A spatial measure-valued model for radiation-induced DNA damage kinetics and repair under protracted irradiation condition.

In the present work, we develop a general spatial stochastic model to describe the formation and repair of radiation-induced DNA damage. The model is described mathematically as a measure-valued particle-based stochastic system and extends in several directions the model developed in Cordoni et al. (Phys Rev E 103:012412, 2021; Int J Radiat Biol 1-16, 2022a; Radiat Res 197:218-232, 2022b). In this new spatial formulation, radiation-induced DNA damage in the cell nucleus can undergo different pathways to either repair or lead to cell inactivation. The main novelty of the work is to rigorously define a spatial model that considers the pairwise interaction of lesions and continuous protracted irradiation. The former is relevant from a biological point of view as clustered lesions are less likely to be repaired, leading to cell inactivation. The latter instead describes the effects of a continuous radiation field on biological tissue. We prove the existence and uniqueness of a solution to the above stochastic systems, characterizing its probabilistic properties. We further couple the model describing the biological system to a set of reaction-diffusion equations with random discontinuity that model the chemical environment. At last, we study the large system limit of the process. The developed model can be applied to different contexts, with radiotherapy and space radioprotection being the most relevant. Further, the biochemical system derived can play a crucial role in understanding an extremely promising novel radiotherapy treatment modality, named in the community FLASH radiotherapy, whose mechanism is today largely unknown.

Integrating microdosimetricin vitroRBE models for particle therapy into TOPAS MC using the MicrOdosimetry-based modeliNg for RBE ASsessment (MONAS) tool.

Objective.In this paper, we present MONAS (MicrOdosimetry-based modelliNg for relative biological effectiveness (RBE) ASsessment) toolkit. MONAS is a TOPAS Monte Carlo extension, that combines simulations of microdosimetric distributions with radiobiological microdosimetry-based models for predicting cell survival curves and dose-dependent RBE.Approach.MONAS expands TOPAS microdosimetric extension, by including novel specific energy scorers to calculate the single- and multi-event specific energy microdosimetric distributions at different micrometer scales. These spectra are used as physical input to three different formulations of themicrodosimetric kinetic model, and to thegeneralized stochastic microdosimetric model(GSM2), to predict dose-dependent cell survival fraction and RBE. MONAS predictions are then validated against experimental microdosimetric spectra andin vitrosurvival fraction data. To show the MONAS features, we present two different applications of the code: (i) the depth-RBE curve calculation from a passively scattered proton SOBP and monoenergetic12C-ion beam by using experimentally validated spectra as physical input, and (ii) the calculation of the 3D RBE distribution on a real head and neck patient geometry treated with protons.Main results.MONAS can estimate dose-dependent RBE and cell survival curves from experimentally validated microdosimetric spectra with four clinically relevant radiobiological models. From the radiobiological characterization of a proton SOBP and12C fields, we observe the well-known trend of increasing RBE values at the distal edge of the radiation field. The 3D RBE map calculated confirmed the trend observed in the analysis of the SOBP, with the highest RBE values found in the distal edge of the target.Significance.MONAS extension offers a comprehensive microdosimetry-based framework for assessing the biological effects of particle radiation in both research and clinical environments, pushing closer the experimental physics-based description to the biological damage assessment, contributing to bridging the gap between a microdosimetric description of the radiation field and its application in proton therapy treatment with variable RBE.

On the Emergence of the Deviation from a Poisson Law in Stochastic Mathematical Models for Radiation-Induced DNA Damage: A System Size Expansion.

In this paper, we study the system size expansion of a stochastic model for radiation-induced DNA damage kinetics and repair. In particular, we characterize both the macroscopic deterministic limit and the fluctuation around it. We further show that such fluctuations are Gaussian-distributed. In deriving such results, we provide further insights into the relationship between stochastic and deterministic mathematical models for radiation-induced DNA damage repair. Specifically, we demonstrate how the governing deterministic equations commonly employed in the field arise naturally within the stochastic framework as a macroscopic limit. Additionally, by examining the fluctuations around this macroscopic limit, we uncover deviations from a Poissonian behavior driven by interactions and clustering among DNA damages. Although such behaviors have been empirically observed, our derived results represent the first rigorous derivation that incorporates these deviations from a Poissonian distribution within a mathematical model, eliminating the need for specific ad hoc corrections.

