In silico selection of therapeutic antibodies for development: viscosity, clearance, and chemical stability

Abstract

For mAbs to be viable therapeutics, they must be formulated to have low viscosity, be chemically stable, and have normal in vivo clearance rates. We explored these properties by observing correlations of up to 60 different antibodies of the IgG1 isotype. Unexpectedly, we observe significant correlations with simple physical properties obtainable from antibody sequences and by molecular dynamics simulations of individual antibody molecules. mAbs viscosities increase strongly with hydrophobicity and charge dipole distribution and decrease with net charge. Fast clearance correlates with high hydrophobicities of certain complementarity determining regions and with high positive or high negative net charge. Chemical degradation from tryptophan oxidation correlates with the average solvent exposure time of tryptophan residues. Aspartic acid isomerization rates can be predicted from solvent exposure and flexibility as determined by molecular dynamics simulations. These studies should aid in more rapid screening and selection of mAb candidates during early discovery.

Keywords: degradation; monoclonal antibodies; pharmacokinetics; prediction; viscosity.

Fig. 1.

(A) Viscosity-concentration profiles of three monoclonal antibodies of the IgG1 isotype in a buffered solution at pH 5.5 and 200 mM arginine-HCl. The points represent the experimental data. The lines are used as a guide to the eye and were generated using the equation of the exponential form y = a + be^cx, where y is viscosity, x is protein concentration, and a, b, and c are fitting parameters. Correlation of log viscosity with the calculated sequence-based parameters of (B) charge at pH 5.5, (C) Fv charge symmetry parameter (FvCSP) at pH 5.5, and (D) Fv hydrophobicity index (HI). The viscosity values were obtained in buffered solution at pH 5.5 and 200 mM arginine-HCl. (E) Principal component regression analysis plot showing the predicted viscosity values against the experimental viscosity values for 180 mg/mL mAb concentration. The observed viscosity values are the experimental values obtained in buffered solution at pH 5.5 and 200 mM arginine-HCl. The predicted viscosity values are the output values from PCR analysis and are described by Eq. 1. Each data point represents an mAb, and the curved lines represent the 90% CIs. (F) Scatterplot between the predicted values obtained using the LOOCV approach through PCR analysis and the experimental viscosity values.

Fig. 2.

(A) Clearance values in Cynomolgus monkeys and the calculated sequence parameters for a training set of 14 mAbs. (B) Assigned criteria of the sequence-derived parameters based on the training set of 14 mAbs to differentiate between mAbs of fast clearance and normal clearance. (C) Distribution of a set of 61 mAbs based on their respective theoretical Fv domain charge at pH 5.5 and a calculated HI sum of CDRs for LC1, LC3, and HC3. Each data point on the grid represents a given mAb, and the size of each data point is proportional to the clearance values by area. Solid green circles represent mAbs with normal clearance (<10 mL/kg per day), and red dashed circles represent mAbs with fast clearance (≥10 mL/kg per day) in Cynomolgus monkeys.

Fig. 3.

Trp oxidation prediction using the time-averaged solvent accessible surface area of a number of Trps on various mAbs. A total of 38 Trps in 17 mAbs were used for the analysis, of which 8 were present in the constant domain of the Fab (indicated by open circles) and the rest were present in the CDRs. A plot compares the AAPH-induced Trp oxidation and % Trp SASA. The lines represent the % SASA cutoff (horizontal) and the % AAPH-induced oxidation cutoff (vertical), respectively, to differentiate between the reactive and the nonreactive Trps in various mAbs. Each data point on the plot is a Trp residue in a given mAb. Based on historical data between AAPH-induced and thermal-induced oxidation of Trps in mAbs, a 35% oxidation cutoff was used to differentiate between reactive (squares) and nonreactive (circles) Trps. Reactive Trp were predicted based on a >30% average SASA of the Trp side chain obtained through MD simulations.

Fig. 4.

(A) Comparison of the average and SD of various properties, i.e., SASA, RMSF, and SASA (n + 1, N), extracted from MD simulations for labile group (red) vs. stable group of Asp residues (green). Calculated P values are shown on each plot. (B) Outcome of the logistical regression to enable prediction of labile vs. stable Asp sites. Logistic regression was performed using SASA, RMSF, and SASA (n + 1, N) as independent variables and the binary rate output as the dependent variable. All sites were assigned a value of 1 or 0 for labile and stable sites, respectively, based on a cutoff value of Asp degradation rate of 2.5%/wk at pH 5.5. Labile residues are shown in red and stable residues are shown in green. The predicted outcome was generated using the binary logistic model as described in the text. The model was validated using the LOOCV approach.