Early- and late-phase radiation-induced lung injuries, namely pneumonitis and lung fibrosis (RILF), severely constrain the maximum dose and irradiated volume in thoracic radiotherapy. As the most radiosensitive targets, epithelial cells respond to radiation either by undergoing apoptosis or switching to a senescent phenotype that triggers the immune system and damages surrounding healthy cells. Unresolved inflammation stimulates mesenchymal cells’ proliferation and extracellular matrix (ECM) secretion, which irreversibly stiffens the alveolar walls and leads to respiratory failure. Although a thorough understanding is lacking, RILF and idiopathic pulmonary fibrosis share multiple pathways and would mutually benefit from further insights into disease progression. Furthermore, current normal tissue complication probability (NTCP) models rely on clinical experience to set tolerance doses for organs at risk and leave aside mechanistic interpretations of the undergoing processes. To these aims, we implemented a 3D agent-based model (ABM) of an alveolar duct that simulates cell dynamics and substance diffusion following radiation injury. Emphasis was placed on cell repopulation, senescent clearance, and intra/inter-alveolar bystander senescence while tracking ECM deposition. Our ABM successfully replicates early and late fibrotic response patterns reported in the literature along with the ECM sigmoidal dose-response curve. Moreover, surrogate measures of RILF severity via a custom indicator show qualitative agreement with published fibrosis indices. Finally, our ABM provides a fully mechanistic alveolar survival curve highlighting the need to include bystander damage in lung NTCP models.

Understanding the pathophysiology of lung fibrosis is of paramount importance to elaborate targeted and effective therapies. As it onsets, the randomly accumulating extracellular matrix (ECM) breaks the symmetry of the branching lung structure. Interestingly, similar pathways have been reported for both idiopathic pulmonary fibrosis and radiation-induced lung fibrosis (RILF). Individuals suffering from the disease, the worldwide incidence of which is growing, have poor prognosis and a short mean survival time. In this context, mathematical and computational models have the potential to shed light on key underlying pathological mechanisms, shorten the time needed for clinical trials, parallelize hypotheses testing, and improve personalized drug development. Agent-based modeling (ABM) has proven to be a reliable and versatile simulation tool, whose features make it a good candidate for recapitulating emergent behaviors in heterogeneous systems, such as those found at multiple scales in the human body. In this paper, we detail the implementation of a 3D agent-based model of lung fibrosis using a novel simulation platform, namely, BioDynaMo, and prove that it can qualitatively and quantitatively reproduce published results. Furthermore, we provide additional insights on late-fibrosis patterns through ECM density distribution histograms. The model recapitulates key intercellular mechanisms, while cell numbers and types are embodied by alveolar segments that act as agents and are spatially arranged by a custom algorithm. Finally, our model may hold potential for future applications in the context of lung disorders, ranging from RILF (by implementing radiation-induced cell damage mechanisms) to COVID-19 and inflammatory diseases (such as asthma or chronic obstructive pulmonary disease).

Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are a valuable tool for in vitro drug testing and disease studies. As contractility has become one of the main experimental outputs, hiPSC-CMs in silico models should also feature the mechanisms of force generation. Thus, we integrated two contractile elements (CE), Rice2008 and Negroni2015, into Paci2020 hiPSC-CM model. The simulated force-Ca 2+ relationships from skinned versions of the CEs revealed rather close pCa50 values for both CEs: 6.17 and 6.10, respectively for Rice2008 and Negroni2015. However, Hill's coefficients for the two curves were 7.30 and 3.6. The relationships agreed with in vitro data from human engineered heart tissues. Most of the biomarkers measured from simulated spontaneous action potentials (APs) and Ca 2+ transients (CaTs) showed good agreement with in vitro data for both CEs. The active peak force observed in paced conditions (1 Hz) and at extracellular Ca 2+ concentration ([Ca 2+ ]o) of 1.8 mM was 0.011 mN/mm 2 for Paci2020+Rice2008 and 0.57 mN/mm 2 for Paci2020+Negroni2015. These values match, qualitatively with the 0.26 mN/mm 2 peak force reported previously in vitro at [Ca 2+ ]o=1.8 mM. Our results set an opening to develop more sophisticated hiPSC-CM models featuring both electrophysiology and biomechanics.

Mechanistic modelling of normal tissue toxicities is unfolding as an alternative to the phenomenological Normal Tissue Complication Probability models currently used in the clinics that rely exclusively on limited patient data. Among the various approaches, Agent-Based Models (ABMs) are appealing as they provide the means to include patient-specific parameters and simulate long-term effects in complex systems. However, Monte Carlo (MC) tools remain the state-of-the-art for modelling radiation transport and provide measurements of the delivered dose with unmatched precision. In this work, we delineate the implementation of and characterize the first coupled 3D ABM-MC model that mechanistically simulates the onset of the radiation-induced lung fibrosis (RILF) in an alveolar segment. Our model replicates extracellular matrix patterns, RILF severity indexes (RSI) and functional-subunits (FSU) survivals that show qualitative agreement with experimental studies and are consistent with our past results. Moreover, in accordance with experimental results, higher FSUs survival and lower RSI were achieved when a 5-fractions treatment was simulated. Finally, the model showed increased sensitivity to peaked protons dose distributions with respect to flatter ones from photons irradiation. Our work lays thus the groundwork for further investigating the effects of different radiotherapeutic treatments on the onset of RILF via mechanistic modelling.