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A mechanistic quantitative systems pharmacology model platform for translational efficacy evaluation and checkpoint combination design of bispecific immuno-modulatory antibodies

Xu, Yiyang, Yang, Siyuan, Rao, Qi, Gao, Yuan, Zhou, Guanyue, Zhao, Dongmei, Shi, Xinsheng, Chai, Yi, Zhao, Chen·2025·Frontiers in Pharmacology
DOI 10.3389/fphar.2025.1571844PMC PMC12018249
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Abstract

Over the past 2 decades, tumor immunotherapies have witnessed remarkable advancements, especially with the emergence of immune checkpoint-targeting bispecific antibodies. However, a quantitative understanding of the dynamic cross-talking mechanisms underlying different immune checkpoints as well as the optimal dosing and target design of checkpoint-targeting bispecific antibodies still remain challenging to researchers. To address this challenge, we have here developed a multi-scale quantitative systems pharmacology (QSP) model platform that integrates a diverse array of immune checkpoints and their interactive functions. The model has been calibrated and validated against an extensive collection of multiscale experimental datasets covering 20+ different monoclonal and bispecific antibody treatments at over 60 administered dose levels. Based on high-throughput simulations, the QSP model platform comprehensively screened and characterized the potential efficacy of different bispecific antibody target combination designs, and model-based preclinical population-level simulations revealed target-specific dose-response relationships as well as alternative dosing strategies that can maintain anti-tumor treatment efficacy while reducing dosing frequencies. Model simulations also pointed out that combining checkpoint-targeting bispecific antibodies with monoclonal antibodies can lead to significantly enhanced anti-tumor efficacy. Our mechanistic QSP model can serve as an integrated precision medicine simulation platform to guide the translational research and clinical development of checkpoint-targeting immuno-modulatory bispecific antibodies.

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FIGURE 1 Diagram describing the mechanistic model structure including tumor cells, T cells and APCs. (A) The model primarily considers the interaction between T cells, APCs, and tumor cells within the tumor microenvironment, as well as the interaction between a number of immune checkpoints that can modulate T cell activation and anti-tumor cytotoxicity. Mono-specific (mAbs) and bispecific antibody (BsAbs) therapeutics can bind their corresponding immune checkpoints and regulate these processes to boost T cell response and tumor shrinkage. (B) We designed the model based on a pathway that starts from cell signaling pathways, proceeds to in vitro cell co-culture, and ultimately extends to in vivo studies in mice. The mouse model incorporates multiple modules, enabling it to meticulously elucidate the pharmacodynamics of diverse antibodies within mice.
FIGURE 1 Diagram describing the mechanistic model structure including tumor cells, T cells and APCs. (A) The model primarily considers the interaction between T cells, APCs, and tumor cells within the tumor microenvironment, as well as the interaction between a number of immune checkpoints that can modulate T cell activation and anti-tumor cytotoxicity. Mono-specific (mAbs) and bispecific antibody (BsAbs) therapeutics can bind their corresponding immune checkpoints and regulate these processes to boost T cell response and tumor shrinkage. (B) We designed the model based on a pathway that starts from cell signaling pathways, proceeds to in vitro cell co-culture, and ultimately extends to in vivo studies in mice. The mouse model incorporates multiple modules, enabling it to meticulously elucidate the pharmacodynamics of diverse antibodies within mice.CC BY · PubMed Central
FIGURE 2 Model-based quantitative characterization of T cell intracellular signaling. Upon T cell activation, ZAP70 was rapidly phosphorylated, as shown by model simulation and experimental data in scenarios of (A) anti-CD3 treatment (data from and (Kästle et al., 2020; Tewari et al., 2021)), (B) anti-CD3 and anti-CD28 treatment (data from (Thumkeo et al., 2020) and (Cattley et al., 2020)), and (C) T-B cell co-culture (data from (Hui et al., 2017)). (D) Downstream activation of AKT by phosphorylation under anti-CD3 and anti-CD28 stimulation (data from (Narayan et al., 2006), (Zheng et al., 2019) and (Fu et al., 2021)). (E) Downstream activation of ERK by phosphorylation under anti-CD3 treatment (data from (Kassem et al., 2016) and (Lee et al., 2013)). (F) Downstream activation of pERK under anti-CD3 and anti-CD28 stimulation (data from (Kästle et al., 2020) and (Sheppard et al., 2004)). (G) In T cells, anti-CD3 and anti-CD28 stimulation activates NF-κB by phosphorylation (data from (Coudronniere et al., 2000) and (Clavijo and Frauwirth, 2012)). (H) Increased IFN-γ secretion by T cells after anti-CD3 stimulation (data from (Coto-Llerena et al., 2021)). (I) Increased IFN-γ secretion by T cells after anti-CD3 and anti-CD28 stimulation (data from (Wang et al., 2007)). (A–G) Y-axes are relative expression levels (normalized to their respective maximum values). S, simulation; D, experimental data.
