Decoding Radiation-Induced Fibrosis: a computational approach to understanding the damage pathway

Computational modeling of radiation-induced lung fibrosis via the agent-based paradigm provides valuable insights into the initiation and progression of toxicity.
Like

Radiation-Induced Lung Fibrosis (RILF) is a complication observed in up to 30% of patients undergoing thoracic irradiation. While there's recognition of a link between treatment and patient-specific characteristics and the severity of the outcomes, the underlying mechanisms remain largely unclear. Clinicians currently rely on phenomenological models based on previous experiences to implement treatment plans for patients. However, a deeper understanding of the involved pathways is crucial for mitigating toxicity risks.

Despite this need, in-vitro and in-vivo animal models of RILF fall short in capturing the complexity observed in human response mechanisms. Additionally, the timeline for observing symptoms and radiographic changes spans months, if not years, significantly impeding research progress.

A potential remedy for the mentioned limitations arises from computational models and virtual experiments. When appropriately implemented, these tools empower scientists to focus on key mechanisms, incorporate patient-specific features, and promptly observe the impact of varied parameter values on outcomes.

Figure 1. 3D Agent-Based Model of a human alveolar duct. The structure is made up of 3 stacked layers, and each layer consists of 6 tangent alveoli. The centres of the alveoli are located on equidistant circles with a radius equal to the duct radius. Cells are represented as coloured spheres, with green = AEC2 cells, blue = AEC1 cells, brown = M1 macrophages, orange = M2 macrophages, light blue = fibroblasts, red = myofibroblasts

In our study, we developed a computational model focusing on a small section of the human lungs, namely, an alveolar segment (see Figure 1). Alveolar segments, located in the distal airways, comprise hollow sphere-like substructures called alveoli, facilitating gas-blood exchange. Implementing the Agent-Based (AB) paradigm, our model features entities (cells or agents) acting autonomously according to defined rules and interacting with neighbors and the environment. Unlike equation-based models, AB models (ABMs) effectively represent spatial and cellular heterogeneities, mirroring complex biological system behaviours.

Figure 2. Alveolar segment model built with a Monte-Carlo particle transport simulator and irradiated with 10 keV gamma particles.

To simulate the irradiation, our ABM integrated with a Monte Carlo (MC) simulator, replicating stochastic trajectories and interactions with matter of numerous particles (see Figure 2). Using the MC simulator, we computed the dose (i.e. the energy per unit mass) delivered to alveolar segment cells and simulated radiation-induced damage onset and development, focusing on RILF. Introducing the RILF Severity Index (RSI) as a biomarker, we gauged fibrosis severity by considering lung density increase and healthy epithelial cell decline. Our coupled model facilitated comparative analyses of different particles and treatment schedules' effects on RSI at various time steps.

Remarkably, our model aligned with prior experimental results, quantitatively and qualitatively mimicking existing severity indices in mice studies and density increase trends observed in human patients with RILF. Additionally, our model replicated the sparing effect observed in multi-fractionation schemes (i.e. treating multiple times with smaller doses) compared to single-fractionation, and highlighted lower survival rates with proton irradiation, attributed to protons releasing more damaging energy per unit length compared to photons. Furthermore, we characterised our model through a targeted sensitivity analysis, assessing how specific parameter variations impacted predicted outcomes. This analysis revealed that the model is highly sensitive to the parameters involved in the bystander damage spread mechanism, while showing little sensitivity to cell radiosensitivity or the rate of damaged cell senescence.

In conclusion, our work offers a novel framework for micro-scale radiation-induced damage simulation in normal tissues and sets the stage for exploring patient-specific parameter effects on treatment outcomes in the pursuit of precision medicine.

Please sign in or register for FREE

If you are a registered user on Research Communities by Springer Nature, please sign in

Subscribe to the Topic

Radiation Oncology
Life Sciences > Biological Sciences > Cancer Biology > Cancer Therapy > Radiation Oncology
Prognostic Markers
Life Sciences > Health Sciences > Biomedical Research > Biomarkers > Prognostic Markers
Computational Science and Engineering
Mathematics and Computing > Mathematics > Computational Mathematics and Numerical Analysis > Computational Science and Engineering

Related Collections

With collections, you can get published faster and increase your visibility.

Cancer therapy toxicity

This Collection welcomes research focused on minimizing and mitigating cancer therapy-related toxicity, or identifying new insights into the epidemiology and pathophysiology of acute and late toxicities.

Publishing Model: Open Access

Deadline: Mar 30, 2024