A010: Model-Based Assessment of the Contribution of Fc Proteins & Serum Proteins to the Elevated Clearance of Monoclonal Antibodies

Statement of Purpose, Innovation or Hypothesis: Shorter overall survival from immune checkpoint inhibitor (ICI) therapies is associated with high drug catabolic clearance (CL), but not drug exposure, indicating high CL is a biomarker for, but not a cause of the poor response¹. Underlying mechanisms linking patient factors, high catabolic CL, and poor outcomes may identify biomarkers to predict patient responses and reveal novel strategies to overcome resistance to ICI therapy. The role of the neonatal Fc receptor (FcRn) in recycling of immunoglobulin G (IgG) monoclonal antibodies (mAbs) is well established, and Fc gamma receptors (FcγRs) have also been suggested by recent data to contribute to the CL of monomeric IgGs^2,3. This study explored the combined roles of FcRn and FcγRs in IgG mAb PK through a novel, mechanistic physiologically based pharmacokinetic (PBPK) model, which integrates endogenous IgG and albumin as surrogates of general catabolic rate and was utilized to discover how disease state may impact relationships between CL, FcRn, FcγRs, endogenous IgG, and albumin in murine cancer models and in cancer patients receiving ICI therapies.

Description of Methods & Materials: We extended and modified a previously described mAb PBPK model⁴, using mAb PK data from cancer cachexia murine models and integrating FcRn and FcγRs, endogenous IgG, and albumin, which has not been done previously. The model was used to explore potential mechanisms and test hypotheses for linking elevated CL to disease-dependent modulation of FcRn, FcγRs, and endogenous IgG. Plasma samples from cancer patients receiving pembrolizumab or nivolumab were used to determine drug CL, endogenous IgG, and albumin. Expression of FcRn and FcγRs was measured by mass cytometry in peripheral blood mononuclear cells.

Data & Results: One major feature of the PBPK model is the incorporation of both FcRn and FcγRs, allowing for direct binding competition between mAb drug and endogenous IgG. The developed model appropriately described the observed plasma concentrations of human IgG mAbs with various binding affinities to FcRn and FcγRs and the measured plasma level of baseline endogenous IgG and albumin in mice. The observation from clinical samples revealed unexpected associations between Fc receptors, serum proteins, and the CL of ICIs.

Interpretation, Conclusion or Significance: The developed model revealed improved fitting of observed PK data across multiple IgG mAbs and with measured endogenous IgG and albumin, compared to currently published models. This model was used to predict the CL of IgG mAbs through dynamic simulation of key factors associated with the clearance pathways, which enhanced our abilities to explore the complex mechanisms linking ICI CL, Fc proteins, and serum proteins and to test our hypotheses utilizing clinical samples and data. This model serves as a translational tool with which we can test mechanistic hypotheses and translate our findings between laboratory models and patients with cancer who are receiving ICI therapies.

Citations/References: 1. Turner, D. C. et al. Pembrolizumab Exposure–Response Assessments Challenged by Association of Cancer Cachexia and Catabolic Clearance. Clin Cancer Res 24, 5841–5849 (2018). 2. Oldham, R. J. et al. FcγRII (CD32) modulates antibody clearance in NOD SCID mice leading to impaired antibody-mediated tumor cell deletion. J Immunother Cancer 8, e000619 (2020). 3. Jin, H. et al. Avelumab internalization by human circulating immune cells is mediated by both Fc gamma receptor and PD-L1 binding. OncoImmunology 10, 1958590 (2021). 4. Shah, D. & Betts, A. Towards a platform PBPK model to characterize the plasma and tissue disposition of monoclonal antibodies in preclinical species and human. Journal of pharmacokinetics and pharmacodynamics 39, 67–86 (2012).

Additional Information/Authors: Acknowledgements: Study was supported by The Ohio State University Comprehensive Cancer Center (OSUCCC) and the National Cancer Institute, grants P30CA016058, R01CA273924 and R01CA201382.