An artificial intelligence-based model for cell killing prediction: development, validation and explainability analysis of the ANAKIN model.

The present work develops ANAKIN: anArtificial iNtelligence bAsed model for (radiation-induced) cell KIlliNg prediction. ANAKIN is trained and tested over 513 cell survival experiments with different types of radiation contained in the publicly available PIDE database. We show how ANAKIN accurately predicts several relevant biological endpoints over a wide broad range on ion beams and for a high number of cell-lines. We compare the prediction of ANAKIN to the only two radiobiological models forRelative Biological Effectivenessprediction used in clinics, that is theMicrodosimetric Kinetic Modeland theLocal Effect Model(LEM version III), showing how ANAKIN has higher accuracy over the all considered cell survival fractions. At last, via modern techniques ofExplainable Artificial Intelligence(XAI), we show how ANAKIN predictions can be understood and explained, highlighting how ANAKIN is in fact able to reproduce relevant well-known biological patterns, such as the overkilling effect.

Multiple levels of stochasticity accounted for in different radiation biophysical models: from physics to biology.

PURPOSE: In the present paper we investigate how some stochastic effects are included in a class of radiobiological models with particular emphasis on how such randomnesses reflect into the predicted cell survival curve. MATERIALS AND METHODS: We consider four different models, namely the Generalized Stochastic Microdosimetric Model GSM2, in its original full form, the Dirac GSM2 the Poisson GSM2 and the Repair-Misrepair Model (RMR). While GSM2 and the RMR models are known in literature, the Dirac and the Poisson GSM2  have been newly introduced in this work. We further numerically investigate via Monte Carlo simulation of four different particle beams, how the proposed stochastic approximations reflect into the predicted survival curves. To achieve these results, we consider different ion species at energies of interest for therapeutic applications, also including a mixed field scenario. RESULTS: We show how the Dirac GSM2, the Poisson GSM2 and the RMR can be obtained from the GSM2 under suitable approximations on the stochasticity considered. We analytically derive the cell survival curve predicted by the four models, characterizing rigorously the high and low dose limits. We further study how the theoretical findings emerge also using Monte Carlo numerical simulations. CONCLUSIONS: We show how different models include different levels of stochasticity in the description of cellular response to radiation. This translates into different cell survival predictions depending on the radiation quality.

An exploratory study of machine learning techniques applied to therapeutic energies particle tracking in microdosimetry using the novel hybrid detector for microdosimetry (HDM).

In this work we present an advanced random forest-based machine learning (ML) model, trained and tested on Geant4 simulations. The developed ML model is designed to improve the performance of the hybrid detector for microdosimetry (HDM), a novel hybrid detector recently introduced to augment the microdosimetric information with the track length of particles traversing the microdosimeter. The present work leads to the following improvements of HDM: (i) the detection efficiency is increased up to 100%, filling not detected particles due to scattering within the tracker or non-active regions, (ii) the track reconstruction algorithm precision. Thanks to the ML models, we were able to reconstruct the microdosimetric spectra of both protons and carbon ions at therapeutic energies, predicting the real track length for every particle detected by the microdosimeter. The ML model results have been extensively studied, focusing on non-accurate predictions of the real track lengths. Such analysis has been used to identify HDM limitations and to understand possible future improvements of both the detector and the ML models.

Cell Survival Computation via the Generalized Stochastic Microdosimetric Model (GSM2); Part I: The Theoretical Framework.

The current article presents the first application of the Generalized Stochastic Microdosimetric Model (GSM2) for computing explicitly a cell survival curve. GSM2 is a general probabilistic model that predicts the kinetic evolution of DNA damages taking full advantage of a microdosimetric description of a radiation energy deposition. We show that, despite the high generality and flexibility of GSM2, an explicit form for the survival fraction curve predicted by the GSM2 is achievable. We illustrate how several correction terms typically added a posteriori in existing radiobiological models to improve the prediction accuracy, are naturally included into GSM2. Among the most relevant features of the survival curve derived from GSM2 and presented in this article, is the linear-quadratic behavior at low doses and a purely linear trend for high doses. The study also identifies and discusses the connections between GSM2 and existing cell survival models, such as the Microdosimetric Kinetic Model (MKM) and the Multi-hit model. Several approximations to predict cell survival in different irradiation regimes are also introduced to include intercellular non-Poissonian behaviors.

