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. 2016;8(1):43-8.
doi: 10.1080/19420862.2015.1099773. Epub 2015 Sep 23.

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

Neeraj J Agrawal  1   2 , Bernhard Helk  3 , Sandeep Kumar  4 , Neil Mody  5 , Hasige A Sathish  5 , Hardeep S Samra  5 , Patrick M Buck  4 , Li Li  6 , Bernhardt L Trout  1
Affiliations

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

Neeraj J Agrawal et al. MAbs. 2016.

Abstract

Highly concentrated antibody solutions often exhibit high viscosities, which present a number of challenges for antibody-drug development, manufacturing and administration. The antibody sequence is a key determinant for high viscosity of highly concentrated solutions; therefore, a sequence- or structure-based tool that can identify highly viscous antibodies from their sequence would be effective in ensuring that only antibodies with low viscosity progress to the development phase. Here, we present a spatial charge map (SCM) tool that can accurately identify highly viscous antibodies from their sequence alone (using homology modeling to determine the 3-dimensional structures). The SCM tool has been extensively validated at 3 different organizations, and has proved successful in correctly identifying highly viscous antibodies. As a quantitative tool, SCM is amenable to high-throughput automated analysis, and can be effectively implemented during the antibody screening or engineering phase for the selection of low-viscosity antibodies.

Keywords: antibodies; biotherapeutics; computer simulation; spatial charge map; viscosity.

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Figures

Figure 1.
Figure 1.
Projection of the SCMatom,i values on the Fv domain. (Left) antibody with a low SCM Score, Rituximab (right) antibody with a high SCM Score, N4. Fv domains are oriented so that the light chain is on the left side and the complementarity-determining regions of the antibodies are visible. Red patches indicate electronegative regions, whereas blue patches indicate electropositive patches.
Figure 2.
Figure 2.
Correlation of the SCM Score with Novartis antibodies. An SCM score of greater than ∼1200 is a good predictor for highly viscous antibodies (shown in red) at Novartis.
Figure 3.
Figure 3.
Correlation of the SCM Score with Pfizer antibodies. An SCM score of greater than ∼1600 is a good predictor for highly viscous antibodies (shown in red) at Pfizer.
Figure 4.
Figure 4.
Correlation of the SCM Score with MedImmune antibodies. An SCM score of greater than ˜2000 is a good predictor for highly viscous antibodies (shown in red) at MedImmune.
See this image and copyright information in PMC

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