(T-061) Impact of PD-L2 on Relative Efficacy of Anti-PD-1 and Anti-PD-L1 Antibodies: Insights From QSP-Based Meta-Analysis

Sarah Head, PhD – Principal Scientist - Biology, Certara; Deborah Flusberg, PhD – Principal Scientist - Biology, Certara; Saheli Sarkar, PhD – Senior Principal Scientist - Biology, Certara; David Flowers, PhD – Associate Fellow - Modeling, Certara; Andrew Matteson, PhD – Director of Product, Certara; Diana Marcantonio, PhD – Senior Director - Biology, Certara; John Burke, PhD – VP, Global Head of ABM Scientific Affairs, Certara; Joshua Apgar, PhD – VP, Global Head of ABM Scientific Affairs, Certara; Georgi Kapatinov, PhD – Director - Modeling, Certara

Objectives: Checkpoint inhibitors that target PD-1 or PD-L1 have had a profound effect in a variety of cancers, both as a single therapy and in combinations [1]. There is a shared hypothesis for the primary mechanism of action of these drugs: inhibition of the PD-1:PD-L1 pathway through binding to either target. Clinical meta-analyses have suggested that anti-PD-1 antibodies yield better survival outcomes [2, 5], but previous QSP modeling work has shown that approved anti-PD-1 and anti-PD-L1 antibodies all have similarly high levels of inhibition of the PD-1:PD-L1 pathway at their respective clinical doses [6]. Therefore, there may be a mechanism missing from the previous QSP model which could explain the efficacy difference between the two drug classes. Recent research has suggested that blocking PD-1:PD-L2 interactions may also be an integral part of anti-PD-1 treatment efficacy [3, 4]. Here, we extend our previous QSP model-based analysis to test if PD-1:PD-L2 interactions are sufficient to explain the efficacy difference between the two drug classes observed in clinical meta-analyses. We also expand the analysis to include a virtual patient population to assess the effect of variability in patient expression of the different targets on the model outcome.

Methods: A mechanistic PK/PD model with literature-informed PD-1, PD-L1, and PD-L2 target burden and turnover was constructed. For each mAb of interest, dosing regimen, pharmacokinetics, and binding affinity were taken from the literature, and target engagement, PD-1:PD-L1 complex inhibition, and PD-1:PD-L2 complex inhibition in a solid tumor were simulated. Furthermore, virtual patients were created based on each drug’s PopPK variability and baseline distributions of PD-1, PD-L1, and PD-L2 expressions to assess variability in the model predictions. Based on the relative efficacy of the two drug classes and the model-predicted inhibition, the relative contribution of each pathway inhibition to efficacy was estimated.

Results: The model shows that high levels of target engagement and PD-1:PD-L1 complex inhibition are achieved by both approved mAbs, yet anti-PD-1 antibodies have greater clinical efficacy. Based on our virtual population analysis, patient variability alone cannot explain the difference in clinical outcomes. Only the anti-PD-1 antibodies show high inhibition of the PD-1:PD-L2 complexes in our simulations, suggesting that this may contribute to anti-PD-1's greater efficacy.

Conclusions: The model suggests that while there is little difference in inhibition of PD-1:PD-L1 interactions between the drug classes, targeting PD-1 leads to inhibition of PD-1:PD-L2 interactions. Empirical analysis of the relative levels of signaling through these two pathways needed for tumor cells to evade antitumor immune responses supports the hypothesis that inhibiting the PD-1:PD-L2 may contribute to the efficacy differences seen in clinical meta-analyses.

Citations: [1] Alsaab et al., (2017). PD-1 and PD-L1 Checkpoint Signaling Inhibition for Cancer Immunotherapy: Mechanism, Combinations, and Clinical Outcome. Frontiers in pharmacology, 8, 561.

[2] Duan et al., (2020). Use of Immunotherapy With Programmed Cell Death 1 vs Programmed Cell Death Ligand 1 Inhibitors in Patients With Cancer: A Systematic Review and Meta-analysis. JAMA oncology, 6(3), 375–384.

[3] Yang et al., (2024). Programmed cell death-ligand 2: new insights in cancer. Frontiers in immunology, 15, 1359532. https://doi.org/10.3389/fimmu.2024.1359532

[4] Yearley et al., (2017). PD-L2 Expression in Human Tumors: Relevance to Anti-PD-1 Therapy in Cancer. Clinical cancer research: an official journal of the American Association for Cancer Research, 23(12), 3158–3167. https://doi.org/10.1158/1078-0432.CCR-16-1761

[5] Zhao et al., (2021). Comparisons of Underlying Mechanisms, Clinical Efficacy and Safety Between Anti-PD-1 and Anti-PD-L1 Immunotherapy: The State-of-the-Art Review and Future Perspectives. Frontiers in pharmacology, 12, 714483.

[6] Johnson et al., (2023). Anti-PD-1 vs. Anti-PD-L1 Antibodies - Insights From QSP-Based Meta-Analysis. ACoP14 [Poster Presentation]. ACoP14, National Harbor, MD.