Nicolò Cogno

The emergence of virtual human twins (VHT) in biomedical research has sparked interest in multiscale in silico modelling frameworks, particularly in their application bridging cellular to tissue levels. Among the diverse array of multiscale modelling tools, off-lattice center-based agent-based models (CBM) offer a promising approach due to their depiction of cells in 3D space, closely resembling biological reality. Despite the proliferation of CBM tools addressing various biomedical challenges, a comprehensive and systematic comparison among them has been elusive. This paper presents a community-driven benchmark initiative aimed at evaluating and comparing CBM for biomedical applications, akin to successful efforts in other scientific domains such as the Critical Assessment of Protein Structure Prediction (CASP). Enlisting developers from leading tools like BioDynaMo, Chaste, PhysiCell,TiSim, and CompuTiX, we devised a benchmark scope, defined metrics, and established reference datasets to ensure a meaningful and equitable evaluation. Unit tests targeting different solvers within these tools were designed, ranging from diffusion and mechanics to cell cycle simulations and growth scenarios. Results from these tests demonstrate varying tool implementations in handling diffusion, mechanics, and cell cycle equations, emphasising the need for standardised benchmarks and interoperability. Discussions among the community underscore the necessity for defining gold standards, fostering interoperability, and drawing lessons from analogous benchmarking experiences. The outcomes, disseminated through a public platform in collaboration with OpenEBench, aim to catalyse advancements in computational biology, offering a comprehensive resource for tool evaluation and guiding future developments in cell-level simulations. This initiative endeavours to strengthen and expand the computational biology simulation community through continued dissemination and performance-oriented benchmarking efforts to enable the use of VHT in biomedicine.

Objective. To develop a computational framework coupling multiscale vascular models of the human liver for improved radiation dosimetry calculations that clearly distinguish the absorbed dose to tissue parenchyma and that to its blood content at all spatial scales. This framework thus addresses limitations of homogeneous blood/tissue organ models in present use in radiopharmaceutical therapy. Approach. High-fidelity tetrahedral mesh models of liver vasculature were constructed at two spatial scales. At the macroscale, detailed hepatic arterial, venous, and portal venous networks were generated within reference adult male and female computational phantoms. At the microscale, a classical hexagonal liver lobule model incorporating sinusoids, bile compartments, and cellular components was developed. A mathematical framework was further developed to couple Monte Carlo radiation transport results across these spatial scales, enabling comprehensive dosimetric calculations for radiation dose to both blood and parenchymal tissues. Main Results. The coupled model system successfully accounted for the entire blood content of the liver, with approximately 31% represented in macroscale vessels (⩾100 μm diameter) and 69% within microscale structures. Specific absorbed fractions were computed for monoenergetic photons, electrons, and alpha particles, demonstrating reciprocity between blood-to-parenchyma and parenchyma-to-blood crossfire. Reference S-values were computed for 22 therapeutic and 11 diagnostic radionuclides, providing the first comprehensive dataset for blood-specific and parenchyma-specific internal dosimetry calculations in the liver. Significance. This work establishes a novel framework for multi-scale radiation transport calculations in vascularized organs, enabling separate tracking of blood and parenchymal tissue doses. The methodology has immediate applications in improving dose calculations for radiopharmaceutical therapies, Y-90 microsphere radioembolization treatment, and analysis of blood dose during external beam radiotherapy. The approach can be readily adapted for other vascularized organs, representing a significant advancement in radiation dosimetry accuracy for both therapeutic and diagnostic applications by fully and independently accounting for organ activity localized within two tissue compartments—organ blood and organ parenchyma.

Computational models are not just appealing because they can simulate and predict the development of biological phenomena across multiple spatial and temporal scales, but also because they can integrate information from well-established in vitro and in vivo models and test new hypotheses in cancer biomedicine. Agent-based models and simulations are especially interesting candidates among computational modeling procedures in cancer research due to the capability to, for instance, recapitulate the dynamics of neoplasia and tumor - host interactions. Yet, the absence of methods to validate the consistency of the results across scales can hinder adoption by turning fine-tuned models into black boxes. This review compiles relevant literature that explores strategies to leverage high-fidelity simulations of multi-scale, or multi-level, cancer models with a focus on verification approached as simulation calibration. We consolidate our review with an outline of modern approaches for agent-based models' validation and provide an ambitious outlook toward rigorous and reliable calibration.

Background: Mechanistic modelling of normal tissue toxicities is unfolding as an alternative to the phenomenological normal tissue complication probability models. The latter, currently used in the clinics, rely exclusively on limited patient data and neglect spatial dose distribution information. Among the various approaches, agent-based models are appealing as they provide the means to include patient-specific parameters and simulate long-term effects in complex systems. However, Monte Carlo tools remain the state-of-the-art for modelling radiation transport and provide measurements of the delivered dose with unmatched precision. Methods: In this work, we develop and characterize a coupled 3D agent-based – Monte Carlo model that mechanistically simulates the onset of the radiation-induced lung fibrosis in an alveolar segment. To the best of our knowledge, this is the first such model. Results: Our model replicates extracellular matrix patterns, radiation-induced lung fibrosis severity indexes and functional subunits survivals that show qualitative agreement with experimental studies and are consistent with our past results. Moreover, in accordance with experimental results, higher functional subunits survival and lower radiation-induced lung fibrosis severity indexes are achieved when a 5-fractions treatment is simulated. Finally, the model shows increased sensitivity to more uniform protons dose distributions with respect to more heterogeneous ones from photon irradiation. Conclusions: This study lays thus the groundwork for further investigating the effects of different radiotherapeutic treatments on the onset of radiation-induced lung fibrosis via mechanistic modelling.

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.

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).