. 2024;42(2):216–228. doi: 10.1038/s41587-024-02127-0. (Open in a new window)PubMed (Open in a new window)Web of Science ®(Open in a new window)Google ScholarNotin P, Rollins N, Gal Y, Sander C, Marks D. 机器学习在功能蛋白质设计中的应用。Nat Biotechnol. 2024;42(2):216–228. doi: 10.1038/s41587-024-02127-0. (在新窗口中打开)PubMed (在新窗口中打开)Web of Science ®(在新窗口中打开)Google Scholar
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An in silico method to assess antibody fragment polyreactivity一种用于评估抗体片段多反应性的体外模拟方法
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