https://orcid.org/0000-0002-9819-0206
![Sample our Medicine, Dentistry, Nursing & Allied Health journals, sign in here to start your FREE access for 14 days]({"type":"image" , "src" : "/sda/5944/TOC-Subject-Sample-2021-Medicine-Dentistry-Nursing-Allied-Health.jpg"})
ABSTRACT
Subcutaneous injection is the preferred route of administration for many antibody therapeutics for reasons that include its speed and convenience. However, the small volume limit (typically 2 mL) for subcutaneous delivery often necessitates antibody formulations at high concentrations (commonly ≥100 mg/mL), which may lead to physicochemical problems. For example, antibodies with large hydrophobic or charged patches can be prone to self-interaction giving rise to high viscosity. Here, we combined X-ray crystallography with computational modeling to predict regions of an anti-glucagon receptor (GCGR) IgG1 antibody prone to self-interaction. An extensive mutational analysis was undertaken of the complementarity-determining region residues residing in hydrophobic surface patches predicted by spatial aggregation propensity, in conjunction with residue-level solvent accessibility, averaged over conformational ensembles from molecular dynamics simulations. Dynamic light scattering (DLS) was used as a medium throughput screen for self-interaction of ~ 200 anti-GCGR IgG1 variants. A negative correlation was found between the viscosity determined at high concentration (180 mg/mL) and the DLS interaction parameter measured at low concentration (2–10 mg/mL). Additionally, anti-GCGR variants were readily identified with reduced viscosity and antigen-binding affinity within a few fold of the parent antibody, with no identified impact on overall developability. The methods described here may be useful in the optimization of other antibodies to facilitate their therapeutic administration at high concentration.
Acknowledgments
We thank the Research Materials group in the Cell Culture Department at Genentech for their valuable assistance in mammalian cell expression. Additionally, we thank the protein purification team in the Protein Chemistry Department at Genentech for antibody purification and characterization. We acknowledge Daniel Kovner for providing support and guidance with the DLS and rheometry experiments. We are grateful to Claudio Ciferri for insightful discussions on strategies for obtaining the free Fab structures. We thank Jawahar Sudhamsu and Jennifer Kung for their guidance in data analysis for the anti-GCGR Fab crystal structure determination. We thank Sam Burns and Alexander Dimmling for creating the automation scripts for conducting bulk buffer exchange. We thank Greg Martyn and Makaeel Sheikh for baculovirus assay data and Jack Bevers III for help with SPR. We gratefully acknowledge the Advanced Light Source, a national user facility operated by Lawrence Berkeley National Laboratory on behalf of the Department of Energy, for providing access to their X-ray diffraction data collection infrastructure.
Disclosure statement
All authors are current or former employees of Genentech, Inc, which develops and commercializes therapeutics, including antibodies.
Abbreviations
| CDR | = | Complementarity-determining region |
| CHO | = | Chinese hamster ovary |
| cP | = | Centipoise |
| DLS | = | Dynamic light scattering |
| FR | = | Framework region |
| GCGR | = | Glucagon receptor |
| HC | = | Heavy chain |
| IV | = | Intravenous |
| kD | = | Diffusion interaction parameter |
| LC | = | Light chain |
| MD | = | Molecular dynamics |
| pI | = | Isoelectric point |
| SAP | = | Spatial aggregation propensity |
| SASA | = | Solvent accessible surface area |
| SC | = | Subcutaneous |
| SEC | = | Size-exclusion chromatography |
| SPR | = | Surface plasmon resonance |
| TAP | = | Therapeutic antibody profiler |
| TLS | = | Translation/libration/screw |
| VH | = | Heavy chain variable domain |
| VL | = | Light chain variable domain |
Supplementary material
Supplemental data for this article can be accessed online at https://doi.org/10.1080/19420862.2024.2304282.