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

Agrawal, Neeraj J.; Helk, Bernhard; Kumar, Sandeep; Mody, Neil; Sathish, Hasige A.; Samra, Hardeep S.; Buck, Patrick M.; Li, Li; Trout, Bernhardt L.

doi:10.1080/19420862.2015.1099773

Cited by 94 publications

(139 citation statements)

References 19 publications

(23 reference statements)

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“…We anticipate that future studies integrating in silico tools, such as spatial aggregation prediction and spatial charge map analysis with structural approaches (e.g., HDX-MS) and high-throughput screening assays (e.g., AC-SINS), will support a Quality by Design approach to generate antibodies with improved high concentration solution properties. 31,41 …”

Section: Discussionmentioning

confidence: 99%

“…CDRs were modeled using the BioLuminate Basic Loop Model function. The identity and similarity of the CDR loops were as follows (% identity, % similarity): H1 (100, 100), H2 (67, 100), H3 (25,31), L1 (82, 82), L2 (71, 100), L3 (67, 100). The BioLuminate Aggregation Surface tool was used to identify residues predicted to contribute to aggregation patches; default tool parameters were applied.…”

Section: In Silico Homology Modelingmentioning

confidence: 99%

“…All antibody numbering is based on Kabat numbering. 31,41 The resulting plasmids were transiently transfected into suspension-adapted HEK293 cells using 293Fectin transfection reagent (Life Technologies, Carlsbad, CA). Cells were grown in FreeStyle 293-F Expression Medium (Life Technologies).…”

Section: Antibody Generationmentioning

confidence: 99%

“…Historically, a number of computational and analytical screening methods have been used to predict aggregation [28][29][30] and high concentration formulation risks. 17,[31][32][33][34][35][36] While these approaches can provide valuable information and help screening of potential lead candidates during discovery and early stage development, they do not necessarily provide insight into the underlying molecular mechanisms involved. With regard to high concentration challenges, the availability of methodologies that can probe native intermolecular interactions at the structural and molecular level can effectively guide corresponding protein engineering and formulation efforts designed to mitigate the development risks.…”

Section: Introductionmentioning

confidence: 99%

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Mitigation of reversible self-association and viscosity in a human IgG1 monoclonal antibody by rational, structure-guided Fv engineering

Geoghegan¹,

Fleming²,

Damschroder³

et al. 2016

mAbs

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Undesired solution behaviors such as reversible self-association (RSA), high viscosity, and liquid-liquid phase separation can introduce substantial challenges during development of monoclonal antibody formulations. Although a global mechanistic understanding of RSA (i.e., native and reversible proteinprotein interactions) is sufficient to develop robust formulation controls, its mitigation via protein engineering requires knowledge of the sites of protein-protein interactions. In the study reported here, we coupled our previous hydrogen-deuterium exchange mass spectrometry findings with structural modeling and in vitro screening to identify the residues responsible for RSA of a model IgG1 monoclonal antibody (mAb-C), and rationally engineered variants with improved solution properties (i.e., reduced RSA and viscosity). Our data show that mutation of either solvent-exposed aromatic residues within the heavy and light chain variable regions or buried residues within the heavy chain/light chain interface can significantly mitigate RSA and viscosity by reducing the IgG's surface hydrophobicity. The engineering strategy described here highlights the utility of integrating complementary experimental and in silico methods to identify mutations that can improve developability, in particular, high concentration solution properties, of candidate therapeutic antibodies.

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Section: Discussionmentioning

confidence: 99%

Section: In Silico Homology Modelingmentioning

confidence: 99%

Section: Antibody Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Mitigation of reversible self-association and viscosity in a human IgG1 monoclonal antibody by rational, structure-guided Fv engineering

Geoghegan¹,

Fleming²,

Damschroder³

et al. 2016

mAbs

Self Cite

View full text Add to dashboard Cite

show abstract

“…Sharma and coworkers, 73 used charge on the Fv region, charge difference between V L and V H domains, and total hydrophobicity of the amino acids in the Fv region to predict viscosity at 180 mg per mL concentration. Agrawal and coworkers 78 have developed another method for viscosity prediction at high concentrations using the distribution of the negatively charged residues in the Fv region. All these methods use structural models of the Fv portions, obtained from homology modeling, for viscosity prediction at high concentration.…”

Section: Strategies I and Ii: Effects Of Sequence And Structure Attrimentioning

confidence: 99%

Molecular basis of high viscosity in concentrated antibody solutions: Strategies for high concentration drug product development

Tomar

Kumar

Singh

et al. 2016

mAbs

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152

145

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Effective translation of breakthrough discoveries into innovative products in the clinic requires proactive mitigation or elimination of several drug development challenges. These challenges can vary depending upon the type of drug molecule. In the case of therapeutic antibody candidates, a commonly encountered challenge is high viscosity of the concentrated antibody solutions. Concentration-dependent viscosity behaviors of mAbs and other biologic entities may depend on pairwise and higher-order intermolecular interactions, non-native aggregation, and concentration-dependent fluctuations of various antibody regions. This article reviews our current understanding of molecular origins of viscosity behaviors of antibody solutions. We discuss general strategies and guidelines to select low viscosity candidates or optimize lead candidates for lower viscosity at early drug discovery stages. Moreover, strategies for formulation optimization and excipient design are also presented for candidates already in advanced product development stages. Potential future directions for research in this field are also explored.

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Highland games: A benchmarking exercise in predicting biophysical and drug properties of monoclonal antibodies from amino acid sequences

Coffman

Marques

Orozco

et al. 2020

Biotech & Bioengineering

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Biopharmaceutical product and process development do not yet take advantage of predictive computational modeling to nearly the degree seen in industries based on smaller molecules. To assess and advance progress in this area, spirited coopetition (mutually beneficial collaboration between competitors) was successfully used to motivate industrial scientists to develop, share, and compare data and methods which would normally have remained confidential. The first “Highland Games” competition was held in conjunction with the October 2018 Recovery of Biological Products Conference in Ashville, NC, with the goal of benchmarking and assessment of the ability to predict development‐related properties of six antibodies from their amino acid sequences alone. Predictions included purification‐influencing properties such as isoelectric point and protein A elution pH, and biophysical properties such as stability and viscosity at very high concentrations. Essential contributions were made by a large variety of individuals, including companies which consented to provide antibody amino acid sequences and test materials, volunteers who undertook the preparation and experimental characterization of these materials, and prediction teams who attempted to predict antibody properties from sequence alone. Best practices were identified and shared, and areas in which the community excels at making predictions were identified, as well as areas presenting opportunities for considerable improvement. Predictions of isoelectric point and protein A elution pH were especially good with all‐prediction average errors of 0.2 and 1.6 pH unit, respectively, while predictions of some other properties were notably less good. This manuscript presents the events, methods, and results of the competition, and can serve as a tutorial and as a reference for in‐house benchmarking by others. Organizations vary in their policies concerning disclosure of methods, but most managements were very cooperative with the Highland Games exercise, and considerable insight into common and best practices is available from the contributed methods. The accumulated data set will serve as a benchmarking tool for further development of in silico prediction tools.

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

Cited by 94 publications

References 19 publications

Mitigation of reversible self-association and viscosity in a human IgG1 monoclonal antibody by rational, structure-guided Fv engineering

Mitigation of reversible self-association and viscosity in a human IgG1 monoclonal antibody by rational, structure-guided Fv engineering

Molecular basis of high viscosity in concentrated antibody solutions: Strategies for high concentration drug product development

Highland games: A benchmarking exercise in predicting biophysical and drug properties of monoclonal antibodies from amino acid sequences

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