Five computational developability guidelines for therapeutic antibody profiling

  • Matthew I. J. Raybould
    Department of Statistics, University of Oxford, Oxford OX1 3LB, United Kingdom;
  • Claire Marks
    Department of Statistics, University of Oxford, Oxford OX1 3LB, United Kingdom;
  • Konrad Krawczyk
    Department of Statistics, University of Oxford, Oxford OX1 3LB, United Kingdom;
  • Bruck Taddese
    Department of Antibody Discovery and Protein Engineering, MedImmune, Cambridge CB21 6GH, United Kingdom;
  • Jaroslaw Nowak
    Department of Statistics, University of Oxford, Oxford OX1 3LB, United Kingdom;
  • Alan P. Lewis
    Computational and Modelling Sciences, GlaxoSmithKline Research and Development, Stevenage SG1 2NY, United Kingdom;
  • Alexander Bujotzek
    Roche Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich, DE-82377 Penzberg, Germany;
  • Jiye Shi
    Chemistry Department, UCB Pharma, Slough SL1 3WE, United Kingdom
  • Charlotte M. Deane
    Department of Statistics, University of Oxford, Oxford OX1 3LB, United Kingdom;

書誌事項

公開日
2019-02-14
権利情報
  • http://creativecommons.org/licenses/by/4.0/
DOI
  • 10.1073/pnas.1810576116
公開者
Proceedings of the National Academy of Sciences

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説明

<jats:title>Significance</jats:title> <jats:p>Immunogenicity, instability, self-association, high viscosity, polyspecificity, or poor expression can all preclude an antibody from becoming a therapeutic. Early identification of these negative characteristics is essential. Akin to the Lipinski guidelines, which measure druglikeness in small molecules, our Therapeutic Antibody Profiler highlights antibodies that possess characteristics that are rare/unseen in clinical-stage mAb therapeutics. The only required input is the variable domain sequence. We show examples where our approach would have advised against manufacturing antibodies that were found to aggregate or have poor expression.</jats:p>

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