Measuring aggregates, self-association, and weak interactions in concentrated therapeutic antibody solutions

Abstract

Monoclonal antibodies are a class of biotherapeutics used for an increasing variety of disorders, including cancer, autoimmune, neurodegenerative, and viral diseases. Besides their antigen specificity, therapeutic use also mandates control of their solution interactions and colloidal properties in order to achieve a stable, efficacious, non-immunogenic, and low viscosity antibody solution at concentrations in the range of 50-150 mg/mL. This requires characterization of their reversible self-association, aggregation, and weak attractive and repulsive interactions governing macromolecular distance distributions in solution. Simultaneous measurement of these properties, however, has been hampered by solution nonideality. Based on a recently introduced sedimentation velocity method for measuring macromolecular size distributions in a mean-field approximation for hydrodynamic interactions, we demonstrate simultaneous measurement of polydispersity and weak and strong solution interactions in a panel of antibodies with concentrations up to 45 mg/mL. By allowing approximately an order of magnitude higher concentrations than previously possible in sedimentation velocity size distribution analysis, this approach can substantially improve efficiency and sensitivity for characterizing polydispersity and interactions of therapeutic antibodies at or close to formulation conditions.

Keywords: Trace aggregation; hydrodynamics; nonideality; protein interactions; sedimentation velocity; self-association; virial coefficient.

Figure 1.

Distortions in the sedimentation boundaries and apparent sedimentation coefficient distributions in nonideal sedimentation. a, Measured concentration profiles of 37 mg/mL mAb A at select times during sedimentation at 45,000 rpm (solid lines; later times indicated by higher color temperature) in comparison with the theoretical sedimentation of a macromolecule with sedimentation properties as measured for the monomer or mAb A under dilute conditions (dotted lines; corresponding to a species with 6.5 S and frictional ratio 1.6). b, Lines show different sedimentation coefficient distribution models derived by fitting the sedimentation data: standard c(s) with unphysical best-fit f/f₀ of 4.0 (blue); apparent sedimentation coefficient distribution g*(s) (purple); and nonideal c_NI(s₀) fixing f/f₀ at 1.6 as measured in dilute solution by ideal c(s) analysis (magenta). The dashed vertical line indicates the s-value of the mAb A monomer measured at 100-fold lower concentration in dilute solution. In (a) and (b) the boundary and distribution features corresponding to trace aggregates are highlighted by shaded areas (arrows).

Figure 2.

Comparison of measured sedimentation boundaries of mAb A (a) and mAb D (b). Concentrations are 37 mg/mL for mAb A and 46 mg/mL for mAb D, and sedimentation at 45,000 rpm is represented by every 10^th data point (points) of every 3^rd scan (time intervals of 120 sec). The solid line is the best-fit nonideal c_NI(s₀) model fixing the frictional ratio f/f₀ at the value measured in dilute solution, while refining the best-fit values for k_S and k_D (see Table 1). This results in a ratio of rmsd/loading signal of 0.42% for mAb A and 0.41% for mAb D. The corresponding best-fit sedimentation coefficient distributions c_NI(s₀) are shown in Figure 3.

Figure 3.

Different protein interactions exhibited by a panel of different mAbs A – E. Shown are concentration-dependent sedimentation coefficient distributions c_NI(s₀) of the mAb at concentrations indicated in the legend. Insets expand scale > 7 S.

Figure 4.

Isotherms of weighted-average sedimentation coefficients s_w (symbols) and best-fit self-association models (lines) for different mAbs. s_w-values were determined by integration of the c_NI(s₀) distribution of Figure 3. For best-fit self-association parameters, see Table 1.