Literature DB >> 21935950

Developability index: a rapid in silico tool for the screening of antibody aggregation propensity.

Timothy M Lauer1, Neeraj J Agrawal, Naresh Chennamsetty, Kamal Egodage, Bernhard Helk, Bernhardt L Trout.   

Abstract

Determining the aggregation propensity of protein-based biotherapeutics is an important step in the drug development process. Typically, a great deal of data collected over a large period of time is needed to estimate the aggregation propensity of biotherapeutics. Thus, candidates cannot be screened early on for aggregation propensity, but early screening is desirable to help streamline drug development. Here, we present a simple molecular computational method to predict the aggregation propensity via hydrophobic interactions, thought to be the most common mechanism of aggregation, and electrostatic interactions. This method uses a new quantity termed Developability Index. It is a function of an antibody's net charge, calculated on the full-length antibody structure, and the spatial aggregation propensity, calculated on the complementarity-determining region structure. Its accuracy is due to the molecular level details and the incorporation of the tertiary structure of the antibody. It is particularly applicable to antibodies or other proteins for which structures are available or could be determined accurately using homology modeling. Applications include the selection of molecules in the discovery or early development process, selection of mutants for stability, and estimation of resources needed for development of a given biomolecule.
Copyright © 2011 Wiley-Liss, Inc.

Mesh:

Substances:

Year:  2011        PMID: 21935950     DOI: 10.1002/jps.22758

Source DB:  PubMed          Journal:  J Pharm Sci        ISSN: 0022-3549            Impact factor:   3.534


  56 in total

1.  Computational tool for the early screening of monoclonal antibodies for their viscosities.

Authors:  Neeraj J Agrawal; Bernhard Helk; Sandeep Kumar; Neil Mody; Hasige A Sathish; Hardeep S Samra; Patrick M Buck; Li Li; Bernhardt L Trout
Journal:  MAbs       Date:  2015-09-23       Impact factor: 5.857

2.  Rational design of therapeutic mAbs against aggregation through protein engineering and incorporation of glycosylation motifs applied to bevacizumab.

Authors:  Fabienne Courtois; Neeraj J Agrawal; Timothy M Lauer; Bernhardt L Trout
Journal:  MAbs       Date:  2016       Impact factor: 5.857

Review 3.  Advances in Antibody Design.

Authors:  Kathryn E Tiller; Peter M Tessier
Journal:  Annu Rev Biomed Eng       Date:  2015-08-14       Impact factor: 9.590

4.  Reduction of Nonspecificity Motifs in Synthetic Antibody Libraries.

Authors:  Ryan L Kelly; Doris Le; Jessie Zhao; K Dane Wittrup
Journal:  J Mol Biol       Date:  2017-11-26       Impact factor: 5.469

5.  Antibody modeling using the prediction of immunoglobulin structure (PIGS) web server [corrected].

Authors:  Paolo Marcatili; Pier Paolo Olimpieri; Anna Chailyan; Anna Tramontano
Journal:  Nat Protoc       Date:  2014-11-06       Impact factor: 13.491

6.  MoFvAb: Modeling the Fv region of antibodies.

Authors:  Alexander Bujotzek; Angelika Fuchs; Changtao Qu; Jörg Benz; Stefan Klostermann; Iris Antes; Guy Georges
Journal:  MAbs       Date:  2015       Impact factor: 5.857

Review 7.  Structure, heterogeneity and developability assessment of therapeutic antibodies.

Authors:  Yingda Xu; Dongdong Wang; Bruce Mason; Tony Rossomando; Ning Li; Dingjiang Liu; Jason K Cheung; Wei Xu; Smita Raghava; Amit Katiyar; Christine Nowak; Tao Xiang; Diane D Dong; Joanne Sun; Alain Beck; Hongcheng Liu
Journal:  MAbs       Date:  2018-12-17       Impact factor: 5.857

8.  In vitro and in silico assessment of the developability of a designed monoclonal antibody library.

Authors:  Adriana-Michelle Wolf Pérez; Pietro Sormanni; Jonathan Sonne Andersen; Laila Ismail Sakhnini; Ileana Rodriguez-Leon; Jais Rose Bjelke; Annette Juhl Gajhede; Leonardo De Maria; Daniel E Otzen; Michele Vendruscolo; Nikolai Lorenzen
Journal:  MAbs       Date:  2019-01-18       Impact factor: 5.857

9.  Biophysical properties of the clinical-stage antibody landscape.

Authors:  Tushar Jain; Tingwan Sun; Stéphanie Durand; Amy Hall; Nga Rewa Houston; Juergen H Nett; Beth Sharkey; Beata Bobrowicz; Isabelle Caffry; Yao Yu; Yuan Cao; Heather Lynaugh; Michael Brown; Hemanta Baruah; Laura T Gray; Eric M Krauland; Yingda Xu; Maximiliano Vásquez; K Dane Wittrup
Journal:  Proc Natl Acad Sci U S A       Date:  2017-01-17       Impact factor: 11.205

10.  An automated immunoassay for early specificity profiling of antibodies.

Authors:  Katrin Frese; Meike Eisenmann; Ralf Ostendorp; Bodo Brocks; Stefan Pabst
Journal:  MAbs       Date:  2013-02-14       Impact factor: 5.857

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.