Predicting Hemagglutinin MHC-II Ligand Analogues in Anti-TNFα Biologics: Implications for Immunogenicity of Pharmaceutical Proteins

Protein aggregation and immunogenicity of biotherapeutics

Recombinant proteins are the mainstay of biopharmaceuticals. A key challenge in the manufacturing and formulation of protein biologic products is the tendency for the active pharmaceutical ingredients to aggregate, resulting in irreversible drug loss, and an increase in immunogenicity risk. While the molecular mechanisms of protein aggregation have been discussed extensively in the literature, knowledge gaps remain in connecting the phenomenon in the context of immunogenicity of biotherapeutics. In this review, we discussed factors that drive aggregation of pharmaceutical recombinant proteins, and highlighted methods of prediction and mitigation that can be deployed through the development stages, from formulation to bioproduction. The purpose is to stimulate new dialogs that would bridge the interface between physical characterizations of protein aggregates in biotherapeutics and the functional attributes of the immune system.

Toward reducing biomaterial antigenic potential: a miniaturized Fc-binding domain for local deposition of antibodies

A peptide derived from staphylococcal protein A (SpA) was developed as an affinity module for antibody delivery applications. The miniaturized protein consists of the first helix of the engineered SpA Z domain fused with the self-assembling peptide (SAP) AEAEAKAKAEAEAKAK, or EAK. The resulting peptide, named Z15_EAK, was shown to possess fibrillization properties and an Fc-binding function. The peptide induced a red shift in the Congo red absorbance characteristic of peptide fibrils, also evidenced in transmission electron microscopy images. The one-site binding affinity (Kd) of a gel-like coacervate generated by admixing Z15_EAK with EAK for IgG was determined to be 1.27 ± 0.14 μM based on a microplate-based titration assay. The coacervate was found to localize IgG subcutaneously in mouse footpads for 8 to 28 days. A set of in vivo data was fit to a one-compartment model for simulating the relative fractions of IgG dissociated from the materials in the depot. The model predicted that close to 27% of the antibodies injected were available unbound for the duration of the experiment. Z15_EAK did not appear to induce innate immune responses; injecting Z15_EAK into mouse footpads elicited neither interleukin-6 (IL-6) nor tumor necrosis factor-alpha (TNF-α) from splenocytes isolated from the animals one day, seven days, or eleven days afterward. The antigenic potential of Z15 was analyzed using a bioinformatic approach in predicting sequences in SpA and Z15 dually presented by class I and class II human MHC alleles covering the majority of the population. A peptide in SpA identified as a potential T cell epitope cross reacting with a known epitope in a microbial antigen was eliminated by miniaturization. These results demonstrate that Z15_EAK is a potential platform for generating antibody depots by which the impacts of Fc-based biotherapeutics can be enhanced through spatiotemporal control.

A Bioinformatics Practicum to Develop Student Understanding of Immunological Rejection of Protein Drugs

Objectives. To design and implement a bioinformatics exercise that applies immunological principles to predicting rejection of protein drugs based upon patient genotype. Design. Doctor of pharmacy (PharmD) students used the Immune Epitope Database, a freely available bioinformatics tool. Over a 2-week laboratory, students interrogated whether a protein drug would be predicted to induce an immune response based upon patient genotype. Results were presented at the last laboratory session, and students completed reports discussing their findings. Assessment. Pre-lab quizzes and a final report were graded. Students answered questionnaires assessing perceived learning gains. To determine the impact on student understanding of immunity against protein drugs, the quality of student data analysis and comparisons to class data were graded. Independent measures of student learning demonstrated that students developed a greater understanding of how patient genotype could contribute to treatment failure with protein drugs. Conclusions. This study indicates that questions related to clinical immunology can be posed using bioinformatics tools.