FIGURE 2 Model-based quantitative characterization of T cell intracellular signaling. Upon T cell activation, ZAP70 was rapidly phosphorylated, as shown by model simulation and experimental data in scenarios of (A) anti-CD3 treatment (data from and (Kästle et al., 2020; Tewari et al., 2021)), (B) anti-CD3 and anti-CD28 treatment (data from (Thumkeo et al., 2020) and (Cattley et al., 2020)), and (C) T-B cell co-culture (data from (Hui et al., 2017)). (D) Downstream activation of AKT by phosphorylation under anti-CD3 and anti-CD28 stimulation (data from (Narayan et al., 2006), (Zheng et al., 2019) and (Fu et al., 2021)). (E) Downstream activation of ERK by phosphorylation under anti-CD3 treatment (data from (Kassem et al., 2016) and (Lee et al., 2013)). (F) Downstream activation of pERK under anti-CD3 and anti-CD28 stimulation (data from (Kästle et al., 2020) and (Sheppard et al., 2004)). (G) In T cells, anti-CD3 and anti-CD28 stimulation activates NF-κB by phosphorylation (data from (Coudronniere et al., 2000) and (Clavijo and Frauwirth, 2012)). (H) Increased IFN-γ secretion by T cells after anti-CD3 stimulation (data from (Coto-Llerena et al., 2021)). (I) Increased IFN-γ secretion by T cells after anti-CD3 and anti-CD28 stimulation (data from (Wang et al., 2007)). (A–G) Y-axes are relative expression levels (normalized to their respective maximum values). S, simulation; D, experimental data.CC BY · PubMed Central
FIGURE 3 Stepwise model calibration using in vitro and in vivo data on antibody-induced tumor growth inhibition. The QSP model can quantitatively capture (A) the time-dependent proliferation of T cells (data from (Teschner et al., 2011)) and (B) the growth of tumor cells over time (data from (Khazen et al., 2019)). The integrated in vitro QSP model captures the dose response relationship of (C) anti-tumor cytotoxicity of anti-TIGIT antibody (data from (Zhong et al., 2022)), (D) anti-tumor cytotoxicity of anti-PD-1 antibody (data from (Wang et al., 2021)), (E) anti-tumor cytotoxicity of anti-TIGIT/PD-L1 bispecific antibody (data from (Yang et al., 2023)), as well as increase in (F) IFN-γ release after anti-4-1BB/PD-L1 bispecific antibody treatment (data from (Muik et al., 2022)) and (G) IL-2 release after anti-LAG3/PD-L1 bispecific antibody treatment (data from (Jiang et al., 2021)). (H) Plasma pharmacokinetics of anti-TIGIT/PD-L1 bispecific antibody in mice (data from (Mu et al., 2022)). (I-P) In vivo antitumor activity of different antibody treatment regimens targeting immune checkpoints (and administered at different doses) as characterized by the QSP model; examples shown here include (I) anti-LAG3/PD-L1 bispecific antibody (data from (Kraman et al., 2020)), (J) anti-4-1BB/PD-L1 bispecific antibody (data from Jeong et al., 2021), (K) anti-TIGIT/PD-L1 bispecific antibody (data form (Xiao et al., 2021)), (L) anti-CTLA4/OX40 bispecific antibody (data form (Kvarnhammar et al., 2019)), (M) anti-CTLA4 antibody (data from (Gan et al., 2022)), (N) anti-OX40 and anti-PD-1 antibodies (data from (Kuang et al., 2020)), (O) anti-LAG3 and anti-PD-1 antibodies (data from (Yu et al., 2019)), and (P) anti-4-1BB and anti-PD-L1 antibodies (data from (Cheng et al., 2022)). (Q) An overview table summarizing the complete in vivo experimental datasets used for model calibration. S, simulation; S*, second simulation after calibration; D, experimental data.
