No abstract

The aggregation of biotherapeutics is a major hindrance to the development of successful drug candidates; however, the propensity to aggregate is often identified too late in the development phase to permit modification to the protein's sequence. Incorporating rational design for the stability of proteins in early discovery has numerous benefits. We engineered out aggregation-prone regions on the Fab domain of a therapeutic monoclonal antibody, bevacizumab, to rationally design a biobetter drug candidate. With the purpose of stabilizing bevacizumab with respect to aggregation, 2 strategies were undertaken: single point mutations of aggregation-prone residues and engineering a glycosylation site near aggregation-prone residues to mask these residues with a carbohydrate moiety. Both of these approaches lead to comparable decreases in aggregation, with an up to 4-fold reduction in monomer loss. These single mutations and the new glycosylation pattern of the Fab domain do not modify binding to the target. Biobetters with increased stability against aggregation can therefore be generated in a rational manner, by either removing or masking the aggregation-prone region or crowding out protein-protein interactions.

Antibody therapeutics are now in widespread use and provide a new approach for treating serious diseases such as rheumatic diseases and cancer. Monoclonal antibodies used as therapeutic agents must be of high quality, and their safety must be guaranteed. Aggregated antibody is a degradation product that may be generated during the manufacturing process. To maintain the high quality and safety of antibody therapeutics, it is necessary to understand the mechanism of aggregation and to develop technologies to strictly control aggregate formation. Here, we extensively investigated the conformational and colloidal characteristics of isolated antibody constant domains, and provided insights into the molecular mechanism of antibody aggregation. Isolated domains (CH2, CH3, CL, and CH1-CL dimer) of human immunoglobulin G were synthesized, solubilized using 49 sets of solution conditions (pH 2-8 and 0-300 mM NaCl), and characterized using circular dichroism, intrinsic tryptophan fluorescence, and dynamic light scattering. Salt-induced conformational changes and oligomer formation were kinetically analyzed by NaCl-jump measurements (from 0 to 300 mM at pH 3). Phase diagrams revealed that the domains have different conformational and colloidal stabilities. The unfolded fractions of CH3 and CH2 at pH 3 were larger than that of CL and CH1-CL dimer. The secondary and tertiary structures and particle sizes of CH3 and CH2 showed that, in non-native states, these domains were sensitive to salt concentration. Kinetic analyses suggest that oligomer formation by CH3 and CH2 proceeds through partially refolded conformations. The colloidal stability of CH3 in non-native states is the lowest of the four domains under the conditions tested. We propose that the impact of IgG constant domains on aggregation follows the order CH3 > CH2 > CH1-CL dimer > CL; furthermore, we suggest that CH3 plays the most critical role in driving intact antibody aggregation under acidic conditions.

A number of potential degradation routes can limit the shelf life of a biotherapeutic. While these are experimentally measurable, the tests to do so require a substantial investment in both time and material, resources rarely available early in the drug development process. To address the potential degradation route of non-enzymatic hydrolysis, we performed a molecular modeling analysis, together with an experimental study, to gain detailed insight into the reaction. On the basis of the mechanism, an algorithm for predicting the likely cleavage sites of a protein has been created. This algorithm measures four key properties during a molecular dynamics simulation, which relate to the key steps of the hydrolysis mechanism, in particular the rate-determining step (which can vary depending on the local environment). The first two properties include the secondary structure and the surface exposure of the amide bond, both of which help detect if the addition of the proton to the amide bond is possible. The second two properties relate to whether the side chain can cyclize and form a furane ring. These two properties are the orientation of the side chain relative to the amide bond and the number of hydrogen bonds between the side chain and the surrounding protein. Overall, the algorithm performs well at identifying reactive versus nonreactive bonds. The algorithm correctly classifies nearly 90% of all amide bonds following an aspartic or glutamic acid residue as reactive or nonreactive.