Stability enhancement in a mAb and Fab coformulation

Site-Specific Structural Changes in Long-Term-Stressed Monoclonal Antibody Revealed with DEPC Covalent-Labeling and Quantitative Mass Spectrometry

Studies of structural changes in mAbs under forced stress and storage conditions are essential for the recognition of degradation hotspots, which can be further remodeled to improve the stability of the respective protein. Herein, we used diethyl pyrocarbonate (DEPC)-based covalent labeling mass spectrometry (CL-MS) to assess structural changes in a model mAb (SILuMAb). Structural changes in the heat-stressed mAb samples were confirmed at specific amino acid positions from the DEPC label mass seen in the fragment ion mass spectrum. The degree of structural change was also quantified by increased or decreased DEPC labeling at specific sites; an increase or decrease indicated an unfolded or aggregated state of the mAb, respectively. Strikingly, for heat-stressed SILuMAb samples, an aggregation-prone area was identified in the CDR region. In the case of longterm stress, the structural consequences for SILuMAb samples stored for up to two years at 2-8 °C were studied with SEC-UV and DEPC-based CL-MS. While SEC-UV analysis only indicated fragmentation of SILuMAb, DEPC-based CL-MS analysis further pinpointed the finding to structural disturbances of disulfide bonds at specific cysteines. This emphasized the utility of DEPC CL-MS for studying disulfide rearrangement. Taken together, our data suggests that DEPC CL-MS can complement more technically challenging methods in the evaluation of the structural stability of mAbs.

Challenges in scaling up AAV-based gene therapy manufacturing

Accelerating the scale up of adeno-associated virus (AAV) manufacture is highly desirable to meet the increased demand for gene therapies. However, the development of bioprocesses for AAV gene therapies remains time-consuming and challenging. The quality by design (QbD) approach ensures bioprocess designs that meet the desired product quality and safety profile. Rapid stress tests, developability screens, and scale-down technologies have the potential to streamline AAV product and manufacturing bioprocess development within the QbD framework. Here we review how their successful use for antibody manufacture development is translating to AAV, but also how this will depend critically on improved analytical methods and adaptation of the tools as more understanding is gained on the critical attributes of AAV required for successful therapy.

Crystal structures and molecular dynamics simulations of a humanised antibody fragment at acidic to basic pH

Antibody-fragment (Fab) therapy development has the potential to be accelerated by computational modelling and simulations that predict their target binding, stability, formulation, manufacturability, and the impact of further protein engineering. Such approaches are currently predicated on starting with good crystal structures that closely represent those found under the solution conditions to be simulated. A33 Fab, is an undeveloped immunotherapeutic antibody candidate that was targeted to the human A33 antigen homogeneously expressed in 95% cases of primary and metastatic colorectal cancers. It is now used as a very well characterised testing ground for developing analytics, formulation and protein engineering strategies, and to gain a deeper understanding of mechanisms of destabilisation, representative of the wider therapeutic Fab platform. In this article, we report the structure of A33 Fab in two different crystal forms obtained at acidic and basic pH. The structures overlapped with RMSD of 1.33 Å overall, yet only 0.5 Å and 0.76 Å for the variable- and constant regions alone. While most of the differences were within experimental error, the switch linker between the variable and the constant regions showed some small differences between the two pHs. The two structures then enabled a direct evaluation of the impact of initial crystal structure selection on the outcomes of molecular dynamics simulations under different conditions, and their subsequent use for determining best fit solution structures using previously obtained small-angle x-ray scattering (SAXS) data. The differences in the two structures did not have a major impact on MD simulations regardless of the pH, other than a slight persistence of structure affecting the solvent accessibility of one of the predicted APR regions of A33 Fab. Interestingly, despite being obtained at pH 4 and pH 9, the two crystal structures were more similar to the SAXS solution structures obtained at pH 7, than to those at pH 4 or pH 9. Furthermore, the P65 crystal structure from pH 4 was also a better representation of the solution structures at any other pH, than was the P1 structure obtained at pH 9. Thus, while obtained at different pH, the two crystal structures may represent highly (P65) and lesser (P1) populated species that both exist at pH 7 in solution. These results now lay the foundation for confident MD simulations of A33 Fab that rationalise or predict behaviours in a range of conditions.

Stability Convergence in Antibody Coformulations

Combined administration of antibody therapeutics has proven to be beneficial for patients with cancer or infectious diseases. As a result, there is a growing trend toward multiple antibodies premixed into a single product form and delivered to patients as a fixed-dose coformulation. However, combining antibodies into a single coformulation could be challenging as proteins have the potential to interact and alter their stability and degradation profiles in the mixture, compared to that in isolation. We show that in two specific antibody-antibody coformulations, the more stable antibody component increased the stability of the less stable component, which in return destabilized the more stable component, hence exhibiting an overall convergence of stability in the coformulation.

Machine learning reveals hidden stability code in protein native fluorescence

Conformational stability of a protein is usually obtained by spectroscopically measuring the unfolding melting temperature. However, optical spectra under native conditions are considered to contain too little resolution to probe protein stability. Here, we have built and trained a neural network model to take the temperature-dependence of intrinsic fluorescence emission under native-only conditions as inputs, and then predict the spectra at the unfolding transition and denatured state. Application to a therapeutic antibody fragment demonstrates that thermal transitions obtained from the predicted spectra correlate highly with those measured experimentally. Crucially, this work reveals that the temperature-dependence of native fluorescence spectra contains a high-degree of previously hidden information relating native ensemble features to stability. This could lead to rapid screening of therapeutic protein variants and formulations based on spectroscopic measurements under non-denaturing temperatures only.