Ahmed Ibrahim Abdelaziz Atta1, Farnoush Farahpour1
1Universität Duisburg-Essen
Introduction: Radiation therapy (RT) is a cornerstone in the treatment of cancers such as lung, pancreatic, and breast cancer. However, ionizing radiation can damage normal tissues, leading to adverse effects such as radiation-induced lung injury (RILI), particularly in the thoracic region. RILI is manifested by pneumonia and pulmonary fibrosis. To better understand RILI, as a complementary approach to experimental and clinical studies, mechanistic mathematical modeling approaches, such as Quantitative Systems Pharmacology (QSP) modeling, can be employed. QSP can integrate prior knowledge of immune system interactions, cytokines, and lung tissue response to radiation to identify new biomarkers and optimize therapy plans. Objectives: By developing a detailed QSP model of the complex immune responses after radiation, we aim to create a mechanistic, dynamic model that elucidates how radiation dose, cell death rates, and immune activation thresholds contribute to lung toxicity. A key objective is to identify biomarkers—such as specific cytokine levels or immune cell counts—that can predict which patients are at high risk for radiation-induced lung injury (RILI). Methods: Our model extends a published QSP model of the lung immune ecosystem by integrating new mechanisms and refining mechanistic interactions. Specifically, we have incorporated a linear-quadratic model within the radiation module to estimate alveolar cell death based on radiation dose, cell type, and repair capacity. The final model comprises 40 species and over 200 parameters. Consequently, performing sensitivity analysis is essential to identify key parameters and guide model parameterization using Sobol analysis. This project integrates preclinical data, including flow cytometry and scRNA-seq data from a preclinical study on mice exposed to varying doses of radiation therapy (RT). Results: Our QSP model helps to investigate the key processes behind radiation-induced lung injury (RILI) and highlights the interaction between the innate and adaptive immune systems, along with cytokines. After radiation exposure, innate immune cells—such as neutrophils, pro-inflammatory M1 macrophages, and plasmacytoid dendritic cells (pDCs)—become active within 24–48 hours, causing an acute inflammatory reaction. In contrast, adaptive immune responses involving Th1, Th17, and cytotoxic T lymphocytes (CTLs) took longer to activate. Sensitivity analysis over time (monitored 84 days after radiation) identified key factors influencing RILI progression. In the innate immune response, the death rate of radiation-damaged alveolar epithelial (AT) cells has a sustained effect throughout the observation period, although this effect gradually attenuates over time. Neutrophil activation is a strong early factor (Days 4–10) but its impact decreases significantly in later stages (Days 21-84). Among cytokines, GM-CSF shows a more significant sensitivity relationship, contributing to the innate response by activating neutrophils in the early stages and M1 macrophages in the later stages. This activation also influences the adaptive response. Within the adaptive immune response, the role of dendritic cell activation and alveolar cell death are critical in the early phase. Additionally, while the impact of Th1 activation decreases in the course of simulation,Th17 cell activation , directly or mediated by TGF-ß, becomes increasingly important over time, highlighting its role in the development of chronic fibrosis. Conclusion: Our QSP model provides valuable insights into the dynamic interactions between immune responses and radiation-induced lung injury (RILI). It helps to test hypotheses on the impact of the immune microenvironment on the adverse effect of radiation. By identifying key biomarkers and understanding the temporal progression of immune activation, this model can aid in the development of targeted therapeutic strategies. Its predictive capability could ultimately assist clinicians in tailoring radiation therapy (RT) plans to minimize adverse effects while ensuring effective tumor targeting.
1.Yan, Yujie, et al. “Exploration of radiation-induced lung injury, from mechanism to treatment: a narrative review.” Translational lung cancer research 11.2 (2022): 307. 2.Dai, Wei, et al. “A prototype QSP model of the immune response to SARS-CoV-2 for community development.” CPT: pharmacometrics & systems pharmacology 10.1 (2021): 18-29. 3.Chaput, Genevieve, and Laura Regnier. “Radiotherapy: Clinical pearls for primary care.” Canadian Family Physician 67.10 (2021): 753.
Reference: PAGE 33 (2025) Abstr 11325 [www.page-meeting.org/?abstract=11325]
Poster: Drug/Disease Modelling - Oncology