(T-068) An integrated model of catabolic clearance mechanisms driving exposure-response confounding for immunotherapies in cancer: interactions between mAb drugs, Fc receptors, and endogenous serum proteins

Bryan Remaily, MS – Graduate Research Fellow, The Ohio State University; Yizhen Guo, PharmD, MS – Research fellow, The Ohio State University; Trang Vu, PhD – Graduate Research Fellow, The Ohio State University; Justin Thomas, MS – Graduate Research Fellow, The Ohio State University; Zhiliang Xie, MD – Senior Research Scientist, The Ohio State Unversity; Adeoluwa Adeluola, MS – Graduate Research Fellow, The Ohio State University; Gregory Young, MS – Graduate Research Fellow, The Ohio State University; Samuel Kulp, DVM, PhD – Senior Research Scientist, The Ohio State University; Latha Ganesan, PhD – Research Assistant Professor, The Ohio State University; Dwight Owen, MD, MS – Physician, The Ohio State University; Xiaokui Mo, PhD – Associate Professor, The Ohio State University; Thomas Mace, PhD – Assistant Professor, The Ohio State University; Christopher Coss, PhD – Associate Professor, The Ohio State University; Mitch Phelps, PhD – Professor, The Ohio State University

Objectives: Higher catabolic clearance (CL) of immune checkpoint inhibitors (ICIs) is a biomarker for less favorable outcomes, independent of drug exposure, in patients with cancer, thus leading to confounded exposure-response relationships. Patients with rapid mAb CL present with cancer cachexia phenotypes[1,2], though underlying mechanisms linking high mAb CL, poor drug response, and cancer cachexia are poorly understood. Our previous research demonstrates elevated CL of ICIs in patients can be replicated in mouse models of cancer cachexia, and the increased CL is partially, but not wholly, attributed to neonatal Fc receptor (FcRn) and Fc gamma receptors (FcγRs)[3]. Endogenous IgG can bind to FcRn or FcγRs, and they are associated with mAb PK [4,5]. This study aimed to develop a mechanistic PBPK model of mAb PK to integrate FcRn, FcγRs, and endogenous IgG, predict their combined roles in IgG mAb PK in mice, and compare model-predictions to observations from patients with cancer.

Methods: A PBPK model was developed by incorporating FcγR-mediated internalization of mAb and endogenous IgG from an initial model adapted from literature[6]. Physiological parameters were obtained from literature, and ratio of FcγR and FcRn expression between healthy and cachectic mice was derived from in vitro observations. Other parameters were identified based on the PK data of human IgG1 (hIgG1), without and with engineering to disrupt binding to FcγR and FcRn, and observed level of serum endogenous IgG and albumin. Model simulation was conducted to explore impact on hIgG1 CL from altered expression of FcRn, FcγR, and circulating endogenous IgG. Clinical samples were from patients with non-small cell lung cancer or renal cell cancer who received ICIs.

Results: The model successfully captured the PK of hIgG1s in healthy mice and cachectic mice. Our measurements of FcRn expression suggest slight decrease in mRNA but slight increase in protein levels in splenocytes and liver sinusoidal endothelial cells. When FcRn expression was assumed to be unchanged or changed minimally in the cachectic mice, simultaneous increases in the production rate of endogenous IgG and FcγR expression, similar to observations, in the cachectic mice were required to describe the CL elevation of wild-type hIgG1 compared to healthy mice. This also explained the smaller extent of CL elevation in the antibodies having disrupted binding to FcγR or FcRn. The model captured the measured baseline level of endogenous IgG. In clinical samples, there was a positive association between baseline endogenous IgG and CL of nivolumab, and this was consistent with the model-prediction.

Conclusions: The model was able to capture the changes in the CL of IgG antibodies in cancer cachexia. Further development incorporating changes in immune cell populations will be investigated. The developed model will greatly enhance our ability to explore the complex mechanisms linking ICI CL and outcomes from ICI therapy.

Citations: [1] Guo, Y. et al. Clinical Cancer Research 30 (2024): 942-958.
[2] Turner, David C., et al. Clinical Cancer Research 24.23 (2018): 5841-5849.
[3] Vu, Trang T., et al. Pharmacological research 199 (2024): 107048.
[4] Oldham, Robert J., et al. Journal for Immunotherapy of Cancer 8.1 (2020).
[5] Abe, Kazuki, et al. European Journal of Clinical Pharmacology 78.1 (2022): 77-87. [6] Shah, Dhaval K., and Alison M. Betts. Journal of pharmacokinetics and pharmacodynamics 39 (2012): 67-86.