FIGURE 3 Stepwise model calibration using in vitro and in vivo data on antibody-induced tumor growth inhibition. The QSP model can quantitatively capture (A) the time-dependent proliferation of T cells (data from (Teschner et al., 2011)) and (B) the growth of tumor cells over time (data from (Khazen et al., 2019)). The integrated in vitro QSP model captures the dose response relationship of (C) anti-tumor cytotoxicity of anti-TIGIT antibody (data from (Zhong et al., 2022)), (D) anti-tumor cytotoxicity of anti-PD-1 antibody (data from (Wang et al., 2021)), (E) anti-tumor cytotoxicity of anti-TIGIT/PD-L1 bispecific antibody (data from (Yang et al., 2023)), as well as increase in (F) IFN-γ release after anti-4-1BB/PD-L1 bispecific antibody treatment (data from (Muik et al., 2022)) and (G) IL-2 release after anti-LAG3/PD-L1 bispecific antibody treatment (data from (Jiang et al., 2021)). (H) Plasma pharmacokinetics of anti-TIGIT/PD-L1 bispecific antibody in mice (data from (Mu et al., 2022)). (I-P) In vivo antitumor activity of different antibody treatment regimens targeting immune checkpoints (and administered at different doses) as characterized by the QSP model; examples shown here include (I) anti-LAG3/PD-L1 bispecific antibody (data from (Kraman et al., 2020)), (J) anti-4-1BB/PD-L1 bispecific antibody (data from Jeong et al., 2021), (K) anti-TIGIT/PD-L1 bispecific antibody (data form (Xiao et al., 2021)), (L) anti-CTLA4/OX40 bispecific antibody (data form (Kvarnhammar et al., 2019)), (M) anti-CTLA4 antibody (data from (Gan et al., 2022)), (N) anti-OX40 and anti-PD-1 antibodies (data from (Kuang et al., 2020)), (O) anti-LAG3 and anti-PD-1 antibodies (data from (Yu et al., 2019)), and (P) anti-4-1BB and anti-PD-L1 antibodies (data from (Cheng et al., 2022)). (Q) An overview table summarizing the complete in vivo experimental datasets used for model calibration. S, simulation; S*, second simulation after calibration; D, experimental data.CC BY · PubMed Central
FIGURE 4 Model Validation using in vivo data on antibody-induced tumor growth inhibition. In vivo antitumor activity of different antibody treatment regimens targeting immune checkpoints (and administered at different doses) as predicted by the QSP model; examples shown here include (A) anti-4-1BB/PD-L1 bispecific antibody (data from (Jeong et al., 2021)), (B) anti-LAG3 and anti- PD-1 antibodies (data from (Yu et al., 2019)), (C) anti-TIGIT and anti- PD-1 antibodies (data from (Shao et al., 2021)), (D) anti-4-1BB/PD-1 bispecific antibody (data from (Qiao et al., 2021)), (E) anti-4-1BB and anti-PD-L1 antibodies (data from (Cheng et al., 2022)), (F–G) anti-LAG3/PD-L1 bispecific antibody (data from (Kraman et al., 2020)), (F) anti-LAG3 and anti-PD-L1 antibodies, (H) anti-TIGIT/PD-L1 bispecific antibody (data from (Zhong et al., 2022)), (I) anti-CTLA4 antibody (data from (Du et al., 2018)), (J) anti-OX40 and anti- PD-1 antibodies (data from (Kuang et al., 2020)). (K) A table summarizing the complete in vivo experimental datasets used for model validation. S, simulation; D, experimental data.
