Jiyeon Jeon 1, Sung Young Lee 3, Soyoung Lee 1, Jung-woo CHAE 1,2, Hwi-yeol Yun 1,2
1 College of Pharmacy, Chungnam National University (Deajeon, South Korea), 2 Department of Bio-AI Convergence, Chungnam National University (Deajeon, South Korea), 3 ImmuneOncia Therapeutics Inc. (Seoul, South Korea)
Objectives
IMC-001 is a novel PD-L1 antibody that is a fully human anti-PD-L1 IgG1 monoclonal antibody which has shown promising results in a dose-escalation first-in-human study.
PD-L1 is a clinically validated immune-checkpoint target which is over-expressed on tumor cells and tumor-infiltrating immune cells in multiple tumor types. PD-L1 inhibits the natural anti-tumor immune response by preventing recruitment of new T cells to the tumor by preventing priming and activation of new T cells in the lymph nodes. PD-L1 also deactivates cytotoxic T cells in the tumor microenvironment. Therefore, antibodies blocking PD-L1 can reactivate T-cell activity and proliferation and lead to anti-tumor immunity.
The first objective of this study was to evaluate the quantitative exposure–response relationship of IMC-001 by developing a mechanistic population pharmacokinetic model incorporating target-mediated drug disposition (TMDD) and FcRn recycling for IMC-001 using phase 1 and phase 2 studies. The second objective was to find an optimal dose regimen in case of changing from Q2W weight-based dosing to Q3W fixed dosing, which was tested using a simulation study.
Methods
Data from phase 1 dose-escalation study(2mg – 20mg per kilogram) and phase 2 study was used to develop model to characterize the pharmacokinetic properties of IMC-001. An individual exposure metrics were derived and linked to binary clinical response(complete response and partial response was considered as responder group) using a logistic exposure–response model. The pharmacokinetic model was assessed using objective function value, goodness-of-fit diagnostics, and predictive checks. The pharmacodynamic model was assessed using objective function value. Simulation of doses from 2 mg to 20 mg Q2W was performed to see direct exposure-response rate. Also, Q3W fixed-dose regimens was tested to see if objective response rate, ORR of 45% can be achieved as original weight-based dose Q2W study aimed. Each dose included 10 participants, and simulations were performed 100 times.
Modeling and simulation were performed using NONMEM (version 7.5) assisted by PsN (version 5.2.6) and R (version 4.2.1).
Results
There were 38 participants throughout the study, and a total of 432 concentrations were observed. Omega was added to the volume of distribution and elimination parameters, and albumin was chosen as a covariate on elimination during the stepwise covariate model searching process. The final model adequately described concentration–time profiles across studies, with mechanistic components capturing FcRn recycling and target binding. Most structural parameters were estimated with acceptable precision. The central volume of distribution (3.49 L) and the volume of distribution of the IMC-001–FcRn complex (0.0594 L) were estimated from the model. The free drug elimination rate constant and the FcRn-mediated recycling rate constant were estimated to be 0.00636 h⁻¹ and 13.9 h⁻¹, respectively. Some target-related parameters such as Kd between PD-L1 and IMC-001, Kd between FcRn and IMC-001, total FcRn concentration, and complex elimination(elimination rate of IMC001 and PD-L1 complex) were fixed for model’s stability.
Simulated exposure metrics demonstrated an increase in response probability with increasing average concentration under Q2W dosing. A dose-regimen change simulation for Q3W fixed dosing showed ORR greater than 45% at doses between 1500 and 1800 mg Q3W.
Conclusions
A mechanistic TMDD model incorporating FcRn-mediated recycling successfully characterized IMC-001 pharmacokinetics and enabled quantitative evaluation of exposure–response relationships. Also, simulation could provide evidence for dose-regimen change from Q2W weight-based dosing to Q3W fixed dosing. This framework provides a basis for model-informed dose optimization in antibody development.
Lee S, CHAE J, and Yun H were contributed equally as correspondents.
References:
Tang, Q., Chen, Y., Li, X., Long, S., Shi, Y., Yu, Y., Wu, W., Han, L., & Wang, S. (2022). The role of PD-1/PD-L1 and application of immune-checkpoint inhibitors in human cancers. Frontiers in immunology, 13, 964442. https://doi.org/10.3389/fimmu.2022.964442
Keam, B., Ock, C. Y., Kim, T. M., Oh, D. Y., Kang, W. K., Park, Y. H., Lee, J., Lee, J. H., Ahn, Y. H., Kim, H. J., Chang, S. K., Park, J., Choi, J. Y., Song, Y. J., & Park, Y. S. (2021). A phase I study of IMC-001, a PD-L1 blocker, in patients with metastatic or locally advanced solid tumors. Investigational new drugs, 39(6), 1624–1632. https://doi.org/10.1007/s10637-021-01078-6
Reference: PAGE 34 (2026) Abstr 12235 [www.page-meeting.org/?abstract=12235]
Poster: Drug/Disease Modelling - Oncology