Aggregation in protein-based biotherapeutics: Computational studies and tools to identify aggregation-prone regions

Authors

  • Neeraj J. Agrawal,

    1. Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
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  • Sandeep Kumar,

    Corresponding author
    1. Pharmaceutical Research and Development, Global Biologics, Pfizer Global Research and Development, Chesterfield, Missouri 63017
    • Pharmaceutical Research and Development, Global Biologics, Pfizer Global Research and Development, Chesterfield, Missouri 63017. Ph: 314-274-0176, Fax: 314-274-7601
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  • Xiaoling Wang,

    1. Pharmaceutical Research and Development, Global Biologics, Pfizer Global Research and Development, Chesterfield, Missouri 63017
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  • Bernhard Helk,

    1. Novartis Pharma AG, Basel CH-4057, Switzerland
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  • Satish K. Singh,

    1. Pharmaceutical Research and Development, Global Biologics, Pfizer Global Research and Development, Chesterfield, Missouri 63017
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  • Bernhardt L. Trout

    Corresponding author
    1. Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
    • Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139. Ph: 617-258-5021, Fax: 617-253-2272
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  • Neeraj J. Agrawal and Sandeep Kumar contributed equally to this work.

Abstract

Because of their large, complex, and conformationally heterogeneous structures, biotherapeutics are vulnerable to several physicochemical stresses faced during the various processing steps from production to administration. In particular, formation of protein aggregates is a major concern. The greatest risk with aggregates arises from their potential to give rise to immunogenic reactions. Hence, it is desirable to bring forward biotherapeutic drug candidates that show low propensity for aggregation and, thus, improved developability. Here, we present a comprehensive review of computational studies into the sequence and structural factors that underpin protein and peptide aggregation. A number of computational approaches have been applied including coarse grain models, atomistic molecular simulations, and bioinformatic approaches. These studies have focused on both the mechanism of aggregation and the identification of potential aggregation-prone sequence and structural motifs. We also survey the computational tools available to predict aggregation in therapeutic proteins. The findings communicated here provide insights that could be potentially useful in the rational design of therapeutic candidates with not only high potency and specificity but also improved stability and solubility. These sequence–structure-based approaches can be applied to both novel as well as follow-on biotherapeutics. © 2011 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 100:5081–5095, 2011

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