FIGURE 4 Model Validation using in vivo data on antibody-induced tumor growth inhibition. In vivo antitumor activity of different antibody treatment regimens targeting immune checkpoints (and administered at different doses) as predicted by the QSP model; examples shown here include (A) anti-4-1BB/PD-L1 bispecific antibody (data from (Jeong et al., 2021)), (B) anti-LAG3 and anti- PD-1 antibodies (data from (Yu et al., 2019)), (C) anti-TIGIT and anti- PD-1 antibodies (data from (Shao et al., 2021)), (D) anti-4-1BB/PD-1 bispecific antibody (data from (Qiao et al., 2021)), (E) anti-4-1BB and anti-PD-L1 antibodies (data from (Cheng et al., 2022)), (F–G) anti-LAG3/PD-L1 bispecific antibody (data from (Kraman et al., 2020)), (F) anti-LAG3 and anti-PD-L1 antibodies, (H) anti-TIGIT/PD-L1 bispecific antibody (data from (Zhong et al., 2022)), (I) anti-CTLA4 antibody (data from (Du et al., 2018)), (J) anti-OX40 and anti- PD-1 antibodies (data from (Kuang et al., 2020)). (K) A table summarizing the complete in vivo experimental datasets used for model validation. S, simulation; D, experimental data.CC BY · PubMed Central
FIGURE 5 Sensitivity analysis and simulation of anti-tumor efficacy of BsAbs with different checkpoint targets. (A) The Sobol indices of the top-ranked parameters predicted have a significant impact on tumor growth. (B) Model-predicted in vivo tumor growth inhibition (TGI) values under different BsAb treatment that targets different combinations of immune checkpoints. Each (X,Y) entry represents a potential BsAb drug that simultaneously targets X and Y checkpoints, with its simulated TGI results under the low dose (1 mg/kg) and high dose (10 mg/kg) conditions shown in the upper diagonal region (in red heatmap) and lower diagonal region (in blue heatmap) respectively.
FIGURE 5 Sensitivity analysis and simulation of anti-tumor efficacy of BsAbs with different checkpoint targets. (A) The Sobol indices of the top-ranked parameters predicted have a significant impact on tumor growth. (B) Model-predicted in vivo tumor growth inhibition (TGI) values under different BsAb treatment that targets different combinations of immune checkpoints. Each (X,Y) entry represents a potential BsAb drug that simultaneously targets X and Y checkpoints, with its simulated TGI results under the low dose (1 mg/kg) and high dose (10 mg/kg) conditions shown in the upper diagonal region (in red heatmap) and lower diagonal region (in blue heatmap) respectively.CC BY · PubMed Central
FIGURE 6 Model-based optimization of preclinical antibody dosing design in immuno-oncology research. (A) Predicted TGI in the virtual mouse population after anti-CTLA4 treatment at doses of 0.1–5 mg/kg, and (B) distribution of TGI response depth (percentages of mice with TGI ≥ 40%, 20%<TGI<40%, and TGI ≤ 20%) at different doses. (C) Predicted TGI in response to anti-CTLA4 treatment at 0.5 mg/kg biw (twice per week) and 1 mg/kg qw (once per week) dosing regimens. (D) Predicted TGI in the virtual mouse population after anti-TIGTI/PD-L1 BsAb treatment at doses of 3–18 mg/kg, and (E) distribution of TGI response depth at different doses. (F) Predicted TGI in response to anti-TIGTI/PD-L1 BsAb treatment at 6 mg/kg biw and 12 mg/kg qw regimens. (G) Predicted TGI in the virtual mouse population after anti-LAG3/PD-L1 BsAb treatment at doses of 1–10 mg/kg, and (H) distribution of TGI response depth at different doses. (I) Predicted TGI in response to anti-LAG3/PD-L1 BsAb treatment at 3 mg/kg biw and 6 mg/kg qw regimens. (J–K) Predicted population-level TGI heatmap for the combination of (J) anti-PD-1 and anit-LAG3, as well as (K) anti-PD-1 and anit-4-1BB, at different doses. ∗P < 0.05, ∗∗P < 0.01, ∗∗∗∗P < 0.0001. Statistical analyses were performed using Wilcoxon rank-sum test.
