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

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

  • Adriana-Michelle Wolf Pérez
  • Pietro Sormanni, h Modelling Department , Novo Nordisk A/S , Måløv , Denmark.
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  • Jonathan Sonne Andersen, h Modelling Department , Novo Nordisk A/S , Måløv , Denmark.
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  • Laila Ismail Sakhnini, h Modelling Department , Novo Nordisk A/S , Måløv , Denmark.
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  • Ileana Rodriguez-Leon, h Modelling Department , Novo Nordisk A/S , Måløv , Denmark.
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  • Jais Rose Bjelke, h Modelling Department , Novo Nordisk A/S , Måløv , Denmark.
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  • Annette Juhl Gajhede, h Modelling Department , Novo Nordisk A/S , Måløv , Denmark.
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  • Leonardo De Maria, h Modelling Department , Novo Nordisk A/S , Måløv , Denmark.
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  • Daniel E Otzen
  • Michele Vendruscolo, h Modelling Department , Novo Nordisk A/S , Måløv , Denmark.
  • ,
  • Nikolai Lorenzen, Novo Nordisk

Despite major advances in antibody discovery technologies, the successful development of monoclonal antibodies (mAbs) into effective therapeutic and diagnostic agents can often be impeded by developability liabilities, such as poor expression, low solubility, high viscosity and aggregation. Therefore, strategies to predict at the early phases of antibody development the risk of late-stage failure of antibody candidates are highly valuable. In this work, we employ the in silico solubility predictor CamSol to design a library of 17 variants of a humanized mAb predicted to span a broad range of solubility values, and we examine their developability potential with a battery of commonly used in vitro and in silico assays. Our results demonstrate the ability of CamSol to rationally enhance mAb developability, and provide a quantitative comparison of in vitro developability measurements with each other and with more resource-intensive solubility measurements, as well as with in silico predictors that offer a potentially faster and cheaper alternative. We observed a strong correlation between predicted and experimentally determined solubility values, as well as with measurements obtained using a panel of in vitro developability assays that probe non-specific interactions. These results indicate that computational methods have the potential to reduce or eliminate the need of carrying out laborious in vitro quality controls for large numbers of lead candidates. Overall, our study provides support to the emerging view that the implementation of in silico tools in antibody discovery campaigns can ensure rapid and early selection of antibodies with optimal developability potential.

Original languageEnglish
JournalmAbs
Volume11
Issue2
Pages (from-to)388-400
Number of pages13
ISSN1942-0862
DOIs
Publication statusPublished - 2019

    Research areas

  • DISCOVERY, PREDICTION, PROTEIN AGGREGATION, RATIONAL DESIGN, SELF-ASSOCIATION, SEQUENCE, SOLUBILITY, STABILITY, THERAPEUTIC ANTIBODIES, VISCOSITY, biophysical properties, computational predictions, developability assessment, monoclonal antibodies

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