Highly aggregated antibody therapeutics can enhance the in vitro innate and late-stage T-cell immune responses.

Aggregation of biotherapeutics has the potential to induce an immunogenic response. Here, we show that aggregated therapeutic antibodies, previously generated and determined to contain a variety of attributes (Joubert, M. K., Luo, Q., Nashed-Samuel, Y., Wypych, J., and Narhi, L. O. (2011) J. Biol. Chem. 286, 25118-25133), can enhance the in vitro innate immune response of a population of naive human peripheral blood mononuclear cells. This response depended on the aggregate type, inherent immunogenicity of the monomer, and donor responsiveness, and required a high number of particles, well above that detected in marketed drug products, at least in this in vitro system. We propose a cytokine signature as a potential biomarker of the in vitro peripheral blood mononuclear cell response to aggregates. The cytokines include IL-1ß, IL-6, IL-10, MCP-1, MIP-1α, MIP-1ß, MMP-2, and TNF-α. IL-6 and IL-10 might have an immunosuppressive effect on the long term immune response. Aggregates made by stirring induced the highest response compared with aggregates made by other methods. Particle size in the 2-10 µm range and the retention of some folded structure were associated with an increased response. The mechanism of aggregate activation at the innate phase was found to occur through specific cell surface receptors (the toll-like receptors TLR-2 and TLR-4, FcγRs, and the complement system). The innate signal was shown to progress to an adaptive T-cell response characterized by T-cell proliferation and secretion of T-cell cytokines. Investigating the ability of aggregates to induce cytokine signatures as biomarkers of immune responses is essential for determining their risk of immunogenicity.

Utility of a Bayesian Mathematical Model to Predict the Impact of Immunogenicity on Pharmacokinetics of Therapeutic Proteins.

The impact of an anti-drug antibody (ADA) response on pharmacokinetic (PK) of a therapeutic protein (TP) requires an in-depth understanding of both PK parameters and ADA characteristics. The ADA and PK bioanalytical assays have technical limitations due to high circulating levels of TP and ADA, respectively, hence, significantly hindering the interpretation of this assessment. The goal of this study was to develop a population-based modeling and simulation approach that can identify a more relevant PK parameter associated with ADA-mediated clearance. The concentration-time data from a single dose PK study using five monoclonal antibodies were modeled using a non-compartmental analysis (NCA), one-compartmental, and two-compartmental Michaelis-Menten kinetic model (MMK). A novel PK parameter termed change in clearance time of the TP (α) derived from the MMK model could predict variations in α much earlier than the time points when ADA could be bioanalytically detectable. The model could also identify subjects that might have been potentially identified as false negative due to interference of TP with ADA detection. While NCA and one-compartment models can estimate loss of exposures, and changes in clearance, the two-compartment model provides this additional ability to predict that loss of exposure by means of α. Modeling data from this study showed that the two-compartment model along with the conventional modeling approaches can help predict the impact of ADA response in the absence of relevant ADA data.