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A single molecular descriptor to predict solution behavior of therapeutic antibodies
Author(s) -
Jonathan S. Kingsbury,
Amandeep Saini,
Sarah M. Auclair,
Li Fu,
Michaela M. Lantz,
Kevin T. Halloran,
Cesar CaleroRubio,
Walter Schwenger,
Christian Airiau,
Jifeng Zhang,
Yatin R. Gokarn
Publication year - 2020
Publication title -
science advances
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.928
H-Index - 146
ISSN - 2375-2548
DOI - 10.1126/sciadv.abb0372
Subject(s) - selection (genetic algorithm) , antibody , computational biology , biological system , computer science , chemistry , biology , artificial intelligence , immunology
Despite the therapeutic success of monoclonal antibodies (mAbs), early identification of developable mAb drug candidates with optimal manufacturability, stability, and delivery attributes remains elusive. Poor solution behavior, which manifests as high solution viscosity or opalescence, profoundly affects the developability of mAb drugs. Using a diverse dataset of 59 mAbs, including 43 approved products, and an array of molecular descriptors spanning colloidal, conformational, charge-based, hydrodynamic, and hydrophobic properties, we show that poor solution behavior is prevalent (>30%) in mAbs and is singularly predicted (>90%) by the diffusion interaction parameter ( ), a dilute-solution measure of colloidal self-interaction. No other descriptor, individually or in combination, was found to be as effective as . We also show that well-behaved mAbs, a substantial subset of which bear high positive charge and pI, present no disadvantages with respect to pharmacokinetics in humans. Here, we provide a systematic framework with quantitative thresholds for selecting well-behaved therapeutic mAbs during drug discovery.

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