Study of relationship between dose, LET and the risk of brain necrosis after proton therapy for skull base tumors.

PURPOSE: We investigated the relationship between RBE-weighted dose (DRBE) calculated with constant (cRBE) and variable RBE (vRBE), dose-averaged linear energy transfer (LETd) and the risk of radiographic changes in skull base patients treated with protons. METHODS: Clinical treatment plans of 45 patients were recalculated with Monte Carlo tool FRED. Radiographic changes (i.e. edema and/or necrosis) were identified by MRI. Dosimetric parameters for cRBE and vRBE were computed. Biological margin extension and voxel-based analysis were employed looking for association of DRBE(vRBE) and LETd with brain edema and/or necrosis. RESULTS: When using vRBE, Dmax in the brain was above the highest dose limits for 38% of patients, while such limit was never exceeded assuming cRBE. Similar values of Dmax were observed in necrotic regions, brain and temporal lobes. Most of the brain necrosis was in proximity to the PTV. The voxel-based analysis did not show evidence of an association with high LETd values. CONCLUSIONS: When looking at standard dosimetric parameters, the higher dose associated with vRBE seems to be responsible for an enhanced risk of radiographic changes. However, as revealed by a voxel-based analysis, the large inter-patient variability hinders the identification of a clear effect for high LETd.

Alignment of Qx100/Qx200 Droplet Digital (Bio-Rad) and QuantStudio 3D (Thermofisher) Digital PCR for Quantification of BCR-ABL1 in Ph+ Chronic Myeloid Leukemia.

In recent years, the digital polymerase chain reaction has received increasing interest as it has emerged as a tool to provide more sensitive and accurate detection of minimal residual disease. In order to start the process of data alignment, we assessed the consistency of the BCR-ABL1 quantification results of the analysis of 16 RNA samples at different levels of disease. The results were obtained by two different laboratories that relied on The Qx100/Qx200 Droplet Digital PCR System (Bio-Rad) and Quant Studio 3D dPCR System (Thermofisher) platforms. We assessed the compatibility between the estimated values by linear regression, Bland-Altman bias-plot, and Mann-Whitney nonparametric test. The results confirmed the compatibility of the measures, allowing us tocompute an 'alignment factor' (AF), equal to 1.41, which was further validated by a different series of experiments. We conclude that the performed measurements by the two laboratories are comparable, and also equalized through the introduction of an alignment factor.

Daptomycin Plasma and CSF Levels in Patients with Healthcare-Associated Meningitis.

BACKGROUND: There are currently few data concerning the cerebrospinal fluid (CSF) penetration of daptomycin in patients with healthcare-associated meningitis. This study aims (1) to better characterize the pharmacokinetics of daptomycin in humans during a 7-day intravenous (IV) therapy course, and (2) to study the penetration of daptomycin in the CSF after IV infusion at the dose of 10 mg/kg. RESULTS: In this prospective observational study, we enrolled nine patients with an implanted external ventricular drainage and a diagnosis of a healthcare-associated meningitis. Daptomycin was administered at 10 mg/kg for a maximum of 7 days. The pharmacokinetic of daptomycin was studied using a two-compartment population/pharmacokinetic (POP/PK) model and by means of a nonlinear mixed effects modeling approach. A large inter-individual variability in plasma area under the curve (Range: 574.7-1366.3 h mg/L), paralleled by high-peak plasma concentration (Cmax) (all values > 60 mg/L), was noted. The inter-individual variability of CSF-AUC although significant (range: 1.17-6.81 h mg/L) was narrower than previously reported and with a late occurrence of CSF-Cmax (range: 6.04-9.54 h). The terminal half-life between plasma and CSF was similar. tmax values in CSF did not show a high inter-individual variability, and the fluctuations of predicted CSF concentrations were minimal. The mean value for daptomycin penetration obtained from our model was 0.45%. CONCLUSIONS: Our POP/PK model was able to describe the pharmacokinetics of daptomycin in both plasma and CSF, showing that daptomycin (up to 7 days at 10 mg/kg) has minimal penetration into central nervous system. Furthermore, the observed variability of AUC, tmax and predicted concentration in CSF was lower than what previously reported in the literature. Based on the present findings, it is unlikely that daptomycin could reach CSF concentrations high enough to have clinical efficacy; this should be tested in future studies.