Literature DB >> 35104168

Current advances in biopharmaceutical informatics: guidelines, impact and challenges in the computational developability assessment of antibody therapeutics.

Rahul Khetan1, Robin Curtis1, Charlotte M Deane2, Johannes Thorling Hadsund3, Uddipan Kar4, Konrad Krawczyk5, Daisuke Kuroda6,7,8, Sarah A Robinson2, Pietro Sormanni9, Kouhei Tsumoto6,7,8,10, Jim Warwicker1, Andrew C R Martin11.   

Abstract

Therapeutic monoclonal antibodies and their derivatives are key components of clinical pipelines in the global biopharmaceutical industry. The availability of large datasets of antibody sequences, structures, and biophysical properties is increasingly enabling the development of predictive models and computational tools for the "developability assessment" of antibody drug candidates. Here, we provide an overview of the antibody informatics tools applicable to the prediction of developability issues such as stability, aggregation, immunogenicity, and chemical degradation. We further evaluate the opportunities and challenges of using biopharmaceutical informatics for drug discovery and optimization. Finally, we discuss the potential of developability guidelines based on in silico metrics that can be used for the assessment of antibody stability and manufacturability.

Entities:  

Keywords:  antibody engineering; biopharmaceutical informatics; computational prediction; developability assessment; developability guidelines; therapeutic antibodies

Mesh:

Substances:

Year:  2022        PMID: 35104168      PMCID: PMC8812776          DOI: 10.1080/19420862.2021.2020082

Source DB:  PubMed          Journal:  MAbs        ISSN: 1942-0862            Impact factor:   5.857


  199 in total

1.  SACS--self-maintaining database of antibody crystal structure information.

Authors:  Lee C Allcorn; Andrew C R Martin
Journal:  Bioinformatics       Date:  2002-01       Impact factor: 6.937

2.  Prediction of protein retention times in hydrophobic interaction chromatography by robust statistical characterization of their atomic-level surface properties.

Authors:  Alexander T Hanke; Marieke E Klijn; Peter D E M Verhaert; Luuk A M van der Wielen; Marcel Ottens; Michel H M Eppink; Emile J A X van de Sandt
Journal:  Biotechnol Prog       Date:  2016-01-20

3.  Anti-galactose-α-1,3-galactose IgE from allergic patients does not bind α-galactosylated glycans on intact therapeutic antibody Fc domains.

Authors:  Jeroen J Lammerts van Bueren; Theo Rispens; Sandra Verploegen; Tjitske van der Palen-Merkus; Steven Stapel; Lisa J Workman; Hayley James; Patrick H C van Berkel; Jan G J van de Winkel; Thomas A E Platts-Mills; Paul W H I Parren
Journal:  Nat Biotechnol       Date:  2011-07-11       Impact factor: 54.908

4.  Sequence features of variable region determining physicochemical properties and polyreactivity of therapeutic antibodies.

Authors:  Maxime Lecerf; Alexia Kanyavuz; Sébastien Lacroix-Desmazes; Jordan D Dimitrov
Journal:  Mol Immunol       Date:  2019-06-26       Impact factor: 4.407

5.  Concentration dependent viscosity of monoclonal antibody solutions: explaining experimental behavior in terms of molecular properties.

Authors:  Li Li; Sandeep Kumar; Patrick M Buck; Christopher Burns; Janelle Lavoie; Satish K Singh; Nicholas W Warne; Pilarin Nichols; Nicholas Luksha; Davin Boardman
Journal:  Pharm Res       Date:  2014-06-07       Impact factor: 4.200

6.  BEST: improved prediction of B-cell epitopes from antigen sequences.

Authors:  Jianzhao Gao; Eshel Faraggi; Yaoqi Zhou; Jishou Ruan; Lukasz Kurgan
Journal:  PLoS One       Date:  2012-06-27       Impact factor: 3.240

7.  ABodyBuilder: Automated antibody structure prediction with data-driven accuracy estimation.

Authors:  Jinwoo Leem; James Dunbar; Guy Georges; Jiye Shi; Charlotte M Deane
Journal:  MAbs       Date:  2016-07-08       Impact factor: 5.857

8.  Institute collection and analysis of Nanobodies (iCAN): a comprehensive database and analysis platform for nanobodies.

Authors:  Jing Zuo; Jian Li; Rongxin Zhang; Longsheng Xu; Hanhan Chen; Xiaohuan Jia; Zhipeng Su; Linhong Zhao; Xing Huang; Wei Xie
Journal:  BMC Genomics       Date:  2017-10-17       Impact factor: 3.969

9.  SCALOP: sequence-based antibody canonical loop structure annotation.

Authors:  Wing Ki Wong; Guy Georges; Francesca Ros; Sebastian Kelm; Alan P Lewis; Bruck Taddese; Jinwoo Leem; Charlotte M Deane
Journal:  Bioinformatics       Date:  2019-05-15       Impact factor: 6.937

10.  Humanization of antibodies using a machine learning approach on large-scale repertoire data.

Authors:  Claire Marks; Alissa M Hummer; Mark Chin; Charlotte M Deane
Journal:  Bioinformatics       Date:  2021-06-10       Impact factor: 6.931

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  3 in total

1.  Conformational Entropy as a Potential Liability of Computationally Designed Antibodies.

Authors:  Thomas Löhr; Pietro Sormanni; Michele Vendruscolo
Journal:  Biomolecules       Date:  2022-05-18

Review 2.  Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery.

Authors:  Wiktoria Wilman; Sonia Wróbel; Weronika Bielska; Piotr Deszynski; Paweł Dudzic; Igor Jaszczyszyn; Jędrzej Kaniewski; Jakub Młokosiewicz; Anahita Rouyan; Tadeusz Satława; Sandeep Kumar; Victor Greiff; Konrad Krawczyk
Journal:  Brief Bioinform       Date:  2022-07-18       Impact factor: 13.994

3.  Understanding the Stabilizing Effect of Histidine on mAb Aggregation: A Molecular Dynamics Study.

Authors:  Suman Saurabh; Cavan Kalonia; Zongyi Li; Peter Hollowell; Thomas Waigh; Peixun Li; John Webster; John M Seddon; Jian R Lu; Fernando Bresme
Journal:  Mol Pharm       Date:  2022-08-10       Impact factor: 5.364

  3 in total

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