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. 2017 Apr 21;429(8):1244-1261.
doi: 10.1016/j.jmb.2017.03.014. Epub 2017 Mar 18.

Prediction and Reduction of the Aggregation of Monoclonal Antibodies

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Free PMC article

Prediction and Reduction of the Aggregation of Monoclonal Antibodies

Rob van der Kant et al. J Mol Biol. .
Free PMC article

Abstract

Protein aggregation remains a major area of focus in the production of monoclonal antibodies. Improving the intrinsic properties of antibodies can improve manufacturability, attrition rates, safety, formulation, titers, immunogenicity, and solubility. Here, we explore the potential of predicting and reducing the aggregation propensity of monoclonal antibodies, based on the identification of aggregation-prone regions and their contribution to the thermodynamic stability of the protein. Although aggregation-prone regions are thought to occur in the antigen binding region to drive hydrophobic binding with antigen, we were able to rationally design variants that display a marked decrease in aggregation propensity while retaining antigen binding through the introduction of artificial aggregation gatekeeper residues. The reduction in aggregation propensity was accompanied by an increase in expression titer, showing that reducing protein aggregation is beneficial throughout the development process. The data presented show that this approach can significantly reduce liabilities in novel therapeutic antibodies and proteins, leading to a more efficient path to clinical studies.

Keywords: monoclonal antibody; protein aggregation; protein engineering; protein folding.

Figures

Image 1
Fig. 1
Fig. 1
In silico analysis of aggregation propensity in antibody crystal structures. (a) Schematic representation of possible locations of APRs in monoclonal antibodies. APRs in CDRs would be more problematic than APRs buried in the immunoglobulin fold. (b) Stretch-plots: representation of aggregation propensity and local stability of APRs. Problems increase toward the top right of the plot; ideally, APRs would be located in the bottom left. (c) Density plot of all APRs located in the FR of over 2000 antibody structures from the abYsis database . (d) Density plot of aggregation propensity and local stability of APRs in globular protein structures. The analysis is based on a set of 2650 high quality structures (R-factor of < 0.20 and resolution of < 1.9, with 30% sequence identity) of globular proteins generated using the Whatif software suite . (e) Density plot of all APRs overlapping with CDRs of all antibody structure from the abYsis database. Cyan dots: APRs overlapping with CDRs of the 11 model antibodies used in the study.
Fig. 2
Fig. 2
Stretch-plots and schematic representation of the structure of the Fab fragment of the 11 model antibodies used in this study. (a–k) Blue: APRs located in FR of the antibody. Red and green: APRs overlapping with CDRs in the heavy chain (H) or the light chain (L), respectively. Numbers represent CDR number (Chothia numbering) with which the respective APR is overlapping. Colors in structures: yellow: low scoring APR, red: high scoring APR.
Fig. 3
Fig. 3
Characterization and scoring of the 11 model antibodies. (a) Temperature-dependent evolution of the barycentric mean (BCM) of the fluorescence emission spectrum of three representative antibodies from our test set. Curves were used to derive the melting temperatures (Tm). (b) Temperature-dependent evolution of the RALS intensity measured simultaneously with the data in (a). The aggregation onset temperature Tagg is derived from these data. (c) A plot of the melting points and aggregation onset temperatures of all the test antibodies obtained from stably transfected CHO DG44 cells measured at 0.7 mg/mL. mAb numbers are indicated. (d) Correlation between the Solubis score and the difference between the Tm and the TaggTmTagg) for the tested antibodies. (Pearson's correlation = 0.89, p < 0.02). mAb numbers are indicated. (e) Distribution of Solubis score over the different datasets. 1: WT, 2: FH101P, 3: SL50K, 4: SL50K_FH101P. (f) Stretch-plot summarizing the aggregation propensity and local stability of all APRs identified using TANGO in 27 FDA-approved monoclonal antibodies. Blue: APRs located in the FR of the antibody. Red and green: APRs overlapping with CDRs in the heavy chain (H) or the light chain (L), respectively. Numbers represent CDR number (Chothia numbering) with which the respective APR is overlapping. (g) Receiver operator curve showing the ability of the Solubis scoring function, TANGO, and the number of APRs to classify the WT antibodies structures, based on original crystal structure or homology model built using one template (Table 1). (h) Receiver operator curve similar to (g) but calculated using the results from different homology modeling approaches.
Fig. 4
Fig. 4
Design of APR disrupting mutations. (a) Stretch-plot of mAb2; critical APRs are highlighted with red circles. (b) Crystal structure of the variable domain of mAb2 with the APRs highlighted. (c–d) MASS plots of the two critical APRs in mAb2 with mutation effects on aggregation propensity and stability. Chosen mutations are highlighted in red. (e) Left: Crystal structure of mAb2 in complex with VEGF. Gray: mAb2, green: VEGF. Top right: Zoom on the APR in the heavy chain. Red: FH101, blue: Tryptophan located close to FH101, green: VEGF with molecular surface displayed. Bottom right: Zoom on the APR in the light chain. Magenta: SL50 and SL52, green: VEGF with molecular surface displayed. Images were made using YASARA Structure.
Fig. 5
Fig. 5
Design of net charge increasing mutation in the heavy chain. (a) Effect of all mutations on the global stability (ΔG) and stability of the complex between the heavy and light chain. Blue: Mutations that were not selected. Red: Selected mutations. (b) Left: Crystal structure of mAb2 in complex with VEGF. Gray: mAb2, green: VEGF. Top right: Zoom on the net charge increasing mutation SH21R. Red: SH21, blue: RH21. Bottom right: Zoom on the net charge increasing mutation SH85R. Red: SH85, blue: RH85. Images were made using YASARA Structure.
Fig. 6
Fig. 6
Characterization of mAb2 mutants. (a) Far-UV CD spectra. Black: Wild-type, yellow: SL50K, green: SL50D, blue: FH101P, cyan: VH100R, red: SL52R. (b) Far-UV CD Spectra. Black: Wild-type, yellow: SL52R_FH101P, green: SL52R_SH21R_SH85R_FH101P, blue: SL50K_FH101P, cyan: SL50K_SH21R_SH85R_FH101P, red: SL50K_SH21R_SH85R. (c) Intrinsic fluorescence emission spectra upon excitation at 20 °C of mAb2 and indicated mutants. (d) Temperature-dependent evolution of the RALS intensity for wild-type and two mutants. (e) Aggregation onset points and melting temperatures of wild-type and mutants at 1 mg/mL, obtained from transiently transfected CHO K1 cells. (f) Expression titers for mAb2 wild-type and mutants. (g) VEGF binding determination using optical laser-induced thermophoresis of mAb2 WT and selected mutants. (h) Fluorescence intensity of the rotor dye Thioflavin-T in the presence of mAb2 WT and the mutants SL50K_SH21R_SH85R and FH101P_SL50K_SH21R_SH85R. Excitation was at 440 nm, emission was recorded at 480 nm. (i) Correlation between the Solubis score and the difference between the Tm and the TaggTmTagg) for mAb2 wild-type and mutant; numbers are indicated.

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