JOURNAL ARTICLE

Abstract 6274: Establishment of a quantitative systems pharmacology platform for syngeneic tumor mouse models: Application in immuno-oncology drug development

Takeshi NakayamaAya KikuchiKota ToshimotoHiroyuki SayamaTaisuke NakazawaMasayo Oishi

Year: 2025 Journal:   Cancer Research Vol: 85 (8_Supplement_1)Pages: 6274-6274   Publisher: American Association for Cancer Research

Abstract

Abstract [Introduction] Quantitative Systems Pharmacology (QSP) modeling is a promising technique for model-informed drug discovery and development, and various QSP models for immuno-oncology (IO) have been published. Syngeneic tumor mice are often used for in vivo pharmacology study, and many kinds of IO QSP models have been reported to understand in vivo data and make prediction. However, published QSP models have varying structures across tumor types that makes it difficult to analyze data across different syngeneic tumor models. In addition, there are few QSP models calibrated by actual data of tumor infiltrating lymphocyte (TIL) dynamics. In this study, we present platform IO QSP modeling for syngeneic tumor mice (MC38, B16F10, CT26, 4T1 and LLC1) with a unified structure based on observed data of TIL dynamics and antitumor efficacy of anti-programmed cell death-1 (anti-PD-1) treatment. [Methods] (Mouse study for TIL dynamics) Five mouse tumors were inoculated into C57BL/6 (MC38, B16F10, LLC1) or BALB/c (CT26, 4T1). Tumors were sampled at three time points (mean tumor volume was about 50 mm3, 300-700 mm3 and 500-2000 mm3) and immune cells in the tumors were analyzed by flow cytometry. (IO QSP platform development) The structure of IO QSP platform was based on a published QSP model for CT26-bearing mice [1] and modified with reference to a comprehensive IO QSP model for breast cancer in human [2] to improve physiological interpretability of model components. The TIL dynamics data and published anti-mouse PD-1 (anti-mPD-1) antibody efficacy data for syngeneic tumor mice [3] were used for model calibration. The platform model was validated by confirming predictability of combination therapy of anti-mPD-1 antibody with a multiple kinase inhibitor (lenvatinib) for syngeneic tumor mice. [Results] The IO QSP platform model contains 12 tumor-specific parameters for each tumor type of syngeneic mice and successfully captured the observed TIL dynamics and antitumor effect of anti-mPD-1 antibody treatment. Mechanism of action of lenvatinib was incorporated into the IO QSP platform and calibrated with published data. The final model was successfully validated by comparing simulation and observation of combination therapy of anti-mPD-1 antibody with lenvatinib. [Conclusions] The IO QSP platform was established for several types of syngeneic tumor mice, which captured TIL dynamics and antitumor efficacy of anti-mPD-1 antibody. This platform model enables us to test a hypothesis by incorporating candidate compounds, to support study design with a translational biomarker and to investigate combination strategies, thus having the potential to facilitate new drug development. [References] [1] Kosinsky Y, et al. J Immunother Cancer. 2018;6(1):17. [2] Wang H, et al. Front Bioeng Biotechnol. 2020;8:141. [3] Georgiev P, et al. Mol Cancer Ther. 2022;21(3):427-439. Citation Format: Takeshi Nakayama, Aya Kikuchi, Kota Toshimoto, Hiroyuki Sayama, Taisuke Nakazawa, Masayo Oishi. Establishment of a quantitative systems pharmacology platform for syngeneic tumor mouse models: Application in immuno-oncology drug development [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6274.

Keywords:
Medicine Drug development Drug Pharmacology Systems pharmacology Cancer research

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Topics

Cancer, Hypoxia, and Metabolism
Life Sciences →  Biochemistry, Genetics and Molecular Biology →  Cancer Research
Cancer Research and Treatments
Life Sciences →  Biochemistry, Genetics and Molecular Biology →  Biotechnology
Computational Drug Discovery Methods
Physical Sciences →  Computer Science →  Computational Theory and Mathematics
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