FIGURE 6 Model-based optimization of preclinical antibody dosing design in immuno-oncology research. (A) Predicted TGI in the virtual mouse population after anti-CTLA4 treatment at doses of 0.1–5 mg/kg, and (B) distribution of TGI response depth (percentages of mice with TGI ≥ 40%, 20%<TGI<40%, and TGI ≤ 20%) at different doses. (C) Predicted TGI in response to anti-CTLA4 treatment at 0.5 mg/kg biw (twice per week) and 1 mg/kg qw (once per week) dosing regimens. (D) Predicted TGI in the virtual mouse population after anti-TIGTI/PD-L1 BsAb treatment at doses of 3–18 mg/kg, and (E) distribution of TGI response depth at different doses. (F) Predicted TGI in response to anti-TIGTI/PD-L1 BsAb treatment at 6 mg/kg biw and 12 mg/kg qw regimens. (G) Predicted TGI in the virtual mouse population after anti-LAG3/PD-L1 BsAb treatment at doses of 1–10 mg/kg, and (H) distribution of TGI response depth at different doses. (I) Predicted TGI in response to anti-LAG3/PD-L1 BsAb treatment at 3 mg/kg biw and 6 mg/kg qw regimens. (J–K) Predicted population-level TGI heatmap for the combination of (J) anti-PD-1 and anit-LAG3, as well as (K) anti-PD-1 and anit-4-1BB, at different doses. ∗P < 0.05, ∗∗P < 0.01, ∗∗∗∗P < 0.0001. Statistical analyses were performed using Wilcoxon rank-sum test.CC BY · PubMed Central
FIGURE 7 Model-based investigation of triple checkpoint combination strategies. (A) Predicted TGI in different virtual mouse populations (with differential checkpoint expression profiles) after mAb plus and BsAb combination treatment targeting three immune checkpoints, and comparisons with (B) predicted TGI in the same virtual mouse populations after only BsAbs treatment targeting two immune checkpoints. CTLA4 (Low), mice with low T cell expression of CTLA4; TIGTI (Low), mice with low T cell expression of TIGIT; PD-1 (Low), mice with low T cell expression of PD-1; LAG3 (Low), mice with low T cell expression of LAG3.
FIGURE 7 Model-based investigation of triple checkpoint combination strategies. (A) Predicted TGI in different virtual mouse populations (with differential checkpoint expression profiles) after mAb plus and BsAb combination treatment targeting three immune checkpoints, and comparisons with (B) predicted TGI in the same virtual mouse populations after only BsAbs treatment targeting two immune checkpoints. CTLA4 (Low), mice with low T cell expression of CTLA4; TIGTI (Low), mice with low T cell expression of TIGIT; PD-1 (Low), mice with low T cell expression of PD-1; LAG3 (Low), mice with low T cell expression of LAG3.CC BY · PubMed Central

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Metadata

Authors
Xu, Y., Yang, S., Rao, Q., Gao, Y., Zhou, G., Zhao, D., Shi, X., Chai, Y., & Zhao, C.
Venue
Frontiers in Pharmacology
Publisher
Frontiers Media SA
Published
2025-04-10
Volume
16
ISSN
1663-9812
DOI
10.3389/fphar.2025.1571844
PMC
PMC12018249
Cited by
3
References
2
Licence
creativecommons.org/licenses/by/4.0/ (vor)

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